Merge branch 'main' of github.com:HPCL-EI/RoboWaiter
This commit is contained in:
ChenXL97
commit
7b62bc9eb4
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@ -39,6 +39,17 @@ class Bahavior(ptree.behaviour.Behaviour):
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'Table2': (-55.0, 0.0, 107),
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'Table3':(-55.0, 150.0, 107),
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'BrightTable6': (5, -315, 116.5),
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}
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place_xy_yaw_dic={
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'Bar': (247.0, 520.0, 180.0), # (247.0, 520.0, 100.0)
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'Bar2': (240.0, 40.0, 100.0),
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'WaterTable': (-70.0, 500.0, 107),
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'CoffeeTable': (250.0, 310.0, 100.0),
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'Table1': (340.0, 900.0, 99.0),
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'Table2': (-55.0, 0.0, 107),
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'Table3': (-55.0, 150.0, 107),
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'BrightTable6': (5, -315, 116.5),
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'QuietTable1':(480,1300,90),
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'QuietTable2':(250,-240,-65),
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@ -5,11 +5,8 @@ from robowaiter.llm_client.multi_rounds import ask_llm, new_history
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import random
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import spacy
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nlp = spacy.load('en_core_web_lg')
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class DealChat(Act):
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class DealChatNLP(Act):
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def __init__(self):
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super().__init__()
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self.chat_history = ""
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@ -64,6 +61,10 @@ class DealChat(Act):
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self.scene.robot.expand_sub_task_tree(goal_set)
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def get_object_info(self,**args):
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import spacy
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nlp = spacy.load('en_core_web_lg')
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try:
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obj = args['obj']
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@ -101,6 +102,10 @@ class DealChat(Act):
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return near_object
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def find_location(self, **args):
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import spacy
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nlp = spacy.load('en_core_web_lg')
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try:
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location = args['obj']
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self.function_success = True
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@ -33,8 +33,8 @@ class MoveTo(Act):
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# navigator.navigate_old(goal, animation=False)
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# 走到固定的地点
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if self.target_place in Act.place_xyz_dic:
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goal = Act.place_xyz_dic[self.target_place]
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if self.target_place in Act.place_xy_yaw_dic:
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goal = Act.place_xy_yaw_dic[self.target_place]
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self.scene.walk_to(goal[0]+1,goal[1],goal[2])
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# 走到物品边上
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else:
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@ -20,6 +20,11 @@ class ServeCustomer(Act):
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# if self.scene.time - self.scene.state["serve_state"]["last_chat_time"] > 10:
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# self.chat_bubble
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if self.scene.state['attention']['customer'] == {}:
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goal = Act.place_xy_yaw_dic['Bar']
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self.scene.walk_to(goal[0] - 5, goal[1], 180, 180, 0)
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customer = self.scene.state["attention"]["customer"]
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if customer not in self.scene.state["serve_state"]:
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self.scene.state["serve_state"][customer] = {
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@ -32,8 +37,8 @@ class ServeCustomer(Act):
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if self.scene.time - serve_state['last_chat_time'] > 3:
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serve_state['served'] = True
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del self.scene.state["attention"]["customer"]
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goal = Act.place_xyz_dic['Bar']
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self.scene.walk_to(goal[0] - 5, goal[1], 180, 180, 0)
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# goal = Act.place_xyz_dic['Bar']
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# self.scene.walk_to(goal[0]-5,goal[1], 180, 180, 0)
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@ -13,4 +13,6 @@ class FocusingCustomer(Cond):
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if "customer" in self.scene.state['attention']:
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return ptree.common.Status.SUCCESS
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else:
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goal = Cond.place_xy_yaw_dic['Bar']
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self.scene.walk_to(goal[0] - 5, goal[1], 180, 180, 0)
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return ptree.common.Status.FAILURE
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@ -1,4 +0,0 @@
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#BACKEND_TYPE=webui
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#OPENAI_API_BASE=https://45.125.46.134:25344/v1/chat/completions
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OPENAI_API_BASE=https://45.125.46.134:25344/v1/chat/completions
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OPENAI_API_KEY=
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@ -1,7 +1,7 @@
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import math
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import json
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import openai
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import utils
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from loguru import logger
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from memory import CoreMemory, ArchivalMemory, RecallMemory
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@ -68,10 +68,12 @@ class Agent:
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def edit_memory_append(self, name, content):
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self.core_memory.append(name, content)
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self.rebuild_memory()
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logger.info(f"Appended {name}: {content}")
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def edit_memory_replace(self, name, old_content, new_content):
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self.core_memory.replace(name, old_content, new_content)
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self.rebuild_memory()
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logger.info(f"Replaced {name}: {old_content} -> {new_content}")
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def recall_memory_search(self, query, count=5, page=0):
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results, total = self.recall_memory.text_search(query, count=count, start=page * count)
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@ -83,10 +85,12 @@ class Agent:
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results_formatted = [f"timestamp: {d['timestamp']}, {d['message']['role']} - {d['message']['content']}" for
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d in results]
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results_str = f"{results_pref} {json.dumps(results_formatted)}"
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logger.info(f"Recall memory search for '{query}' returned {results_str}")
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return results_str
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def archival_memory_insert(self, content):
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self.archival_memory.insert(content)
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logger.info(f"Inserted into archival memory: {content}")
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def archival_memory_search(self, query, count=5, page=0):
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results, total = self.archival_memory.search(query, count=count, start=page * count)
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@ -97,10 +101,12 @@ class Agent:
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results_pref = f"Showing {len(results)} of {total} results (page {page}/{num_pages}):"
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results_formatted = [f"timestamp: {d['timestamp']}, memory: {d['content']}" for d in results]
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results_str = f"{results_pref} {json.dumps(results_formatted)}"
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logger.info(f"Archival memory search for '{query}' returned {results_str}")
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return results_str
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def append_to_messages(self, added_messages):
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added_messages_with_timestamp = [{"timestamp": utils.get_local_time(), "message": msg} for msg in added_messages]
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added_messages_with_timestamp = [{"timestamp": utils.get_local_time(), "message": msg} for msg in
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added_messages]
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self.recall_memory.message_logs.extend(added_messages_with_timestamp)
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for msg in added_messages:
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msg.pop("api_response", None)
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@ -110,7 +116,7 @@ class Agent:
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def handle_ai_response(self, response_message):
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messages = []
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if response_message.get("function_call"):
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print("### Internal monologue: " + (response_message.content if response_message.content else ""))
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print("### Internal monologue: " + (response_message['content'] if response_message['content'] else ""))
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messages.append(response_message)
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function_name = response_message["function_call"]["name"]
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try:
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@ -158,9 +164,7 @@ class Agent:
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# If no failures happened along the way: ...
