修复找不到sub_task_seq的Bug

robowaiter/behavior_lib/_base/Behavior.py

@@ -36,15 +36,10 @@ class Bahavior(ptree.behaviour.Behaviour):
         return ins_name
 
     def __init__(self,*args):
-        self.name = Bahavior.get_ins_name(*args)
-        #get valid args
-        # self.valid_arg_list = []
-        # lines = self.valid_params.strip().splitlines()
-        # for line in lines:
-        #     self.valid_arg_list.append((x.strip for x in line.split(",")))
+        ins_name = self.__class__.get_ins_name(*args)
         self.args = args
 
-        super().__init__(self.name)
+        super().__init__(ins_name)
 
     def _update(self) -> ptree.common.Status:
         print("this is just a _base behavior node.")

robowaiter/behavior_tree/obtea/OptimalBTExpansionAlgorithm.py

@@ -187,7 +187,7 @@ class OptBTExpAlgorithm:
                     self.ptml_string += c_set_str
                 elif child.type == 'act':
                     if '(' not in child.content.name:
-                        self.ptml_string += 'act '+child.content.name+"()\n"
+                        self.ptml_string += 'act ' + child.content.name + "()\n"
                     else:
                         self.ptml_string += 'act ' + child.content.name + "\n"
             elif isinstance(child, ControlBT):

robowaiter/behavior_tree/obtea/tools.py

@@ -10,7 +10,7 @@ import time
 def print_action_data_table(goal,start,actions):
     data = []
     for a in actions:
-        data.append([a.name ,a.pre ,a.add ,a.del_set ,a.cost])
+        data.append([a.name , a.pre , a.add , a.del_set , a.cost])
     data.append(["Goal" ,goal ," " ,"Start" ,start])
     print(tabulate(data, headers=["Name", "Pre", "Add" ,"Del" ,"Cost"], tablefmt="fancy_grid"))  # grid plain simple github fancy_grid
 

robowaiter/llm_client/ask_llm.py

@@ -20,8 +20,10 @@ with open(file_path,'r',encoding="utf-8") as f:
 
 def ask_llm(question):
     if question in test_questions_dict:
-        return test_questions_dict[question]
-    ans = single_round(question)
+        ans = test_questions_dict[question]
+    else:
+        ans = single_round(question)
+    print(f"大模型输出： {ans}")
     return ans
 
 

