Merge branch 'main' of https://github.com/HPCL-EI/RoboWaiter into main

README.md

@@ -1,15 +1,65 @@
 # RoboWaiter
 大模型具身智能比赛-机器人控制端
 
-### 机器人控制
-1. 加载场景
-```python
-from scene_utils import control
-control.init_world(scene_num=1, mapID=3)
-```
-当前只有一个咖啡馆场景。加载操作只需要执行一遍，当引擎进入相应场景后，可以用`control.reset()`重置场景。
+# 项目安装（必看）
+## 环境要求
+Python=3.10
 
-2. 物品类别
+### 安装步骤
+```shell
+cd RoboWaiter
+pip install -e .
+```
+以上步骤将完成robowaiter项目以及相关依赖库的安装
+
+### 快速入门
+1. 安装UE及Harix插件，打开默认项目并运行
+2. 运行 run_robowaiter.py 文件即可实现机器人控制端与仿真器的交互
+
+
+# 运行流程介绍
+run_robowaiter.py 入口文件如下：
+```python
+import os
+from robowaiter import Robot, task_map
+
+TASK_NAME = 'GQA'
+
+# create robot
+project_path = "./robowaiter"
+ptml_path = os.path.join(project_path, 'robot/Default.ptml')
+behavior_lib_path = os.path.join(project_path, 'behavior_lib')
+
+robot = Robot(ptml_path,behavior_lib_path)
+
+# create task
+task = task_map[TASK_NAME](robot)
+task.reset()
+task.run()
+```
+
+## Robot 
+Robot是机器人类，包括从ptml加载行为树的方法，以及执行行为树的方法等
+
+
+## task_map
+task_map是任务字典，通过任务缩写来返回相应的场景类。
+
+| 缩写 | 任务      |
+|----|---------|
+| AEM  | 主动探索和记忆 |
+| GQA  | 具身多轮对话  |
+| VLN  | 视觉语言导航  |
+| VLM  | 视觉语言操作  |
+| OT  | 复杂开放任务   |
+| AT  | 自主任务    |
+
+
+## Scene
+Scene是场景基类，task_map返回的任务场景都继承于Scene。
+该类实现了一些通用的场景操作接口。
+
+### 场景中物品类别
 
 | ID  | Item                 |
 |-----|----------------------|
@@ -115,4 +165,11 @@ control.init_world(scene_num=1, mapID=3)
 | 252 | Floor                |
 | 253 | Roof                 |
 | 254 | Wall                 |
-注意：78及以后无法使用add_object方法生成
+注意：78及以后无法使用add_object方法生成
+
+# 调用大模型接口
+运行llm_client.py文件调用大模型进行多轮对话。
+```shell
+python llm_client.py
+```
+输入字符即可等待回答，输入end表示对话结束。

llm_clients.py

@@ -0,0 +1,29 @@
+import requests
+import urllib3
+
+########################################
+#   该文件实现了与大模型的简单通信、多轮对话，输入end表示对话结束
+########################################
+
+# 忽略https的安全性警告
+urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
+
+url = "https://45.125.46.134:25344/v1/chat/completions"
+headers = {"Content-Type": "application/json"}
+
+#在这里输入你的问题
+k=input()
+data_memory=[]
+n=1
+while k!='end':
+	question_now=k
+	user_dict={"role": "user","content":question_now}
+	data_memory.append(user_dict)
+	#print(data_memory)
+	response = requests.post(url, headers=headers, json={"messages":data_memory, "repetition_penalty": 1.0}, verify=False)
+	answer=response.json()['choices'][n]['message']['content']
+	print(answer)
+	assistant_dict={"role": "assistant","content":answer}
+	data_memory.append(assistant_dict)
+	n=n+2
+	k=input()

llm_comm/llm_client.py

@@ -1,28 +0,0 @@
-import requests
-import urllib3
-
-########################################
-#   该文件实现了与大模型的简单通信
-########################################
-
-# 忽略https的安全性警告
-urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
-
-#在这里输入你的问题
-question = "假设你是一个咖啡厅的机器人服务员，有一个顾客的请求是'请给我一杯咖啡'，请生成对应的行为树来控制机器人完成该动作"
-
-url = "https://45.125.46.134:25344/v1/completions"
-headers = {"Content-Type": "application/json"}
-data = {
-    "prompt": question
-}
-
-response = requests.post(url, headers=headers, json=data, verify=False)
-
-if response.status_code == 200:
-    result = response.json()
-    print(f'问题：{question}')
-    print('回答：' + result['choices'][0]['text'])
-else:
-    print("请求失败:", response.status_code)
-

