config update - swipe and pnr

BKairat · Jan 21, 2023 · ee12746 · ee12746
1 parent 55fa5b4
commit ee12746
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Showing 4 changed files with 134 additions and 24 deletions.
diff --git a/myGym/configs/pnr_medium_multi2.json b/myGym/configs/pnr_medium_multi2.json
@@ -0,0 +1,55 @@
+{
+    #Environment
+    "env_name"  :"Gym-v0",
+    "workspace" :"table",
+    "engine"    :"pybullet",
+    "render"    :"opengl",
+    "camera"    :0,
+    "gui"       :0,
+    "visualize" :0,
+    "visgym"    :0,
+    #Robot
+    "robot"         :"panda",
+    "robot_action"  :"joints",
+    "robot_init"    :[-0.4, 0.6, 0.5],
+    "max_velocity"  :3,
+    "max_force"     :100,
+    "action_repeat" :1,
+    #Task
+    "task_type"             :"pnprot",
+    "task_objects"          :[{"init":{"obj_name":"bar","fixed":0,"rand_rot":0, "sampling_area":[-0.5,0.0,0.35,0.65,0.1,0.1]},
+                           "goal":{"obj_name":"bar_target","fixed":1,"rand_rot":1, "sampling_area":[-0.5,0.0,0.35,0.65,0.3,0.3]}},
+                           {"init":{"obj_name":"bar","fixed":0,"rand_rot":0, "sampling_area":[0.1,0.5,0.35,0.65,0.1,0.1]},
+                           "goal":{"obj_name":"bar_target","fixed":1,"rand_rot":1, "sampling_area":[0.1,0.5,0.35,0.65,0.3,0.3]}}],
+    "color_dict"            : {"cube_holes":[[0.2,0.5,0.2,1]], "target":[[0.3,0.3,0.3,1]]},
+    "used_objects"          :{"num_range":[0,0], "obj_list":[]},
+    // Observation
+    // actual_state options (pick one): "endeff_xyz", "endeff_6D" (robot end effector), "obj_xyz", "obj_6D", "vae", "yolact", "voxel" or "dope"
+    // goal_state options (pick one): "obj_xyz", "obj_6D", "vae", "yolact", "voxel" or "dope"
+    // additional_obs options (pick none/one/more): "joints_xyz", "joints_angles", "endeff_xyz", "endeff_6D", "touch", "distractor"
+    "observation"           : {"actual_state":"obj_6D", "goal_state":"obj_6D", "additional_obs":["endeff_xyz"]},
+    #Distractor
+    "distractors"           : {"list":null, "moveable":1, "constant_speed":0, "movement_dims":3,
+                                       "movement_endpoints":[-0.3, 0.3, 0.4, 0.7, 0.1, 0.3]},
+    #Reward
+    "reward"        :"pnprot",
+    "distance_type" :"euclidean",
+    "vae_path"      :null,
+    "yolact_path"   :null,
+    "yolact_config" :null,
+    #Train
+    "train_framework"   :"tensorflow",
+    "algo"              :"multi",
+    "num_networks"      :3,
+    "max_episode_steps" :1024,
+    "algo_steps"        :1024,
+    "steps"             :15000000,
+    "pretrained_model"  :null,
+    "multiprocessing"   :false,
+    #Evaluation
+    "eval_freq"     :1000000,
+    "eval_episodes" :50,
+    #Saving and logging
+    "logdir"    :"trained_models/pnr_medium",
+    "record"    :0
+    }
diff --git a/myGym/configs/train_swipe.json b/myGym/configs/train_swipe.json
@@ -17,8 +17,8 @@
     "action_repeat" :1,
     #Task
     "task_type"             :"pnpswipe",
-    "task_objects"          :[{"init":{"obj_name":"sponge","fixed":0,"rand_rot":0, "sampling_area":[-0.4,-0.5,0.4,0.5,0.08,0.08]},
-                           "goal":{"obj_name":"bar_target","fixed":1,"rand_rot":1, "sampling_area":[-0.0,-0.1,0.4,0.5,0.15,0.15]}}],
+    "task_objects"          :[{"init":{"obj_name":"sponge","fixed":0,"rand_rot":0, "sampling_area":[-0.4,-0.6,0.4,0.6,0.08,0.08]},
+                           "goal":{"obj_name":"bar_target","fixed":1,"rand_rot":1, "sampling_area":[-0.0,-0.2,0.4,0.6,0.15,0.15]}}],
     "color_dict"            : {"cube_holes":[[0.2,0.5,0.2,1]], "target":[[0.3,0.3,0.3,1]]},
     "used_objects"          :{"num_range":[0,0], "obj_list":[]},
     // Observation
@@ -41,11 +41,11 @@
     "num_networks"      :3,
     "max_episode_steps" :512,
     "algo_steps"        :512,
-    "steps"             :10000000,
+    "steps"             :15000000,
     "pretrained_model"  :null,
     "multiprocessing"   :false,
     #Evaluation
-    "eval_freq"     :500000,
+    "eval_freq"     :1000000,
     "eval_episodes" :50,
     #Saving and logging
     "logdir"    :"trained_models/debug",