robowaiter/llm_client/data/test_questions.txt

@@ -1 +1 @@
-{"测试VLM：做一杯咖啡": {"Answer": "测试VLM：做一杯咖啡", "Goal": "{\"At(Coffee,Bar)\"}"}, "测试VLM：前往桌子2": {"Answer": " 测试VLM：前往桌子2", "Goal": "{\"At(Robot,Table2)\"}"}, "测试AEM": {"Answer": "测试AEM", "Goal": "{\"EnvExplored()\"}"}, "测试VLM：倒一杯水": {"Answer": "测试VLM：倒一杯水", "Goal": "{\"On(Water,WaterTable)\"}"}, "测试VLM：开空调": {"Answer": "测试VLM：开空调", "Goal": "{\"Is(AC,On)\"}"}, "测试VLM：关空调": {"Answer": "测试VLM：关空调", "Goal": "{\"Is(AC,Off)\"}"}, "测试VLM：关大厅灯": {"Answer": "测试VLM：关大厅灯", "Goal": "{\"Is(HallLight,Off)\"}"}, "测试VLM：开大厅灯": {"Answer": "测试VLM：开大厅灯", "Goal": "{\"Is(HallLight,On)\"}"}, "测试VLM：关筒灯": {"Answer": "测试VLM：关筒灯", "Goal": "{\"Is(TubeLight,Off)\"}"}, "测试VLM：开筒灯": {"Answer": "测试VLM：开筒灯", "Goal": "{\"Is(TubeLight,On)\"}"}, "测试VLM：关窗帘": {"Answer": "测试VLM：关窗帘", "Goal": "{\"Is(Curtain,Off)\"}"}, "测试VLM：开窗帘": {"Answer": "测试VLM：开窗帘", "Goal": "{\"Is(Curtain,On)\"}"}, "测试VLM：拖地": {"Answer": "测试VLM：拖地", "Goal": "{\"Is(Floor,Clean)\"}"}, "测试VLM：擦桌子": {"Answer": "测试VLM：擦桌子", "Goal": "{\"Is(Table1,Clean)\"}"}, "测试VLM：整理椅子": {"Answer": "测试VLM：整理椅子", "Goal": "{\"Is(Chairs,Clean)\"}"}, "测试VLM：把冰红茶放到Table2": {"Answer": "测试VLM：把冰红茶放到Table2", "Goal": "{\"On(BottledDrink,Table2)\"}"}}
+{"测试VLM：做一杯咖啡": {"Answer": "测试VLM：做一杯咖啡", "Goal": "{\"At(Coffee,Bar)\"}"}, "测试VLN：前往2号桌": {"Answer": " 测试VLN：前往2号桌", "Goal": "{\"At(Robot,Table2)\"}"}, "测试AEM": {"Answer": "测试AEM", "Goal": "{\"EnvExplored()\"}"}, "测试VLM：倒一杯水": {"Answer": "测试VLM：倒一杯水", "Goal": "{\"On(Water,WaterTable)\"}"}, "测试VLM：开空调": {"Answer": "测试VLM：开空调", "Goal": "{\"Is(AC,On)\"}"}, "测试VLM：关空调": {"Answer": "测试VLM：关空调", "Goal": "{\"Is(AC,Off)\"}"}, "测试VLM：关大厅灯": {"Answer": "测试VLM：关大厅灯", "Goal": "{\"Is(HallLight,Off)\"}"}, "测试VLM：开大厅灯": {"Answer": "测试VLM：开大厅灯", "Goal": "{\"Is(HallLight,On)\"}"}, "测试VLM：关筒灯": {"Answer": "测试VLM：关筒灯", "Goal": "{\"Is(TubeLight,Off)\"}"}, "测试VLM：开筒灯": {"Answer": "测试VLM：开筒灯", "Goal": "{\"Is(TubeLight,On)\"}"}, "测试VLM：关窗帘": {"Answer": "测试VLM：关窗帘", "Goal": "{\"Is(Curtain,Off)\"}"}, "测试VLM：开窗帘": {"Answer": "测试VLM：开窗帘", "Goal": "{\"Is(Curtain,On)\"}"}, "测试VLM：拖地": {"Answer": "测试VLM：拖地", "Goal": "{\"Is(Floor,Clean)\"}"}, "测试VLM：擦桌子": {"Answer": "测试VLM：擦桌子", "Goal": "{\"Is(Table1,Clean)\"}"}, "测试VLM：整理椅子": {"Answer": "测试VLM：整理椅子", "Goal": "{\"Is(Chairs,Clean)\"}"}, "测试VLM：把冰红茶放到Table2": {"Answer": "测试VLM：把冰红茶放到Table2", "Goal": "{\"On(BottledDrink,Table2)\"}"}}

robowaiter/llm_client/data_raw/test_questions.csv

@@ -1,6 +1,6 @@
 Question,Answer,Goal
 测试VLM：做一杯咖啡,测试VLM：做一杯咖啡,"{""At(Coffee,Bar)""}"
-测试VLM：前往桌子2, 测试VLM：前往桌子2,"{""At(Robot,Table2)""}"
+测试VLN：前往2号桌,测试VLN：前往2号桌,"{""At(Robot,Table2)""}"
 测试AEM,测试AEM,"{""EnvExplored()""}"
 测试VLM：倒一杯水,测试VLM：倒一杯水,"{""On(Water,WaterTable)""}"
 测试VLM：开空调,测试VLM：开空调,"{""Is(AC,On)""}"

robowaiter/robot/robot.py

@@ -32,7 +32,8 @@ class Robot(object):
 
     def load_BT(self):
         self.bt = load_bt_from_ptml(self.scene, self.ptml_path,self.behavior_lib_path)
-        sub_task_place_holder = find_node_by_name(self.bt.root,"SubTaskPlaceHolder")
+        sub_task_place_holder = find_node_by_name(self.bt.root,"SubTaskPlaceHolder()")
+        print(sub_task_place_holder)
         if sub_task_place_holder:
             sub_task_seq = sub_task_place_holder.parent
             sub_task_seq.children.pop()
@@ -44,12 +45,13 @@ class Robot(object):
 
     def expand_sub_task_tree(self,goal):
         if self.action_list is None:
-            self.action_list = self.collect_action_nodes()
             print("\n--------------------")
-            print(f"首次运行行为树扩展算法，收集到{len(self.action_list)}个有效动作:")
-            for a in self.action_list:
-                if "Turn" in a.name:
-                    print(a.name)
+            print(f"首次运行行为树扩展算法")
+            self.action_list = self.collect_action_nodes()
+            print(f"共收集到{len(self.action_list)}个实例化动作:")
+            # for a in self.action_list:
+            #     if "Turn" in a.name:
+            #         print(a.name)
             print("--------------------\n")
 