requirements.txt

@@ -0,0 +1,8 @@
+antlr4-python3-runtime
+py_trees
+shortuuid
+protobuf==3.20.0
+gym==0.21.0
+grpcio==1.53.0
+requests
+urllib3

robowaiter/__init__.py

@@ -0,0 +1,3 @@
+
+from robowaiter.robot.robot import Robot
+from robowaiter.scene import task_map

robowaiter/behavior_lib/Behavior.py

@@ -0,0 +1,22 @@
+import py_trees as ptree
+from typing import Any
+
+# base Behavior
+class Bahavior(ptree.behaviour.Behaviour):
+    scene = None
+    def __init__(self, name: str, scene):
+        super().__init__(name)
+        self.scene = scene
+
+    def setup(self, **kwargs: Any) -> None:
+        return super().setup(**kwargs)
+
+    def initialise(self) -> None:
+        return super().initialise()
+
+    def update(self) -> ptree.common.Status:
+        print("this is a base behavior node.")
+        return ptree.common.Status.SUCCESS
+
+    def terminate(self, new_status: ptree.common.Status) -> None:
+        return super().terminate(new_status)

robowaiter/behavior_lib/CoffeeCupFound.py

@@ -1,10 +1,10 @@
 import py_trees as ptree
 from typing import Any
+from robowaiter.behavior_lib.Behavior import Bahavior
 
-
-class CoffeeCupFound(ptree.behaviour.Behaviour):
+class CoffeeCupFound(Bahavior):
     def __init__(self, name: str, scene):
-        super().__init__(name)
+        super().__init__(name, scene)
 
     def setup(self, **kwargs: Any) -> None:
         return super().setup(**kwargs)

robowaiter/behavior_lib/CoffeeCupGrasped.py

@@ -1,10 +1,11 @@
 import py_trees as ptree
 from typing import Any
+from robowaiter.behavior_lib.Behavior import Bahavior
 
-class CoffeeCupGrasped(ptree.behaviour.Behaviour):
-
+class CoffeeCupGrasped(Bahavior):
     def __init__(self, name: str, scene):
-        super().__init__(name)
+        super().__init__(name, scene)
+
 
     def setup(self, **kwargs: Any) -> None:
         return super().setup(**kwargs)

robowaiter/behavior_lib/IsChatting.py

@@ -0,0 +1,20 @@
+import py_trees as ptree
+from typing import Any
+from robowaiter.behavior_lib.Behavior import Bahavior
+
+class IsChatting(Bahavior):
+    def __init__(self, name: str, scene):
+        super().__init__(name, scene)
+
+    def setup(self, **kwargs: Any) -> None:
+        return super().setup(**kwargs)
+
+    def initialise(self) -> None:
+        return super().initialise()
+
+    def update(self) -> ptree.common.Status:
+        # if self.scene.status?
+        return ptree.common.Status.SUCCESS
+
+    def terminate(self, new_status: ptree.common.Status) -> None:
+        return super().terminate(new_status)

robowaiter/behavior_tree/__init__.py

@@ -0,0 +1,3 @@
+
+# from robowaiter.behavior_tree.behavior_tree import BehaviorTree
+from robowaiter.behavior_tree.behavior_tree import load_bt_from_ptml

robowaiter/behavior_tree/behavior_tree.py

@@ -0,0 +1,19 @@
+import py_trees as ptree
+from robowaiter.behavior_tree.ptml import ptmlCompiler
+
+
+def load_bt_from_ptml(scene, ptml_path, behavior_lib_path):
+    ptml_bt = ptmlCompiler.load(scene, ptml_path, behavior_lib_path)
+    bt =  ptree.trees.BehaviourTree(ptml_bt)
+
+    with open(ptml_path, 'r') as f:
+        ptml = f.read()
+
+    print(f'BT loaded: \n {ptml}')
+
+    # print(ptree.display.unicode_tree(root=bt.root, show_status=True))
+    return bt
+
+# class BehaviorTree(ptree):
+#     def __init__(self):
+#         super().__init__()