diff --git a/myGym/configs/train_swipe_multi2.json b/myGym/configs/train_swipe_multi2.json
@@ -0,0 +1,55 @@
+{
+    #Environment
+    "env_name"  :"Gym-v0",
+    "workspace" :"table",
+    "engine"    :"pybullet",
+    "render"    :"opengl",
+    "camera"    :0,
+    "gui"       :0,
+    "visualize" :0,
+    "visgym"    :0,
+    #Robot
+    "robot"         :"kuka",
+    "robot_action"  :"joints",
+    "robot_init"    :[-0.4, 0.6, 0.5],
+    "max_velocity"  :3,
+    "max_force"     :100,
+    "action_repeat" :1,
+    #Task
+    "task_type"             :"pnpswipe",
+    "task_objects"          :[{"init":{"obj_name":"sponge","fixed":0,"rand_rot":0, "sampling_area":[-0.4,-0.6,0.4,0.6,0.08,0.08]},
+                           "goal":{"obj_name":"bar_target","fixed":1,"rand_rot":1, "sampling_area":[-0.0,-0.2,0.4,0.6,0.15,0.15]}},
+                           {"init":{"obj_name":"sponge","fixed":0,"rand_rot":0, "sampling_area":[0.1,0.2,0.4,0.6,0.08,0.08]},
+                           "goal":{"obj_name":"bar_target","fixed":1,"rand_rot":1, "sampling_area":[0.4,0.5,0.4,0.6,0.15,0.15]}}],
+    "color_dict"            : {"cube_holes":[[0.2,0.5,0.2,1]], "target":[[0.3,0.3,0.3,1]]},
+    "used_objects"          :{"num_range":[0,0], "obj_list":[]},
+    // Observation
+    // actual_state options (pick one): "endeff_xyz", "endeff_6D" (robot end effector), "obj_xyz", "obj_6D", "vae", "yolact", "voxel" or "dope"
+    // goal_state options (pick one): "obj_xyz", "obj_6D", "vae", "yolact", "voxel" or "dope"
+    // additional_obs options (pick none/one/more): "joints_xyz", "joints_angles", "endeff_xyz", "endeff_6D", "touch", "distractor"
+    "observation"           : {"actual_state":"obj_6D", "goal_state":"obj_6D", "additional_obs":["endeff_xyz"]},
+    #Distractor
+    "distractors"           : {"list":null, "moveable":1, "constant_speed":0, "movement_dims":3,
+                                       "movement_endpoints":[-0.3, 0.3, 0.4, 0.7, 0.1, 0.3]},
+    #Reward
+    "reward"        :"pnpswipe",
+    "distance_type" :"euclidean",
+    "vae_path"      :null,
+    "yolact_path"   :null,
+    "yolact_config" :null,
+    #Train
+    "train_framework"   :"tensorflow",
+    "algo"              :"multi",
+    "num_networks"      :3,
+    "max_episode_steps" :1024,
+    "algo_steps"        :1024,
+    "steps"             :15000000,
+    "pretrained_model"  :null,
+    "multiprocessing"   :false,
+    #Evaluation
+    "eval_freq"     :1000000,
+    "eval_episodes" :50,
+    #Saving and logging
+    "logdir"    :"trained_models/debug",
+    "record"    :0
+    }
diff --git a/myGym/envs/rewards.py b/myGym/envs/rewards.py
@@ -1287,22 +1287,22 @@ def gripper_reached_object(self, gripper, object):
 