 
@@ -85,6 +87,7 @@ class Robot(object):
         behavior_dict = load_behavior_tree_lib()
         for cls in behavior_dict["act"].values():
             if cls.can_be_expanded:
+                print(f"可扩展动作：{cls.__name__}, 存在{len(cls.valid_args)}个有效论域组合")
                 if cls.num_args == 0:
                     action_list.append(Action(name=cls.get_ins_name(),**cls.get_info()))
                 if cls.num_args == 1:
@@ -94,7 +97,7 @@ class Robot(object):
                     for args in cls.valid_args:
                         action_list.append(Action(name=cls.get_ins_name(*args),**cls.get_info(*args)))
 
-        print(action_list)
+        # print(action_list)
         # action_list = [
         #     Action(name='MakeCoffee', pre={'At(Robot,CoffeeMachine)'},
         #            add={'At(Coffee,Bar)'}, del_set=set(), cost=1),

robowaiter/scene/tasks/VLN.py

@@ -22,7 +22,7 @@ class SceneVLN(Scene):
         super().__init__(robot)
         # 在这里加入场景中发生的事件， (事件发生的时间，事件函数)
         self.event_list = [
-            (5, self.create_chat_event("测试VLN：前往桌子")),
+            (5, self.create_chat_event("测试VLN：前往2号桌")),
         ]
 
     def _reset(self):

robowaiter/utils/bt/draw.py

@@ -129,7 +129,7 @@ def dot_tree(
     root_name = str(root.id)
     node_root = pydot.Node(
         name=root_name,
-        label=get_node_label(root.name, root),
+        label=get_node_label(root.ins_name, root),
         shape=node_shape,
         style="filled",
         fillcolor=node_colour,
@@ -319,7 +319,7 @@ def render_dot_tree(root: behaviour.Behaviour,
         root, visibility_level, collapse_decorators,
         with_blackboard_variables=with_blackboard_variables,
         with_qualified_names=with_qualified_names)
-    filename_wo_extension_to_convert = root.name if name is None else name
+    filename_wo_extension_to_convert = root.ins_name if name is None else name
     filename_wo_extension = utilities.get_valid_filename(filename_wo_extension_to_convert)
     filenames = {}
     for extension, writer in {"dot": graph.write, "png": graph.write_png, "svg": graph.write_svg}.items():

run_robowaiter.py

@@ -1,7 +1,7 @@
 import os
 from robowaiter import Robot, task_map
 
-TASK_NAME = 'VLM'
+TASK_NAME = 'VLN'
 
 # create robot
 project_path = "./robowaiter"

zoo/opt_bt_expansion/OptimalBTExpansionAlgorithm.py

@@ -183,7 +183,7 @@ class OptBTExpAlgorithm:
                     c_set_str = ', '.join(map(str, child.content)) + "\n"
                     self.ptml_string += c_set_str
                 elif child.type == 'act':
-                    self.ptml_string += 'act '+child.content.name+"\n"
+                    self.ptml_string += 'act ' + child.content.name + "\n"
             elif isinstance(child, ControlBT):
                 if parnode.type == '?':
                     self.ptml_string += "selector{\n"

zoo/opt_bt_expansion/tools.py

@@ -10,7 +10,7 @@ import time
 def print_action_data_table(goal,start,actions):
     data = []
     for a in actions:
-        data.append([a.name ,a.pre ,a.add ,a.del_set ,a.cost])
+        data.append([a.name , a.pre , a.add , a.del_set , a.cost])
     data.append(["Goal" ,goal ," " ,"Start" ,start])
     print(tabulate(data, headers=["Name", "Pre", "Add" ,"Del" ,"Cost"], tablefmt="fancy_grid"))  # grid plain simple github fancy_grid