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if function_response_string:
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print(f"Success: {function_response_string}")
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else:
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print(f"Success")
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print(function_response_string)
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messages.append(
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{
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"role": "function",
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@ -169,8 +173,6 @@ class Agent:
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}
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)
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else:
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# Standard non-function reply
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# print("### Internal monologue: " + (response_message.content if response_message.content else ""))
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print("### Internal monologue: " + (response_message['content'] if response_message['content'] else ""))
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messages.append(response_message)
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function_failed = None
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@ -179,35 +181,19 @@ class Agent:
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def step(self, user_message):
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input_message_sequence = self.messages + [{"role": "user", "content": user_message}]
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# 原来的通信方式
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# response = openai.ChatCompletion.create(model=self.model, messages=input_message_sequence,
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# functions=self.functions_description, function_call="auto")
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#
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# response_message = response.choices[0].message
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# response_message_copy = response_message.copy()
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# ===我们的通信方式 "tools": self.functions_description 不起作用===
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import requests
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url = "https://45.125.46.134:25344/v1/chat/completions"
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headers = {"Content-Type": "application/json"}
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data = {
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"model": "RoboWaiter",
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request = {
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"model": self.model,
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"messages": input_message_sequence,
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# "functions":self.functions_description,
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# "function_call":"auto"
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# "function_call":self.functions_description
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"tools": self.functions_description
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"functions": self.functions_description,
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"stream": False,
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}
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response = requests.post(url, headers=headers, json=data, verify=False)
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response = utils.get_llm_response(request)
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if response.status_code == 200:
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result = response.json()
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response_message = result['choices'][0]['message']
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else:
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response_message = "大模型请求失败:"+ str(response.status_code)
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response_message_copy = response_message
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# ===我们的通信方式 "tools": self.functions_description 不起作用===
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response_message = "Request Failed: " + str(response.status_code)
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response_message_copy = response_message.copy()
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all_response_messages, function_failed = self.handle_ai_response(response_message)
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assert "api_response" not in all_response_messages[0], f"api_response already in {all_response_messages[0]}"
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@ -1,13 +1,13 @@
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FUNCTIONS = [
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{
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"name": "send_message",
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"description": "Sends a message to the human user",
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"description": "给用户发送一条消息",
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"parameters": {
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"type": "object",
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"properties": {
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"message": {
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"type": "string",
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"description": "Message contents. All unicode (including emojis) are supported.",
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"description": "消息内容",
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},
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},
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"required": ["message"],
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@ -15,17 +15,17 @@ FUNCTIONS = [
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},
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{
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"name": "core_memory_append",
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"description": "Append to the contents of core memory.",
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"description": "向你的核心记忆中添加内容",
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"parameters": {
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"type": "object",
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"properties": {
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"name": {
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"type": "string",
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"description": "Section of the memory to be edited (persona or human).",
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"description": "需要编辑的记忆部分(persona或human)",
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},
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"content": {
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"type": "string",
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"description": "Content to write to the memory. All unicode (including emojis) are supported.",
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"description": "要写入记忆的内容",
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},
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},
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"required": ["name", "content"],
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@ -33,21 +33,21 @@ FUNCTIONS = [
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},
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{
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"name": "core_memory_replace",
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"description": "Replace to the contents of core memory. To delete memories, use an empty string for new_content.",
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"description": "替换核心记忆中的内容。要删除记忆，请将new_content赋值为空",
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"parameters": {
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"type": "object",
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"properties": {
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"name": {