     def find_compute(self, gripper, object):
         # initial reach
-        self.env.p.addUserDebugText("find object", [0.7,0.7,0.7], lifeTime=0.1, textColorRGB=[125,0,0])
+        self.env.p.addUserDebugText("Subgoal: Pick", [.65, 1., 0.5], lifeTime=0.1, textColorRGB=[125,0,0])
         dist = self.task.calc_distance(gripper, object)
         self.env.p.addUserDebugLine(gripper[:3], object[:3], lifeTime=0.1)
-        self.env.p.addUserDebugText("Distance: {}".format(round(dist,3)), [0.5,0.5,0.7], lifeTime=0.1, textColorRGB=[0, 125, 0])
+        self.env.p.addUserDebugText("Distance: {}".format(round(dist,3)), [0.65,1,0.55], lifeTime=0.1, textColorRGB=[0, 125, 0])
         if self.last_find_dist is None:
             self.last_find_dist = dist
         if self.last_owner != 0:
             self.last_find_dist = dist
         reward = self.last_find_dist - dist
-        self.env.p.addUserDebugText(f"Reward:{reward}", [0.7,0.7,1.0], lifeTime=0.1, textColorRGB=[0,125,0])
+        self.env.p.addUserDebugText(f"Reward:{reward}", [0.65,1,0.65], lifeTime=0.1, textColorRGB=[0,125,0])
         self.last_find_dist = dist
         if self.task.check_object_moved(self.env.task_objects["actual_state"], threshold=1.2):
             self.env.episode_over   = True
             self.env.episode_failed = True
         self.network_rewards[0] += reward
-        self.env.p.addUserDebugText(f"Rewards:{self.network_rewards[0]}", [0.7,0.7,1.1], lifeTime=0.1, textColorRGB=[0,0,125])
+        self.env.p.addUserDebugText(f"Rewards:{self.network_rewards[0]}", [0.65,1,0.7], lifeTime=0.1, textColorRGB=[0,0,125])
         return reward
 
     def move_compute(self, object, goal):
@@ -1329,7 +1329,7 @@ def move_compute(self, object, goal):
             self.env.episode_over = True
         ix = 1 if self.num_networks > 1 else 0
         self.network_rewards[ix] += reward
-        self.env.p.addUserDebugText(f"Rewards:{self.network_rewards[ix]}", [0.7,0.7,1.1], lifeTime=0.1, textColorRGB=[0,0,125])
+        self.env.p.addUserDebugText(f"Rewards:{self.network_rewards[ix]}", [0.65,1,0.7], lifeTime=0.1, textColorRGB=[0,0,125])
         return reward
 
 class ThreeStagePnP(TwoStagePnP):
@@ -1382,29 +1382,29 @@ def decide(self, observation=None):
 
     def move_compute(self, object, goal):
         # moving object above goal position (forced 2D reach)
-        self.env.p.addUserDebugText("move", [0.7,0.7,0.7], lifeTime=0.1, textColorRGB=[0,0,125])
+        self.env.p.addUserDebugText("Subgoal:move", [.65, 1., 0.5], lifeTime=0.1, textColorRGB=[0,0,125])
         object_XY = object[:3]
         goal_XY   = [goal[0], goal[1], goal[2]+0.2]
         self.env.p.addUserDebugLine(object_XY, goal_XY, lifeTime=0.1)
         dist = self.task.calc_distance(object_XY, goal_XY)
-        self.env.p.addUserDebugText("Distance: {}".format(round(dist,3)), [0.5,0.5,0.7], lifeTime=0.1, textColorRGB=[0, 125, 0])
+        self.env.p.addUserDebugText("Distance: {}".format(round(dist,3)), [0.65,1,0.55], lifeTime=0.1, textColorRGB=[0, 125, 0])
         if self.last_move_dist is None or self.last_owner != 1:
            self.last_move_dist = dist
         reward = self.last_move_dist - dist
         self.env.p.addUserDebugText(f"Reward:{reward}", [0.7,0.7,1.2], lifeTime=0.1, textColorRGB=[0,125,0])
         self.last_move_dist = dist
         ix = 1 if self.num_networks > 1 else 0
         self.network_rewards[ix] += reward
-        self.env.p.addUserDebugText(f"Rewards:{self.network_rewards[ix]}", [0.7,0.7,1.1], lifeTime=0.1, textColorRGB=[0,0,125])
+        self.env.p.addUserDebugText(f"Rewards:{self.network_rewards[ix]}", [0.65,1,0.7], lifeTime=0.1, textColorRGB=[0,0,125])
         return reward
 