robowaiter/behavior_tree/ptml/CoffeeDelivery.ptml

@@ -1,35 +1,35 @@
-//sequence:
-//    act action1()
-//    act action2(2, 2.3, True)
-//
-//    parallel 2:
-//        act action3(int a, float b)
-//        act action4()
-
-sequence{
-    selector{
-        cond CoffeeCupFound()
-        task FindCoffeeCup()
-        sequence{
-            cond SeqTest()
-            task Move(1.2, 2, 2.3, True)
-            task Grasp()
-            parallel 3 {
-                cond Istask()
-                task TestTask()
-            }
-        }
-    }
-    selector{
-        cond CoffeeCupGrasped()
-        task GraspCoffeeCup()
-    }
-    selector{
-        cond DestinationAReached()
-        task ReachDestinationA()
-    }
-    selector{
-        cond CoffeeCupPlaced()
-        task PlaceCoffeeCup()
-    }
-}
+//sequence:
+//    act action1()
+//    act action2(2, 2.3, True)
+//
+//    parallel 2:
+//        act action3(int a, float b)
+//        act action4()
+
+sequence{
+    selector{
+        cond CoffeeCupFound()
+        task FindCoffeeCup()
+        sequence{
+            cond SeqTest()
+            task Move(1.2, 2, 2.3, True)
+            task Grasp()
+            parallel 3 {
+                cond Istask()
+                task TestTask()
+            }
+        }
+    }
+    selector{
+        cond CoffeeCupGrasped()
+        task GraspCoffeeCup()
+    }
+    selector{
+        cond DestinationAReached()
+        task ReachDestinationA()
+    }
+    selector{
+        cond CoffeeCupPlaced()
+        task PlaceCoffeeCup()
+    }
+}

robowaiter/behavior_tree/ptml/ptml_test.py

@@ -1,16 +1,16 @@
 import os
 import py_trees as ptree
 
-from scene_utils import control
+from scene import scene
 from ptmlCompiler import load
 
 
 if __name__ == '__main__':
 
-    project_path = "."
-    
+    project_path = ""
+
     ptml_path = os.path.join(project_path, 'CoffeeDelivery.ptml')
-    behavior_lib_path = os.path.join(project_path, 'behaviour_lib')
+    behavior_lib_path = os.path.join(project_path, '../../behavior_lib')
 
     scene = control.Scene(sceneID=0)
     # load

robowaiter/llm_client/ask_llm.py

@@ -0,0 +1,31 @@
+
+import requests
+import urllib3
+
+########################################
+#   该文件实现了与大模型的简单通信
+########################################
+
+# 忽略https的安全性警告
+urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
+
+def ask_llm(question):
+    url = "https://45.125.46.134:25344/v1/completions"
+    headers = {"Content-Type": "application/json"}
+    data = {
+        "prompt": question
+    }
+
+    response = requests.post(url, headers=headers, json=data, verify=False)
+
+    if response.status_code == 200:
+        result = response.json()
+        return result['choices'][0]['text']
+    else:
+        return "大模型请求失败:", response.status_code
+
+
+if __name__ == '__main__':
+    question = "假设你是一个咖啡厅的机器人服务员，有一个顾客的请求是'请给我一杯咖啡'，请生成对应的行为树来控制机器人完成该动作"
+
+    print(ask_llm(question))