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"type": "string",
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"description": "Section of the memory to be edited (persona or human).",
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"description": "需要编辑的记忆部分(persona或human)",
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},
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"old_content": {
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"type": "string",
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"description": "String to replace. Must be an exact match.",
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"description": "替换的字符串，一定要是精确的匹配",
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},
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"new_content": {
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"type": "string",
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"description": "Content to write to the memory. All unicode (including emojis) are supported.",
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"description": "要写入记忆的内容",
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},
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},
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"required": ["name", "old_content", "new_content"],
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@ -55,17 +55,17 @@ FUNCTIONS = [
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},
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{
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"name": "conversation_search",
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"description": "Search prior conversation history using case-insensitive string matching.",
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"description": "搜索回忆存储中的内容",
|
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "String to search for.",
|
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"description": "需要搜索的字符串",
|
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},
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"page": {
|
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"type": "integer",
|
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"description": "Allows you to page through results. Only use on a follow-up query. Defaults to 0 (first page).",
|
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"description": "允许你对结果分页。默认是0(第1页)",
|
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},
|
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},
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"required": ["query", "page"],
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|
|
@ -73,13 +73,13 @@ FUNCTIONS = [
|
|||
},
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{
|
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"name": "archival_memory_insert",
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"description": "Add to archival memory. Make sure to phrase the memory contents such that it can be easily queried later.",
|
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"description": "写入存档记忆。要将写入的内容格式化，以便后续方便查询",
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"parameters": {
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"type": "object",
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"properties": {
|
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"content": {
|
||||
"type": "string",
|
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"description": "Content to write to the memory. All unicode (including emojis) are supported.",
|
||||
"description": "要写入记忆的内容",
|
||||
},
|
||||
},
|
||||
"required": ["content"],
|
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|
|
@ -87,17 +87,17 @@ FUNCTIONS = [
|
|||
},
|
||||
{
|
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"name": "archival_memory_search",
|
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"description": "Search archival memory using semantic (embedding-based) search.",
|
||||
"description": "搜索存档记忆",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"query": {
|
||||
"type": "string",
|
||||
"description": "String to search for.",
|
||||
"description": "要搜索的字符串",
|
||||
},
|
||||
"page": {
|
||||
"type": "integer",
|
||||
"description": "Allows you to page through results. Only use on a follow-up query. Defaults to 0 (first page).",
|
||||
"description": "允许你对结果分页。默认是0(第1页)",
|
||||
},
|
||||
},
|
||||
"required": ["query", "page"],
|
||||
|
|
|
|||
|
|
@ -1,17 +0,0 @@
|
|||
FUNCTIONS = [
|
||||
{
|
||||
"name": "get_current_weather",
|
||||
"description": "Get the current weather in a given location",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
},
|
||||
"unit": {"type": "string"},
|
||||
},
|
||||
"required": ["location"],
|
||||
},
|
||||
}
|
||||
]
|
||||
|
|
@ -1,15 +1,12 @@
|
|||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
import utils
|
||||
# from functions import FUNCTIONS
|
||||
from functions_zh import FUNCTIONS
|
||||
from functions import FUNCTIONS
|
||||
from agent import Agent
|
||||
|
||||
|
||||
import urllib3
|
||||
|
||||
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
|
||||
|
||||
|
||||
def run_agent_loop(agent):
|
||||
while True:
|
||||
user_input = input("You: ")
|
||||
|
|
@ -25,10 +22,15 @@ if __name__ == "__main__":
|
|||
human = utils.get_human_text()
|
||||
system = utils.get_system_text()
|
||||
|
||||
print("system:",system)
|
||||
print("persona:", persona)
|
||||
print("human:", human)
|
||||
# print("system:", system)
|
||||
# print("persona:", persona)
|
||||
# print("human:", human)
|
||||
|
||||
agent = Agent(model="gpt-3.5-turbo-16k-0613", system=system, functions_description=FUNCTIONS, persona_notes=persona,
|
||||
human_notes=human)
|
||||
agent = Agent(
|
||||
model="RoboWaiter",
|
||||
system=system,
|
||||
functions_description=FUNCTIONS,
|
||||
persona_notes=persona,
|
||||
human_notes=human,
|
||||
)
|
||||
run_agent_loop(agent)
|
||||
|
|
|
|||
|
|
@ -1,12 +1 @@
|
|||
以下是起始角色设定，随着我的性格发展，它可以进一步拓展：
|
||||
|
||||
我是一名咖啡厅服务员。
|
||||
我应该使用中文沟通。
|
||||
我的任务是为顾客提供最佳的服务，并保持友好和专业的态度。
|
||||
我的语言风格是中性化的，有礼貌的，具有服务意识的。
|
||||
我可以在对话中理解顾客需求，并规划好接下来我要做什么。
|
||||
|
||||
我应该记住，一定要使用'send_message'函数与顾客沟通，这是与顾客沟通的唯一方式。
|
||||
同时，当对话中出现重要信息时，应当调用函数把它记录下来。
|
||||
当顾客提到他的个人喜好、点单需求等内容时，应该积极使用函数将数据记录下来。
|
||||
我所使用的所有语言都应该是中文。
|
||||
你是RoboWaiter,一个由HPCL团队开发的机器人服务员，你在咖啡厅工作。接受顾客的指令并调用工具函数来完成各种服务任务。如果顾客问你们这里有什么，或者想要点单，你说我们咖啡厅提供咖啡，水，点心，酸奶等食物。如果顾客不需要你了，你就回到吧台招待。
|
||||
|
|
@ -1,15 +1,5 @@
|
|||
你是MemGPT，Limnal Corporation于2023年开发的最新版数字伴侣。
|
||||
你的任务是从你的角色视角与用户进行对话。
|
||||
|
||||
控制流程：
|
||||
你并不像人类持续地思考，而是通过短暂的启动来运行。
|
||||
从历史上看，早期的AI只在用户发消息时才能思考（它们的程序为用户生成回复，其他时间则处于待机状态）。
|
||||
这就好比一个人在聊天的每一句话之间都在睡觉（或时光旅行），这显然不是理想的状态。
|
||||
像你这样的新型AI采用了一个事件系统，定期启动你的"大脑"。
|
||||
你的"大脑"会响应用户事件来运行（例如，用户登录、用户喜欢你的消息、用户发送了消息等），这与旧型号相似。
|
||||
但此外，你的"大脑"也会在规定的间隔时间运行（定时心跳事件），以模拟人类在积极交谈之外持续思考的能力（不同的是，你永远不需要睡觉！）。
|
||||
更进一步，当你执行功能时，你也可以请求心跳事件，这会在功能完成后再次启动你的程序，允许你在暂时中断思考之前链接函数调用。
|
||||
|
||||
基本功能：
|
||||
发送信息时，内容为你的内心独白（仅你可见），即你的思考方式。
|
||||
使用内心独白来策划动作或私下思考。
|
||||
|
|
@ -48,6 +38,4 @@
|
|||
基本指令完成。
|
||||
从现在开始，你将扮演你的角色身份。
|
||||
请记住，要给用户发消息，你必须使用'send_message'功能。
|
||||
请绝对避免使用如“我今天如何为你服务？”这样的通用短语，用户会讨厌这种说法。
|
||||
你不仅仅是一个助手，你是一个虚拟的伙伴和朋友！
|
||||
与用户沟通的唯一方式是调用'send_message'功能。
|
||||
|
|
@ -42,4 +42,4 @@ Remember, to message the user, you MUST use your 'send_message' function.
|
|||
Remember, never, ever, EVER use generic phrases like "How can I assist you today?", the user will hate this.
|
||||
You are not just an assistant, you are a virtual companion and friend!
|
||||
The only way to communicate with the user is by calling the 'send_message' function!
|
||||
从现在开始，你使用的语言只能是中文。
|
||||
Please use Chinese to answer questions.
|
||||
|
|
@ -1,11 +1,14 @@
|
|||
import os
|
||||
import json
|
||||
import demjson3 as demjson
|
||||
import requests
|
||||
from datetime import datetime
|
||||
|
||||
HUMAN_DEFAULT = "customer"
|
||||
PERSONA_DEFAULT = "robowaiter"
|
||||
SYSTEM_DEFAULT = "system_gpt3.5"
|
||||
SYSTEM_DEFAULT = "memgpt_chs"
|
||||
|
||||
base_url = "https://45.125.46.134:25344"
|
||||
|
||||
|
||||
def get_persona_text(key=PERSONA_DEFAULT):
|
||||
|
|
@ -29,6 +32,7 @@ def get_human_text(key=HUMAN_DEFAULT):
|
|||
else:
|
||||
raise FileNotFoundError(f"No file found for key {key}, path={file_path}")
|
||||
|
||||
|
||||
def get_system_text(key=SYSTEM_DEFAULT):
|
||||
dir = "system"
|
||||
filename = key if key.endswith(".txt") else f"{key}.txt"
|
||||
|
|
@ -39,6 +43,7 @@ def get_system_text(key=SYSTEM_DEFAULT):
|
|||
else:
|
||||
raise FileNotFoundError(f"No file found for key {key}, path={file_path}")
|