robowaiter/robot/Default.ptml

@@ -0,0 +1,105 @@
+//sequence:
+//    act action1()
+//    act action2(2, 2.3, True)
+//
+//    parallel 2:
+//        act action3(int a, float b)
+//        act action4()
+
+sequence{
+    selector{
+        cond CoffeeCupFound()
+        task FindCoffeeCup()
+        sequence{
+            cond SeqTest()
+            task Move(1.2, 2, 2.3, True)
+            task Grasp()
+            parallel 3 {
+                cond Istask()
+                task TestTask()
+            }
+        }
+    }
+    selector{
+        cond CoffeeCupGrasped()
+        task GraspCoffeeCup()
+    }
+    selector{
+        cond DestinationAReached()
+        task ReachDestinationA()
+    }
+    selector{
+        cond CoffeeCupPlaced()
+        task PlaceCoffeeCup()
+    }
+}
+
+
+
+
+
+
+sequence{
+    cond NotAt(Coffee,Table)
+    selector{
+        cond Holding(Coffee)
+        sequence{
+            selector{
+                cond At(Bar)
+                task MoveTo(Bar)
+            }
+            task Grasp(Coffee)
+        }
+    }
+    selector{
+        cond At(Table)
+        task MoveTo(Table)
+    }
+    Put(Coffee)
+}
+
+
+selector{
+    sequence{
+        cond At(Table)
+        cond Holding(Coffee)
+        task Put(Table)
+    }
+    sequence{
+        cond NotHolding(Coffee)
+        task MoveTo(Table)
+    }
+    sequence{
+        cond At(Bar)
+        task Grasp(Coffee)
+    }
+    task MoveTo(Table)
+
+
+
+    sequence{
+        cond Coffee()
+        task FindCoffeeCup()
+        sequence{
+            cond SeqTest()
+            task Move(1.2, 2, 2.3, True)
+            task Grasp()
+            parallel 3 {
+                cond Istask()
+                task TestTask()
+            }
+        }
+    }
+    selector{
+        cond At(Table,Coffee)
+        task GraspCoffeeCup()
+    }
+    selector{
+        cond DestinationAReached()
+        task ReachDestinationA()
+    }
+    selector{
+        cond CoffeeCupPlaced()
+        task PlaceCoffeeCup()
+    }
+}

robowaiter/robot/robot.py

@@ -0,0 +1,20 @@
+import os
+import py_trees as ptree
+
+from robowaiter.behavior_tree import load_bt_from_ptml
+
+class Robot(object):
+    scene = None
+    def __init__(self,ptml_path,behavior_lib_path):
+        self.ptml_path = ptml_path
+        self.behavior_lib_path = behavior_lib_path
+
+    def set_scene(self,scene):
+        self.scene = scene
+
+    def load_BT(self):
+        self.bt = load_bt_from_ptml(self.scene, self.ptml_path,self.behavior_lib_path)
+
+
+if __name__ == '__main__':
+    pass

robowaiter/scene/__init__.py

@@ -0,0 +1,17 @@
+
+from .scene import Scene
+from robowaiter.scene.tasks.AEM import SceneAEM
+from robowaiter.scene.tasks.GQA import SceneGQA
+from robowaiter.scene.tasks.VLN import SceneVLN
+from robowaiter.scene.tasks.VLM import SceneVLM
+from robowaiter.scene.tasks.Open_tasks import SceneOT
+from robowaiter.scene.tasks.Auto_tasks import SceneAT
+
+task_map = {
+    "AEM": SceneAEM,
+    "GQA": SceneGQA,
+    "VLN": SceneVLN,
+    "VLM": SceneVLM,
+    "OT": SceneOT,
+    "AT": SceneAT,
+}

robowaiter/scene/scene.py

@@ -1,12 +1,10 @@
 import time
-import gym
 import grpc
 import numpy as np
 
-from proto import GrabSim_pb2
-from proto import GrabSim_pb2_grpc
+from robowaiter.proto import GrabSim_pb2
+from robowaiter.proto import GrabSim_pb2_grpc
 
-from ptml import ptmlCompiler
 
 channel = grpc.insecure_channel(
     "localhost:30001",
@@ -21,7 +19,7 @@ animation_step = [4, 5, 7, 3, 3]
 loc_offset = [-700, -1400]
 
 
-def init_world(scene_num, mapID):
+def init_world(scene_num=1, mapID=3):
     stub.SetWorld(GrabSim_pb2.BatchMap(count=scene_num, mapID=mapID))
     time.sleep(3)  # wait for the map to load
 