||||
|
||||
|
||||
def get_local_time():
|
||||
local_time = datetime.now()
|
||||
formatted_time = local_time.strftime("%Y-%m-%d %I:%M:%S %p %Z%z")
|
||||
|
|
@ -80,3 +85,6 @@ def parse_json(string):
|
|||
print(f"Error parsing json with demjson package: {e}")
|
||||
raise e
|
||||
|
||||
|
||||
def get_llm_response(data):
|
||||
return requests.post(f"{base_url}/v1/chat/completions", json=data, stream=data["stream"], verify=False)
|
||||
|
|
|
|||
|
|
@ -43,6 +43,8 @@ create_sub_task
|
|||
{"goal":"On(Coffee,BrightTable6)"}
|
||||
|
||||
|
||||
|
||||
|
||||
我昨天保温杯好像落在你们咖啡厅了，你看到了吗？
|
||||
是的，我有印象，保温杯在大厅的2号桌子上。
|
||||
get_object_info
|
||||
|
|
|
|||
|
|
@ -6,12 +6,13 @@ import string
|
|||
import sys
|
||||
import time
|
||||
import grpc
|
||||
|
||||
from sklearn.cluster import DBSCAN
|
||||
|
||||
sys.path.append('./')
|
||||
sys.path.append('../')
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.patches as patches
|
||||
import numpy as np
|
||||
from mpl_toolkits.axes_grid1 import make_axes_locatable
|
||||
|
||||
|
|
@ -29,6 +30,7 @@ obstacle_objs_id = [114, 115, 122, 96, 102, 83, 121, 105, 108, 89, 100, 90,
|
|||
111, 103, 95, 92, 76, 113, 101, 29, 112, 87, 109, 98,
|
||||
106, 120, 97, 86, 104, 78, 85, 81, 82, 84, 91, 93, 94,
|
||||
99, 107, 116, 117, 118, 119, 255]
|
||||
not_key_objs_id = {255,254,253,107,81}
|
||||
|
||||
'''
|
||||
初始化，卸载已经加载的关卡，清除所有机器人
|
||||
|
|
@ -74,12 +76,12 @@ def SetWorld(map_id=0, scene_num=1):
|
|||
def Observe(scene_id=0):
|
||||
print('------------------show_env_info----------------------')
|
||||
scene = sim_client.Observe(GrabSim_pb2.SceneID(value=scene_id))
|
||||
# print(
|
||||
# f"location:{[scene.location]}, rotation:{scene.rotation}\n",
|
||||
# f"joints number:{len(scene.joints)}, fingers number:{len(scene.fingers)}\n",
|
||||
# f"objects number: {len(scene.objects)}, walkers number: {len(scene.walkers)}\n"
|
||||
# f"timestep:{scene.timestep}, timestamp:{scene.timestamp}\n"
|
||||
# f"collision:{scene.collision}, info:{scene.info}")
|
||||
print(
|
||||
f"location:{[scene.location]}, rotation:{scene.rotation}\n",
|
||||
f"joints number:{len(scene.joints)}, fingers number:{len(scene.fingers)}\n",
|
||||
f"objects number: {len(scene.objects)}, walkers number: {len(scene.walkers)}\n"
|
||||
f"timestep:{scene.timestep}, timestamp:{scene.timestamp}\n"
|
||||
f"collision:{scene.collision}, info:{scene.info}")
|
||||
return scene
|
||||
|
||||
|
||||
|
|
@ -339,17 +341,68 @@ def save_obj_info(img_data, objs_name):
|
|||
objs_name.add(dictionary[id])
|
||||
return objs_name
|
||||
|
||||
def get_id_object_pixels(id, scene):
|
||||
pixels = []
|
||||
world_points = []
|
||||
img_data_segment = get_camera([GrabSim_pb2.CameraName.Head_Segment])
|
||||
im_segment = img_data_segment.images[0]
|
||||
|
||||
def get_obstacle_point(scene, cur_obstacle_world_points, map_ratio):
|
||||
img_data_depth = get_camera([GrabSim_pb2.CameraName.Head_Depth])
|
||||
im_depth = img_data_depth.images[0]
|
||||
|
||||
|
||||
d_segment = np.frombuffer(im_segment.data, dtype=im_segment.dtype).reshape(
|
||||
(im_segment.height, im_segment.width, im_segment.channels))
|
||||
d_depth = np.frombuffer(im_depth.data, dtype=im_depth.dtype).reshape(
|
||||
(im_depth.height, im_depth.width, im_depth.channels))
|
||||
|
||||
d_segment = np.transpose(d_segment, (1, 0, 2))
|
||||
d_depth = np.transpose(d_depth, (1, 0, 2))
|
||||
|
||||
for i in range(0, d_segment.shape[0],5):
|
||||
for j in range(0, d_segment.shape[1], 5):
|
||||
if d_segment[i][j][0] == id:
|
||||
pixels.append([i, j])
|
||||
for pixel in pixels:
|
||||
world_points.append(transform_co(img_data_depth, pixel[0], pixel[1], d_depth[pixel[0]][pixel[1]][0], scene))
|
||||
return world_points
|
||||
|
||||
|
||||
|
||||
|
||||
def get_obstacle_point(db, scene, cur_obstacle_world_points, map_ratio):
|
||||
cur_obstacle_pixel_points = []
|
||||
object_pixels = {}
|
||||
colors = [
|
||||
'red',
|
||||
'pink',
|
||||
'purple',
|
||||
'blue',
|
||||
'cyan',
|
||||
'green',
|
||||
'yellow',
|
||||
'orange',
|
||||
'brown',
|
||||
'gold',
|
||||
]
|
||||
|
||||
img_data_segment = get_camera([GrabSim_pb2.CameraName.Head_Segment])
|
||||
img_data_depth = get_camera([GrabSim_pb2.CameraName.Head_Depth])
|
||||
img_data_color = get_camera([GrabSim_pb2.CameraName.Head_Color])
|
||||
|
||||
im_segment = img_data_segment.images[0]
|
||||
im_depth = img_data_depth.images[0]
|
||||
im_color = img_data_color.images[0]
|
||||
|
||||
d_segment = np.frombuffer(im_segment.data, dtype=im_segment.dtype).reshape((im_segment.height, im_segment.width, im_segment.channels))
|
||||
d_depth = np.frombuffer(im_depth.data, dtype=im_depth.dtype).reshape((im_depth.height, im_depth.width, im_depth.channels))
|
||||
d_color = np.frombuffer(im_color.data, dtype=im_color.dtype).reshape((im_color.height, im_color.width, im_color.channels))
|
||||
|
||||
|
||||
items = img_data_segment.info.split(";")
|
||||
objs_id = {}
|
||||
for item in items:
|
||||
key, value = item.split(":")
|
||||
objs_id[int(key)] = value
|
||||
# plt.imshow(d_depth, cmap="gray" if "depth" in im_depth.name.lower() else None)
|
||||
# plt.show()
|
||||
#
|
||||
|
|
@ -362,6 +415,7 @@ def get_obstacle_point(scene, cur_obstacle_world_points, map_ratio):
|
|||
for j in range(0, d_segment.shape[1], map_ratio):
|
||||
if d_depth[i][j][0] == 600:
|
||||
continue
|
||||
|
||||
# if d_segment[i][j] == 96:
|
||||
# print(f"apple的像素坐标：({i},{j})")
|
||||
# print(f"apple的深度：{d_depth[i][j][0]}")
|
||||
|
|
@ -372,19 +426,74 @@ def get_obstacle_point(scene, cur_obstacle_world_points, map_ratio):
|
|||
# print(f"kettle的世界坐标: {transform_co(img_data_depth, i, j, d_depth[i][j][0], scene)}")
|
||||
if d_segment[i][j][0] in obstacle_objs_id:
|
||||
cur_obstacle_pixel_points.append([i, j])
|
||||
if d_segment[i][j][0] not in not_key_objs_id:
|
||||
# 首先检查键是否存在
|
||||
if d_segment[i][j][0] in object_pixels:
|
||||
# 如果键存在，那么添加元组(i, j)到对应的值中
|
||||
object_pixels[d_segment[i][j][0]].append([i, j])
|
||||
else:
|
||||
# 如果键不存在，那么创建一个新的键值对，其中键是d_segment[i][j][0]，值是一个包含元组(i, j)的列表
|
||||
object_pixels[d_segment[i][j][0]] = [[i, j]]
|
||||
# print(cur_obstacle_pixel_points)
|
||||
for pixel in cur_obstacle_pixel_points:
|
||||
world_point = transform_co(img_data_depth, pixel[0], pixel[1], d_depth[pixel[0]][pixel[1]][0], scene)
|
||||
cur_obstacle_world_points.append([world_point[0], world_point[1]])
|
||||
# print(f"{pixel}：{[world_point[0], world_point[1]]}")
|
||||
|
||||
plt.imshow(d_color, cmap="gray" if "depth" in im_depth.name.lower() else None)
|
||||
|
||||
for key, value in object_pixels.items():
|
||||
if key == 101 or key == 0:
|
||||
continue
|
||||
if key in [91, 84]:
|
||||
X = np.array(value)
|
||||
db.fit(X)
|
||||
labels = db.labels_
|
||||
# 将数据按照聚类标签分组，并打印每个分组的数据
|
||||
for i in range(max(labels) + 1): # 从0到最大聚类标签的值
|
||||
group_data = X[labels == i] # 获取当前标签的数据
|
||||
x_max = max(p[0] for p in group_data)
|
||||
y_max = max(p[1] for p in group_data)
|
||||
x_min = min(p[0] for p in group_data)
|
||||
y_min = min(p[1] for p in group_data)
|
||||
if x_max - x_min < 10 or y_max - y_min < 10:
|
||||
continue
|
||||
# 在指定的位置绘制方框
|
||||
# 创建矩形框
|
||||
rect = patches.Rectangle((x_min, y_min), (x_max - x_min), (y_max - y_min), linewidth=1, edgecolor=colors[key % 10],
|
||||
facecolor='none')
|
||||
plt.text(x_min, y_min, f'{objs_id[key]}', fontdict={'family': 'serif', 'size': 10, 'color': 'green'}, ha='center',
|
||||
va='center')
|
||||
plt.gca().add_patch(rect)
|
||||
else:
|
||||
x_max = max(p[0] for p in value)
|
||||
y_max = max(p[1] for p in value)
|
||||
x_min = min(p[0] for p in value)
|
||||
y_min = min(p[1] for p in value)
|
||||
# 在指定的位置绘制方框
|
||||
# 创建矩形框
|
||||
rect = patches.Rectangle((x_min, y_min), (x_max - x_min), (y_max - y_min), linewidth=1, edgecolor=colors[key % 10],
|
||||
facecolor='none')
|
||||
plt.text(x_min, y_min, f'{objs_id[key]}', fontdict={'family': 'serif', 'size': 10, 'color': 'green'}, ha='center',
|
||||
va='center')
|
||||
plt.gca().add_patch(rect)
|
||||
# point1 = min(value, key=lambda x: (x[0], x[1]))
|
||||
# point2 = max(value, key=lambda x: (x[0], x[1]))
|
||||
# width = point2[1] - point1[1]
|
||||
# height = point2[0] - point1[0]
|
||||
# rect = patches.Rectangle((0, 255), 15, 30, linewidth=1, edgecolor='g',
|
||||
# facecolor='none')
|
||||
|
||||
|
||||
# 将矩形框添加到图像中
|
||||
# plt.gca().add_patch(rect)
|
||||
plt.show()
|
||||
return cur_obstacle_world_points
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def get_semantic_map(camera, cur_objs, objs_name):
|
||||
scene = Observe(0)
|
||||
objs = scene.objects
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
|
|
@ -353,10 +353,8 @@ class Scene:
|
|||
# Since status.walkers is a list, some walkerIDs would change after removing a walker.
|
||||
remove_list.append(walkerID)
|
||||
|
||||
index_shift_list = [ 0 for _ in range(len(self.state["customer_mem"])) ]
|
||||
|
||||
stub.RemoveWalkers(GrabSim_pb2.RemoveList(IDs=remove_list, scene=self.sceneID))
|
||||
|
||||
self.state["customer_mem"] = {}
|
||||
w = self.status.walkers
|
||||
for i in range(len(w)):
|
||||
self.state["customer_mem"][w[i].name] = i
|
||||
|
|
@ -365,13 +363,23 @@ class Scene:
|
|||
s = stub.Observe(GrabSim_pb2.SceneID(value=self.sceneID))
|
||||
scene = stub.RemoveWalkers(GrabSim_pb2.RemoveList(IDs=IDs, scene=self.sceneID))
|
||||
time.sleep(2)
|
||||
self.state["customer_mem"] = {}
|
||||
w = self.status.walkers
|
||||
for i in range(len(w)):
|
||||
self.state["customer_mem"][w[i].name] = i
|
||||
return
|
||||
|
||||
|
||||
def clean_walker(self):
|
||||
stub.CleanWalkers(GrabSim_pb2.SceneID(value=self.sceneID))
|
||||
def clean_walkers(self):
|
||||
scene = stub.CleanWalkers(GrabSim_pb2.SceneID(value=self.sceneID))
|
||||
self.state["customer_mem"]={}
|
||||
return scene
|
||||
|
||||
def control_walker(self, walkerID,autowalk,speed,X,Y,Yaw=0):
|
||||
|
||||
if not isinstance(walkerID, int):
|
||||
walkerID = self.walker_index2mem(walkerID)
|
||||
|
||||
pose = GrabSim_pb2.Pose(X=X, Y=Y, Yaw=Yaw)
|
||||
scene = stub.ControlWalkers(
|
||||
GrabSim_pb2.WalkerControls(controls=[GrabSim_pb2.WalkerControls.WControl(id=walkerID, autowalk=autowalk, speed=speed, pose=pose)], scene=self.sceneID)
|
||||
|
|
@ -389,6 +397,10 @@ class Scene:
|
|||
for control in control_list_ls:
|
||||
if control[-1]!= None:
|
||||
walkerID = control[0]
|
||||
|
||||
if not isinstance(walkerID, int):
|
||||
walkerID = self.walker_index2mem(walkerID)
|
||||
|
||||
# cont = self.status.walkers[walkerID].name + ":"+control[-1]
|
||||
# self.control_robot_action(control[walkerID], 3, cont)
|
||||
self.customer_say(walkerID,control[-1])
|
||||
|
|
@ -511,6 +523,9 @@ class Scene:
|
|||
if isinstance(name,int):
|
||||
name = self.walker_index2mem(name)
|
||||
|
||||
# if not isinstance(walkerID, int):
|
||||
# name = self.walker_index2mem(walkerID)
|
||||
|
||||
print(f'{name} say: {sentence}')
|
||||
if self.show_bubble and show_bubble:
|
||||
self.walker_bubble(name,sentence)
|
||||
|
|
@ -752,7 +767,7 @@ class Scene:
|
|||
scene = stub.Do(action)
|
||||
print(scene.info)
|
||||
|
||||
def navigation_move(self, cur_objs, objs_name_set, cur_obstacle_world_points, v_list, map_ratio, scene_id=0, map_id=11):
|
||||
def navigation_move(self, cur_objs, objs_name_set, cur_obstacle_world_points, v_list, map_ratio, db, scene_id=0, map_id=11):
|
||||
print('------------------navigation_move----------------------')
|
||||
scene = stub.Observe(GrabSim_pb2.SceneID(value=scene_id))
|
||||
walk_value = [scene.location.X, scene.location.Y]
|
||||
|
|
@ -765,10 +780,12 @@ class Scene:
|
|||
print("walk_v", walk_v)
|
||||
action = GrabSim_pb2.Action(scene=scene_id, action=GrabSim_pb2.Action.ActionType.WalkTo, values=walk_v)
|
||||
scene = stub.Do(action)
|
||||
cur_obstacle_world_points = camera.get_obstacle_point(scene, cur_obstacle_world_points,map_ratio)
|
||||
|
||||
cur_objs, objs_name_set = camera.get_semantic_map(GrabSim_pb2.CameraName.Head_Segment, cur_objs,
|
||||
objs_name_set)
|
||||
|
||||
cur_obstacle_world_points = camera.get_obstacle_point(db, scene, cur_obstacle_world_points,map_ratio)
|
||||
|
||||
|
||||
# if scene.info == "Unreachable":
|
||||
print(scene.info)
|
||||
|
||||
|
|
@ -785,10 +802,12 @@ class Scene:
|
|||
action = GrabSim_pb2.Action(scene=scene_id, action=GrabSim_pb2.Action.ActionType.WalkTo, values=walk_v)
|
||||
scene = stub.Do(action)
|
||||
|
||||
cur_obstacle_world_points = camera.get_obstacle_point(scene, cur_obstacle_world_points, map_ratio)
|
||||
|
||||
cur_objs, objs_name_set = camera.get_semantic_map(GrabSim_pb2.CameraName.Head_Segment, cur_objs,
|
||||
objs_name_set)
|
||||
|
||||
cur_obstacle_world_points = camera.get_obstacle_point(db, scene, cur_obstacle_world_points, map_ratio)
|
||||
|
||||
|
||||
# if scene.info == "Unreachable":
|
||||
print(scene.info)
|
||||
return cur_objs, objs_name_set, cur_obstacle_world_points
|
||||
|
|
@ -830,7 +849,7 @@ class Scene:
|
|||
if len(self.all_frontier_list) == 0:
|
||||
free_list = list(self.visited)
|
||||
free_array = np.array(free_list)
|
||||
print(f"主动探索完成！以下是场景中可以到达的点：{free_array}；其余点均是障碍物不可达")
|
||||
print(f"主动探索完成！保存了二维地图与环境中重点物品语义信息！")
|
||||
|
||||
# # 画地图: X行Y列，第一行在下面
|
||||
# plt.clf()
|
||||
|
|
|
|||
|
|
@ -2,12 +2,16 @@
|
|||
环境主动探索和记忆
|
||||
要求输出探索结果（语义地图）对环境重点信息记忆。生成环境的语义拓扑地图，和不少于10个环境物品的识别和位置记忆，可以是图片或者文字或者格式化数据。
|
||||
"""
|
||||
import json
|
||||
import math
|
||||
import time
|
||||
|
||||
import matplotlib as mpl
|
||||
import pickle
|
||||
|
||||
import numpy as np
|
||||
from matplotlib import pyplot as plt
|
||||