@@ -34,7 +32,21 @@ def image_extract(camera_data):
 
 
 class Scene:
+    robot = None
+    state = {}
     """
+    # 当前场景的状态
+    state: {
+        "chat_pool": [    #未处理的顾客的对话池
+            {
+                "pos": 顾客的位置,
+                "chat": 顾客对话的内容
+            }
+        ],
+        
+        "status":   # 仿真器中的观测信息，见下方详细解释
+    }
+    
     status:
         location: Dict[X: float, Y: float]
         rotation: Dict[Yaw: float]
@@ -47,18 +59,40 @@ class Scene:
         collision: str, info: str
     """
 
-    def __init__(self, sceneID=0):
+    def __init__(self,robot, sceneID=0):
         self.sceneID = sceneID
         self.use_offset = True
-        self.BT = None
+
+        # init robot
+        robot.set_scene(self)
+        robot.load_BT()
+        self.robot = robot
+
+
+    def run(self):
+        pass
+
 
     @property
     def status(self):
         return stub.Observe(GrabSim_pb2.SceneID(value=self.sceneID))
 
-    def reset(self):
+    def reset_sim(self):
         stub.Reset(GrabSim_pb2.ResetParams(scene=self.sceneID))
 
+        # reset world
+        init_world()
+
+        # reset state
+        self.state = {
+            "chatting_list": []
+        }
+
+
+
+    def reset(self):
+        self.reset_sim()
+
     def walker_control_generator(self, walkerID, autowalk, speed, X, Y, Yaw):
         if self.use_offset:
             X, Y = X + loc_offset[0], Y + loc_offset[1]
@@ -239,5 +273,3 @@ class Scene:
     def animation_reset(self):
         stub.ControlRobot(GrabSim_pb2.ControlInfo(scene=self.sceneID, type=0, action=0))
 
-    def load_BT(self, ptml_path, behaviour_lib_path):
-        self.BT = ptmlCompiler.load(self, ptml_path, behaviour_lib_path)

robowaiter/scene/tasks/AEM.py

@@ -0,0 +1,26 @@
+"""
+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。
+"""
+
+from robowaiter.scene.scene import Scene
+
+class SceneAEM(Scene):
+    def __init__(self, robot):
+        super().__init__(robot)
+
+        # control.init_world(1, 3)
+
+    def reset(self):
+        self.reset_sim()
+
+        self.add_object(0, 570, 1600, 85.5)  # type与物品编号对应，具体参考README.md
+        self.add_object(1, 570, 1630, 85.5)
+        self.add_object(2, 570, 1660, 85.5)
+        self.add_object(3, 580, 1680, 85.5)
+
+        # todo: 探索并获得语义地图
+        print(self.status.objects)  # 全部的物品信息，包括名称、位置等，与获得的语义地图进行对比
+
+    def run(self):
+        pass

robowaiter/scene/tasks/Auto_tasks.py

@@ -0,0 +1,26 @@
+"""
+在特定环境下，机器人发现目标，可自主完成任务
+1. 打扫地面：地面有垃圾，机器人主动扫地、清理地面垃圾
+2. 收拾桌子：桌子上的污渍，机器人主动擦桌子
+3. 摆椅子：椅子不正，机器人主动摆正椅子
+4. 开灯：室内光线暗，机器人主动打开房屋的灯
+"""
+
+# todo: 通过行为树控制自动任务
+
+from robowaiter.scene.scene import Scene
+
+
+class SceneAT(Scene):
+    def __init__(self, robot):
+        super().__init__(robot)
+
+    def reset(self):
+        self.reset_sim()
+
+        self.add_walker(1085, 2630, 220)
+        self.control_walker([self.walker_control_generator(0, False, 100, 755, 1900, 180)])
+
+    def run(self):
+        self.chat_bubble("顾客说：请给我一杯咖啡")
+

robowaiter/scene/tasks/GQA.py

@@ -1,5 +1,5 @@
 """
-具身多轮对话
+具身多轮对话 GQA
 点餐（order）的对话，咖啡厅服务员可以为客人（NPC）完成点餐基本对话
 场景对话（GQA）结合场景：询问卫生间、附近娱乐场所（数据来源自主定义）
 开始条件：顾客NPC发出点餐指令
@@ -8,3 +8,21 @@
 