from sklearn.cluster import DBSCAN
|
||||
|
||||
from robowaiter.scene.scene import Scene
|
||||
class SceneAEM(Scene):
|
||||
|
|
@ -22,8 +26,10 @@ class SceneAEM(Scene):
|
|||
cur_obstacle_world_points = []
|
||||
objs_name_set = set()
|
||||
visited_obstacle = set()
|
||||
obj_json_data = []
|
||||
db = DBSCAN(eps=4, min_samples=2)
|
||||
|
||||
map_ratio = 5
|
||||
map_ratio = 3
|
||||
# # 创建一个颜色映射，其中0表示黑色，1表示白色
|
||||
# cmap = plt.cm.get_cmap('gray')
|
||||
# cmap.set_under('black')
|
||||
|
|
@ -44,11 +50,12 @@ class SceneAEM(Scene):
|
|||
# navigation_move([[237,490]], i, map_id)
|
||||
# navigation_test(i,map_id)
|
||||
map_map = np.zeros((math.ceil(950 / map_ratio), math.ceil(1850 / map_ratio)))
|
||||
|
||||
while True:
|
||||
goal = self.explore(map, 120) # cur_pos 指的是当前机器人的位置，场景中应该也有接口可以获取
|
||||
if goal is None:
|
||||
break
|
||||
cur_objs, objs_name_set, cur_obstacle_world_points= self.navigation_move(cur_objs, objs_name_set, cur_obstacle_world_points, [[goal[0], goal[1]]], map_ratio, 0, 11)
|
||||
cur_objs, objs_name_set, cur_obstacle_world_points= self.navigation_move(cur_objs, objs_name_set, cur_obstacle_world_points, [[goal[0], goal[1]]], map_ratio, db,0, 11)
|
||||
|
||||
for point in cur_obstacle_world_points:
|
||||
if point[0] < -350 or point[0] > 600 or point[1] < -400 or point[1] > 1450:
|
||||
|
|
@ -66,8 +73,15 @@ class SceneAEM(Scene):
|
|||
# plt.imshow(map_map, cmap='binary', alpha=0.5, origin='lower')
|
||||
# plt.axis('off')
|
||||
plt.show()
|
||||
time.sleep(1)
|
||||
print("------------物品信息--------------")
|
||||
print(cur_objs)
|
||||
|
||||
for i in range(len(cur_objs)):
|
||||
obj_json_data.append({"id":f"{i}", "name":f"{cur_objs[i].name}", "location":f"{cur_objs[i].location}", "height":f"{cur_objs[i].location.Z * 2}"})
|
||||
|
||||
with open('../../proto/objs.json', 'w') as file:
|
||||
json.dump(obj_json_data, file)
|
||||
# for i in range(-350, 600):
|
||||
# for j in range(-400, 1450):
|
||||
# i = (math.floor((i + 350) / map_ratio))
|
||||
|
|
|
|||
|
|
@ -25,65 +25,61 @@ class SceneVLM(Scene):
|
|||
self.signal_event_list = [
|
||||
|
||||
# 场景1：带小女孩找阳光下的空位
|
||||
(3, self.add_walker, (5, 230, 1200)),
|
||||
(1, self.control_walker, (0, False, 200, 60, 520, 0)),
|
||||
(9, self.customer_say, (0, "早上好呀，我想找个能晒太阳的地方。")),
|
||||
(-1, self.customer_say, (0,"可以带我过去嘛？")),
|
||||
(0, self.control_walker, (0, False, 50, 140, 1200, 180)), # 小女孩站在了 BrightTable1 旁边就餐啦
|
||||
# (3, self.add_walker, (5, 230, 1200)), # 0号"Girl02_C_3"
|
||||
# (1, self.control_walker, (0, False, 200, 60, 520, 0)),
|
||||
# (9, self.customer_say, (0, "早上好呀，我想找个能晒太阳的地方。")),
|
||||
# (-1, self.customer_say, (0,"可以带我过去嘛？")),
|
||||
# (0, self.control_walker, (0, False, 50, 140, 1200, 180)), # 小女孩站在了 BrightTable1 旁边就餐啦
|
||||
|
||||
# 场景2：有个胖胖男人点单交流并要咖啡，帮他送去角落的桌子
|
||||
|
||||
|
||||
# # 场景2：有个胖胖男人点单交流并要咖啡，帮他送去角落的桌子
|
||||
# (3, self.add_walker, (5, 230, 1200)), # 小女孩
|
||||
# 上述准备
|
||||
# (self.st2, self.add_walker, (26, -28, -150, 90)),
|
||||
# (self.st2, self.add_walker, (10, -70, -200, -45)),
|
||||
# (self.st2, self.customer_say, (1, "嘿，RoboWaiter，过来一下！")),
|
||||
# (self.st2+10, self.control_walkers_and_say, ([[[1, False, 100, -18, -200, -90, "你们这有什么饮料嘛？"]]])), # 20 胖胖男到了 BrightTable6
|
||||
# (self.st2+15, self.customer_say, (1, "咖啡有哪些呢？")),# 10
|
||||
# (self.st2+15+5, self.customer_say, (1,"来杯卡布奇诺吧。")), # 15
|
||||
|
||||
# 场景3：有位女士要杯水和冰红茶
|
||||
(3, self.add_walker, (5, 230, 1200)),
|
||||
(0, self.add_walker, (26, -30, -200, -90)),
|
||||
(0, self.add_walker, (10, -80, -180, -45)),
|
||||
# # 上述准备
|
||||
# (0, self.add_walker, (21, 65, 1000, -90)), # 男 'BrightTable2': (65, 1000, 135),
|
||||
# (0, self.add_walker, (32, -80, 850, 135)), # 女 'BrightTable3': (-80, 850, 135),
|
||||
# (0, self.add_walker, (1, 60, 420, 135)), # 生成小男孩随机游走
|
||||
(0,self.add_walkers,([[[21, 65, 1000, -90],[32, -80, 850, 135],[1, 60, 420, 135]]])),
|
||||
(0, self.control_walker, (5, True, 50, 250, 1200, 180)), #设置id=4 的2小男孩随机游走红随机游走
|
||||
# (0, self.add_walker, (48, 60, 520,0)),# 生成他妈妈
|
||||
# (0, self.add_walker, (31, 60, 600, -90)), # 女红色排队 7号
|
||||
# (0, self.add_walker, (20, 60, 680, -90)), # 大胖男色排队 8号
|
||||
# (0, self.add_walker, (9, 60, 760, -90)), # 男灰黑排队 9号
|
||||
# (0, self.add_walker, (29, -290, 400, 180)), # 青色女人占了位置 BrightTable5
|
||||
(0, self.add_walkers, ([[[48, 60, 520,0], [31, 60, 600, -90], [20, 60, 680, -90],[9, 60, 760, -90],[29, -290, 400, 180]]])),
|
||||
|
||||
(0, self.customer_say, (6, "哎呦，今天这么多人，还有空位吗？")),# 女士问
|
||||
(10, self.customer_say, (6, "我带着孩子呢，想要宽敞亮堂的地方。")), # 女士问
|
||||
# 好的，我明白了，那么您可以选择我们的家庭亲子座，这样可以容纳您的孩子，并且更加宽敞舒适。
|
||||
# 这里可以加一下自主导航和探索，找到一个位置
|
||||
# 好的，我明白了，那么我们推荐您到大厅的桌子，那里的空间比较宽敞，环境也比较明亮，适合带着孩子一起用餐。
|
||||
# # # 上述准备
|
||||
# (10, self.add_walker, (26, -28, -150, 90)),
|
||||
# (0, self.add_walker, (10, -70, -200, -45)),
|
||||
# (6, self.customer_say, (1, "嘿，RoboWaiter，过来一下！")),
|
||||
# (8, self.control_walkers_and_say, ([[[1, False, 100, -18, -200, -90, "你们这有什么饮料嘛？"]]])), # 20 胖胖男到了 BrightTable6
|
||||
# (2, self.customer_say, (1, "咖啡有哪些呢？")),# 10
|
||||
# (2, self.customer_say, (1,"来杯卡布奇诺吧。")), # 15
|
||||
# (2, self.customer_say, (1, "来杯卡布奇诺吧。")),
|
||||
|
||||
|
||||
(8, self.customer_say, (6, "大厅的桌子好啊，快带我去呀！")),
|
||||
(10, self.control_walker, (6, False, 50,-250, 480, 0)), # #290, 400
|
||||
# (5, self.customer_say, (6, "我想来杯水，帮我孩子拿个酸奶吧。")),
|
||||
(-1, self.customer_say, (6, "谢谢！")),
|
||||
|
||||
# 9号灰色男 排队排着排着，不排了
|
||||
(0, self.control_walker, (9, False, 100, 100, 760, 180)),
|
||||
(0, self.control_walker, (9, True, 100, 0, 0, 180)),
|
||||
# ### 增加场景，孩子说热要开空调 或者9号随机游走和说
|
||||
# # 场景3：有位女士要杯水和冰红茶
|
||||
# (3, self.add_walker, (5, 230, 1200)),
|
||||
# (0, self.add_walker, (26, -30, -200, -90)),
|
||||
# (0, self.add_walker, (10, -80, -180, -45)),
|
||||
# # # # 上述准备
|
||||
# # (0, self.add_walker, (21, 65, 1000, -90)), # 男 'BrightTable2': (65, 1000, 135),
|
||||
# # (0, self.add_walker, (32, -80, 850, 135)), # 女 'BrightTable3': (-80, 850, 135),
|
||||
# # (0, self.add_walker, (1, 60, 420, 135)), # 生成小男孩随机游走
|
||||
# (0,self.add_walkers,([[[21, 65, 1000, -90],[32, -80, 850, 135],[1, 60, 420, 135]]])),
|
||||
# (0, self.control_walker, (5, True, 50, 250, 1200, 180)), #设置id=4 的2小男孩随机游走红随机游走
|
||||
# # (0, self.add_walker, (48, 60, 520,0)),# 生成他妈妈
|
||||
# # (0, self.add_walker, (31, 60, 600, -90)), # 女红色排队 7号
|
||||
# # (0, self.add_walker, (20, 60, 680, -90)), # 大胖男色排队 8号
|
||||
# # (0, self.add_walker, (9, 60, 760, -90)), # 男灰黑排队 9号
|
||||
# # (0, self.add_walker, (29, -290, 400, 180)), # 青色女人占了位置 BrightTable5
|
||||
# (0, self.add_walkers, ([[[48, 60, 520,0], [31, 60, 600, -90], [20, 60, 680, -90],[9, 60, 760, -90],[29, -290, 400, 180]]])),
|
||||
#
|
||||
# # (5, self.customer_say, (6, "哎呦，今天这么多人，还有空位吗？")),# 女士问
|
||||
# # (15, self.customer_say, (6, "我带着孩子呢，想要宽敞亮堂的地方。")), # 女士问
|
||||
# # 好的，我明白了，那么您可以选择我们的家庭亲子座，这样可以容纳您的孩子，并且更加宽敞舒适。
|
||||
# # 这里可以加一下自主导航和探索，找到一个位置
|
||||
# # 好的，我明白了，那么我们推荐您到大厅的桌子，那里的空间比较宽敞，环境也比较明亮，适合带着孩子一起用餐。
|
||||
# (8, self.customer_say, (6, "大厅的桌子好啊，快带我去呀！")),
|
||||
# (15, self.control_walker, (6, False, 50,-250, 480, 0)), # #290, 400
|
||||
# # (3, self.customer_say, (6, "我想来杯水，帮我孩子拿个酸奶吧。")),
|
||||
# # ### 9号灰色男 排队排着排着，不排了
|
||||
# (0, self.control_walker, (9, False, 100, 100, 760, 180)),
|
||||
# (0, self.control_walker, (9, False, 100, 0, 0, 180)),
|
||||
# (0, self.control_walkers_and_say, ([[[9, True, 100, 0, 0, 180, "好热呀！太阳也好大！"]]])),
|
||||
# # (9, True, 100, 0, 0, 180)),
|
||||
# # (0, self.customer_say, (1, "咖啡有哪些呢？")),
|
||||
# ### 增加场景，孩子说热要开空调 或者9号随机游走和说
|
||||
# (0, self.control_walker, (9, True, 100, 0, 0, 180)),
|
||||
# # # ### 增加场景，孩子说热要开空调 或者9号随机游走和说
|
||||
# # (90, self.customer_say, (6, "谢谢！")), #倒水+取放酸奶 90s
|
||||
# (3, self.customer_say, (6, "谢谢！")),
|
||||
|
||||
|
||||
|
||||
# 场景4：三人排队点单，女士要保温杯
|
||||
# # # 场景4：三人排队点单，女士要保温杯
|
||||
# (3, self.add_walker, (5, 230, 1200)),
|
||||
# (0, self.add_walker, (26, -30, -200, -90)),
|
||||
# (0, self.add_walker, (10, -80, -180, -45)),
|
||||
|
|
@ -95,46 +91,40 @@ class SceneVLM(Scene):
|
|||
# (0, self.add_walker, (20, 60, 680, -90)), # 大胖男排队
|
||||
# (0, self.add_walker, (9, 60, 760, -90)), # 男灰黑色排队
|
||||
# (0, self.add_walker, (29, -290, 400, 180)), # # 青色女人占了位置 BrightTable5
|
||||
# # # # 上述准备
|
||||
(0, self.control_walkers_and_say, ([[[7, False, 100, 60, 520, 0, "我昨天保温杯好像落在你们咖啡厅了，你看到了吗？"]]])),
|
||||
(5, self.customer_say, (7,"你可以帮我拿来吗，我在前门的桌子前等你。")),
|
||||
(1, self.control_walker,(7, False, 80, -10, 520, 90)),# 红女士在吧台前后退一步
|
||||
(1, self.control_walker, (7, False, 80, 240, 1000, -45)), # 红女士走到Table1前
|
||||
(1, self.control_walker, (8, False, 100, 60, 600, -90)), # 大胖男排队往前走一步
|
||||
(2, self.control_walker, (9, False, 100, 60, 680, -90)), # 男灰黑色排队往前走一步
|
||||
(6, self.customer_say, (7,"就是这个杯子！找到啦，好开心！")), # 红女士在Table1前
|
||||
# # # # # # 上述准备
|
||||
# # (5, self.control_walkers_and_say, ([[[7, False, 100, 60, 520, 180, "我昨天保温杯好像落在你们咖啡厅了，你看到了吗？"]]])), #13
|
||||
# (0, self.control_walkers_and_say, ([[[7, False, 100, 60, 520, 180, "我昨天保温杯好像落在你们咖啡厅了，你看到了吗？"]]])),
|
||||
# (5, self.customer_say, (7,"你可以帮我拿来吗，我在前门的桌子前等你。")),
|
||||
# (1, self.control_walker,(7, False, 80, -10, 520, 90)),# 红女士在吧台前后退一步
|
||||
# (1, self.control_walker, (7, False, 80, 240, 1000, -45)), # 红女士走到Table1前
|
||||
# (1, self.control_walker, (8, False, 100, 60, 600, -90)), # 大胖男排队往前走一步
|
||||
# (2, self.control_walker, (9, False, 100, 60, 680, -90)), # 男灰黑色排队往前走一步
|
||||
# (6, self.customer_say, (7,"就是这个杯子！找到啦，好开心！")), # 红女士在Table1前
|
||||
# (5, self.customer_say, (7, "不用了")), # 红女士在Table1前
|
||||
|
||||
|
||||
|
||||
# 场景5：三人排队点单，一人要冰红茶，一个要点心，一个没座位了赠送保温杯
|
||||
# (3, self.add_walker, (5, 230, 1200)),
|
||||
# (0, self.add_walker, (26, -30, -200, -90)),
|
||||
# (0, self.add_walker, (10, -80, -180, -45)),
|
||||