 # todo: 使用大模型进行对话，获得指令信息，适时结束对话
 # order = {...}
+
+from robowaiter.scene.scene import Scene
+
+class SceneGQA(Scene):
+    def __init__(self, robot):
+        super().__init__(robot)
+
+    def reset(self):
+        self.reset_sim()
+
+        self.add_walker(1085, 2630, 220)
+        self.control_walker([self.walker_control_generator(0, False, 100, 755, 1900, 180)])
+
+
+    def run(self):
+        self.chat_bubble("顾客说：123546567")
+
+

robowaiter/scene/tasks/Open_tasks.py

@@ -8,12 +8,24 @@
 
 """
 
-from scene_utils import control
-
-# control.init_world(1, 3)
-
-scene = control.Scene(sceneID=0)
-
-scene.reset()
-
 # todo: 接收点单信息，大模型生成任务规划
+
+from robowaiter.scene.scene import Scene
+
+class SceneOT(Scene):
+    def __init__(self, robot):
+        super().__init__(robot)
+
+    def reset(self):
+        self.reset_sim()
+
+        self.add_walker(1085, 2630, 220)
+        self.control_walker([self.walker_control_generator(0, False, 100, 755, 1900, 180)])
+
+
+    def run(self):
+        self.chat_bubble("顾客说：请给我一杯咖啡")
+        
+
+
+

robowaiter/scene/tasks/VLM.py

@@ -0,0 +1,36 @@
+"""
+视觉语言操作
+机器人根据指令人的指令调节空调，自主探索环境导航到目标点，通过手臂的运动规划能力操作空调，比如开关按钮、调温按钮、显示面板
+"""
+
+import time
+from robowaiter.scene.scene import Scene
+
+
+class SceneVLM(Scene):
+    def __init__(self, robot):
+        super().__init__(robot)
+
+    def reset(self):
+        self.reset_sim()
+
+        self.add_walker(1085, 2630, 220)
+        self.control_walker([self.walker_control_generator(0, False, 100, 755, 1900, 180)])
+
+    def run(self):
+        # 空调操作
+        self.walk_to(950, 1260, 90)  # 没法转向？
+        # todo: 手臂操作
+        time.sleep(5)
+        self.walk_to(947, 1900, 0)
+        
+        # 物品挪动
+        # todo: 视觉导航至目标点，操作手臂至可抓位置
+        """
+        scene.grasp(1, your_objectID)
+        """
+
+        # todo: 视觉导航至目标点，找准释放位置
+        """
+        scene.release(1)
+        """

robowaiter/scene/tasks/VLN.py

@@ -0,0 +1,39 @@
+"""
+视觉语言导航
+识别顾客（NPC）靠近、打招呼、对话、领位导航到适合人数的空闲餐桌
+开始条件：监测到顾客靠近
+结束条件：完成领位，语音：“请问您想喝点什么？”，并等待下一步指令
+"""
+
+from robowaiter.scene.scene import Scene
+
+
+class SceneVLN(Scene):
+    def __init__(self, robot):
+        super().__init__(robot)
+
+    def reset(self):
+        self.reset_sim()
+
+        self.add_walker(1085, 2630, 220)
+        self.control_walker([self.walker_control_generator(0, False, 100, 755, 1900, 180)])
+
+    def run(self):
+        # 实现单顾客领位
+        self.add_walker(1085, 2630, 220)
+        self.control_walker([self.walker_control_generator(0, False, 100, 755, 1900, 180)])
+
+        # todo: 监测到顾客靠近，打招呼，对话，识别获取空闲餐桌位置
+        # 可以使用scene.chat_bubble(message)函数实现对话
+
+        """
+        scene.walk_to(your_free_table_location)
+        time.sleep(5)
+        scene.control_walker([scene.walker_control_generator(your_free_table_location)])
+        """
+
+        reach = True
+        if reach:
+            self.chat_bubble("请问您想喝点什么？")
+
+        print(self.status.walkers)

run_robowaiter.py

@@ -0,0 +1,16 @@
+import os
+from robowaiter import Robot, task_map
+
+TASK_NAME = 'GQA'
+
+# create robot
+project_path = "./robowaiter"
+ptml_path = os.path.join(project_path, 'robot/Default.ptml')
+behavior_lib_path = os.path.join(project_path, 'behavior_lib')
+
+robot = Robot(ptml_path,behavior_lib_path)
+
+# create task
+task = task_map[TASK_NAME](robot)
+task.reset()
+task.run()

scene_utils/__init__.py

@@ -1 +0,0 @@
-from . import control

setup.py

@@ -0,0 +1,22 @@
+from setuptools import setup, find_packages
+import os
+
+with open('requirements.txt') as f:
+    required = f.read().splitlines()
+
+setup(
+    name='robowaiter',
+    version='0.1.0',
+    packages=['robowaiter'],
+    install_requires=required,
+    author='HPCL-EI',
+    author_email='hpcl_ei@163.com',
+    description='RoboWaiter',
+    url='https://github.com/HPCL-EI/RoboWaiter',
+    classifiers=[
+        'Programming Language :: Python :: 3',
+        'License :: OSI Approved :: MIT License',
+        'Operating System :: OS Independent',
+    ],
+)
+

tasks/AEM.py

@@ -1,20 +0,0 @@
-"""
-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。
-"""
-
-from scene_utils import control
-
-# control.init_world(1, 3)
-
-scene = control.Scene(sceneID=0)
-
-scene.reset()
-scene.add_object(0, 570, 1600, 85.5)  # type与物品编号对应，具体参考README.md
-scene.add_object(1, 570, 1630, 85.5)
-scene.add_object(2, 570, 1660, 85.5)
-scene.add_object(3, 580, 1680, 85.5)
-
-# todo: 探索并获得语义地图
-
-print(scene.status.objects)  # 全部的物品信息，包括名称、位置等，与获得的语义地图进行对比

tasks/Auto_tasks.py

@@ -1,9 +0,0 @@
-"""
-在特定环境下，机器人发现目标，可自主完成任务
-1. 打扫地面：地面有垃圾，机器人主动扫地、清理地面垃圾
-2. 收拾桌子：桌子上的污渍，机器人主动擦桌子
-3. 摆椅子：椅子不正，机器人主动摆正椅子
-4. 开灯：室内光线暗，机器人主动打开房屋的灯
-"""
-
-# todo: 通过行为树控制自动任务

tasks/VLM.py

@@ -1,29 +0,0 @@
-"""
-视觉语言操作
-机器人根据指令人的指令调节空调，自主探索环境导航到目标点，通过手臂的运动规划能力操作空调，比如开关按钮、调温按钮、显示面板
-"""
-
-import time
-from scene_utils import control
-
-# control.init_world(1, 3)
-
-scene = control.Scene(sceneID=0)
-
-scene.reset()
-
-# 空调操作
-scene.walk_to(950, 1260, 90)  # 没法转向？
-# todo: 手臂操作
-time.sleep(5)
-scene.walk_to(947, 1900, 0)
-
-# 物品挪动
-# todo: 视觉导航至目标点，操作手臂至可抓位置
-"""
-scene.grasp(1, your_objectID)
-"""
-# todo: 视觉导航至目标点，找准释放位置
-"""
-scene.release(1)
-"""

tasks/VLN.py

@@ -1,32 +0,0 @@
-"""
-视觉语言导航
-识别顾客（NPC）靠近、打招呼、对话、领位导航到适合人数的空闲餐桌
-开始条件：监测到顾客靠近
-结束条件：完成领位，语音：“请问您想喝点什么？”，并等待下一步指令
-"""
-
-from scene_utils import control
-
-# control.init_world(1, 3)
-
-scene = control.Scene(sceneID=0)
-
-# 实现单顾客领位
-scene.reset()
-scene.add_walker(1085, 2630, 220)
-scene.control_walker([scene.walker_control_generator(0, False, 100, 755, 1900, 180)])
-
-# todo: 监测到顾客靠近，打招呼，对话，识别获取空闲餐桌位置
-# 可以使用scene.chat_bubble(message)函数实现对话
-
-"""
-scene.walk_to(your_free_table_location)
-time.sleep(5)
-scene.control_walker([scene.walker_control_generator(your_free_table_location)])
-"""
-
-reach = True
-if reach:
-    scene.chat_bubble("请问您想喝点什么？")
-
-print(scene.status.walkers)