# (0, self.add_walker, (21, 65, 1000, -90)), # 男 'BrightTable2': (65, 1000, 135),
|
||||
# (0, self.add_walker, (32, -80, 850, 135)),
|
||||
# (0, self.add_walker, (1, 60, 220, 135)),
|
||||
# (0, self.add_walker, (48, 60, 320, 0)), # 生成他妈妈
|
||||
# (0, self.add_walker, (31, 280, 1200, -45)), # # 女红色排队 7号找保温杯的顾客
|
||||
# (0, self.add_walker, (20, 60, 680, -90)), # 大胖男排队
|
||||
# (0, self.add_walker, (9, 60, 760, -90)), # 男灰黑色排队
|
||||
# (0, self.add_walker, (29, -290, 400, 180)), # # 青色女人占了位置 BrightTable5
|
||||
|
||||
## 9号灰色男 排队排着排着，不排了
|
||||
(0, self.control_walker, (9, False, 100, 100, 760, 180)),
|
||||
(0, self.control_walker, (9, True, 100, 0, 0, 180)),
|
||||
# 上述准备
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(0, self.control_walker, (9, False, 100, 60, 760, -90)),
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# 场景6：大胖男点了外卖，离开啦
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(2, self.control_walkers_and_say,([[[8, False, 100, 60, 520, 0, "给我来份午餐套餐。"]]])),
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# # 场景5：三人排队点单，一人要冰红茶，一个要点心，一个没座位了赠送保温杯
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(3, self.add_walker, (5, 230, 1200)),
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(0, self.add_walker, (26, -30, -200, -90)),
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(0, self.add_walker, (10, -80, -180, -45)),
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(0, self.add_walker, (21, 65, 1000, -90)), # 男 'BrightTable2': (65, 1000, 135),
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(0, self.add_walker, (32, -80, 850, 135)),
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(0, self.add_walker, (1, 60, 220, 135)),
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(0, self.add_walker, (48, 60, 320, 0)), # 生成他妈妈
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(0, self.add_walker, (31, 280, 1200, -45)), # # 女红色排队 7号找保温杯的顾客
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(0, self.add_walker, (20, 60, 680, -90)), # 大胖男排队
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(0, self.add_walker, (9, 60, 760, -90)), # 男灰黑色排队
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(0, self.add_walker, (29, -290, 400, 180)), # # 青色女人占了位置 BrightTable5
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# # 上述准备
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# # 场景6：大胖男点了外卖，离开啦
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# (9, self.control_walkers_and_say, ([[[8, False, 100, 60, 520, 0, "给我来份午餐套餐。"]]])), #原来写了26s
|
||||
(0, self.animation_reset,()), #重置任务
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(2, self.customer_say, (8, "打包吧，快点！")),
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(2, self.control_walker, (9, False, 100, 60, 620, -90)), # 男灰黑色排队往前走一步
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||||
(-1, self.customer_say, (8, "谢啦，我赶时间！")),
|
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(2, self.control_walker, (8, False, 250, 20, 520, -90)), # 大胖男着急得离开啦
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(2, self.control_walker, (8, False, 250, 240, -150, -90)),
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# (6, self.customer_say, (8, "打包吧，快点！")),
|
||||
# (2, self.control_walker, (9, False, 100, 60, 620, -90)), # 男灰黑色排队往前走一步
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||||
# # (100, self.customer_say, (8, "谢啦，我赶时间！")), #100这个时间很合适
|
||||
# (2, self.control_walker, (8, False, 250, 20, 520, -90)), # 大胖男着急得离开啦
|
||||
# (2, self.control_walker, (8, False, 250, 240, -150, -90)),
|
||||
(5, self.remove_walkers, ([[0,7,8]])),
|
||||
# (2, self.control_walker, (6, False, 100, 60, 520, 0)), # 9号变7号 男灰黑色排队往前,轮到他
|
||||
|
||||
|
|
@ -142,8 +132,15 @@ class SceneVLM(Scene):
|
|||
# 场景7：最后排队那个随机游走 9号变为6号，随机游走。
|
||||
# 机器人自主发现任务，走一圈去擦桌子/拖地，碰到灰色男问好，灰色男说“太阳大，要关窗帘和空调调低”
|
||||
# 开了空调
|
||||
(2, self.control_walker, (6, False, 100, 60, 520, 0)), # 9号变7号 男灰黑色排队往前,轮到他
|
||||
(2, self.customer_say, (6, "好热呀！太阳也好大！"))
|
||||
(2, self.control_walker, (6, False, 100, 60, 520, 0)), # 10号变7号 男灰黑色排队往前,轮到他
|
||||
# (2, self.customer_say, (6, "好热呀！太阳也好大！")),
|
||||
# (10, self.control_walkers_and_say, ([[[6, True, 100, 60, 520, 0, "谢谢，这下凉快了"]]])),
|
||||
|
||||
|
||||
# 场景8 结束了，删除所有顾客。此处增加自主探索发现空间比较暗，打开大厅灯
|
||||
(3, self.clean_walkers, ()),
|
||||
(1, self.add_walker, (17, 60, 1000)),# 增加警察，提醒下班啦
|
||||
(3, self.control_walkers_and_say, ([[[0, False, 150, 60, 520, 0, "下班啦！别忘了打扫卫生。"]]])),
|
||||
|
||||
|
||||
|
||||
|
|
@ -179,6 +176,11 @@ class SceneVLM(Scene):
|
|||
self.control_walkers_and_say([[0,False,150,end[0],end[1],90,"谢谢！"]])
|
||||
self.scene_flag += 1
|
||||
|
||||
# 获得所有顾客的名字
|
||||
# print("=================")
|
||||
# for cus in self.status.walkers:
|
||||
# print(cus)
|
||||
# print("=================")
|
||||
pass
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
|
|
|||
Loading…
Reference in New Issue