 
     def place_compute(self, object, goal):
         # reach of goal position + task object height in Z axis and release
-        self.env.p.addUserDebugText("place", [0.7,0.7,0.7], lifeTime=0.1, textColorRGB=[125,125,0])
+        self.env.p.addUserDebugText("Subgoal: place", [.65, 1., 0.5], lifeTime=0.1, textColorRGB=[125,125,0])
         self.env.p.addUserDebugLine(object[:3], goal[:3], lifeTime=0.1)
         dist = self.task.calc_distance(object, goal)
-        self.env.p.addUserDebugText("Distance: {}".format(round(dist,3)), [0.5,0.5,0.7], lifeTime=0.1, textColorRGB=[0, 125, 0])
+        self.env.p.addUserDebugText("Distance: {}".format(round(dist,3)), [0.65,1,0.55], lifeTime=0.1, textColorRGB=[0, 125, 0])
         if self.last_place_dist is None or self.last_owner != 2:
             self.last_place_dist = dist
         reward = self.last_place_dist - dist
@@ -1422,7 +1422,7 @@ def place_compute(self, object, goal):
             self.env.episode_info = "Task finished in initial configuration"
             self.env.episode_over = True
         self.network_rewards[-1] += reward
-        self.env.p.addUserDebugText(f"Rewards:{self.network_rewards[-1]}", [0.7,0.7,1.1], lifeTime=0.1, textColorRGB=[0,0,125])
+        self.env.p.addUserDebugText(f"Rewards:{self.network_rewards[-1]}", [0.65,1,0.7], lifeTime=0.1, textColorRGB=[0,0,125])
         return reward
 
     def gripper_reached_object(self, gripper, object):
@@ -1510,7 +1510,7 @@ def find_compute(self, gripper, object):
         self.env.p.addUserDebugText("find object", [0.63,1,0.5], lifeTime=0.5, textColorRGB=[125,0,0])
         dist = self.task.calc_distance(gripper[:3], object[:3])
         self.env.p.addUserDebugLine(gripper[:3], object[:3], lifeTime=0.1)
-        self.env.p.addUserDebugText("Distance: {}".format(round(dist,3)), [0.59,1,0.65], lifeTime=0.5, textColorRGB=[0, 125, 0])
+        self.env.p.addUserDebugText("Distance: {}".format(round(dist,3)), [0.65,1,0.55], lifeTime=0.5, textColorRGB=[0, 125, 0])
         if self.last_find_dist is None:
             self.last_find_dist = dist
         if self.last_owner != 0:
@@ -1522,7 +1522,7 @@ def find_compute(self, gripper, object):
         #    self.env.episode_over   = True
         #    self.env.episode_failed = True
         self.network_rewards[0] += reward
-        self.env.p.addUserDebugText(f"Rewards:{self.network_rewards[0]}", [0.59,1,0.6], lifeTime=0.5, textColorRGB=[0,0,125])
+        self.env.p.addUserDebugText(f"Rewards:{self.network_rewards[0]}", [0.65,1,0.7], lifeTime=0.5, textColorRGB=[0,0,125])
         return reward
 
     def move_compute(self, object, goal):
@@ -1532,15 +1532,15 @@ def move_compute(self, object, goal):
         goal_XY   = goal[:3]
         self.env.p.addUserDebugLine(object_XY, goal_XY, lifeTime=0.1)
         dist = self.task.calc_distance(object_XY, goal_XY)
-        self.env.p.addUserDebugText("Distance: {}".format(round(dist,3)), [0.59,1,0.65], lifeTime=0.5, textColorRGB=[0, 125, 0])
+        self.env.p.addUserDebugText("Distance: {}".format(round(dist,3)), [0.65,1,0.55], lifeTime=0.5, textColorRGB=[0, 125, 0])
         if self.last_move_dist is None or self.last_owner != 1:
            self.last_move_dist = dist
         reward = self.last_move_dist - dist
         self.env.p.addUserDebugText(f"Reward:{reward}", [0.61,1,0.55], lifeTime=0.5, textColorRGB=[0,125,0])
         self.last_move_dist = dist
         ix = 1 if self.num_networks > 1 else 0
         self.network_rewards[ix] += reward
-        self.env.p.addUserDebugText(f"Rewards:{self.network_rewards[ix]}", [0.59,1,0.6], lifeTime=0.5, textColorRGB=[0,0,125])
+        self.env.p.addUserDebugText(f"Rewards:{self.network_rewards[ix]}", [0.65,1,0.7], lifeTime=0.5, textColorRGB=[0,0,125])
         return reward
 
 
@@ -1549,13 +1549,13 @@ def rotate_compute(self, object, goal):
         self.env.p.addUserDebugText("rotate", [0.63,1,0.5], lifeTime=0.1, textColorRGB=[125,125,0])
         self.env.p.addUserDebugLine(object[:3], goal[:3], lifeTime=0.1)
         dist = self.task.calc_distance(object, goal)
-        self.env.p.addUserDebugText("Distance: {}".format(round(dist,3)), [0.59,1,0.65], lifeTime=0.5, textColorRGB=[0, 125, 0])
+        self.env.p.addUserDebugText("Distance: {}".format(round(dist,3)), [0.65,1,0.55], lifeTime=0.5, textColorRGB=[0, 125, 0])
         if self.last_place_dist is None or self.last_owner != 2:
             self.last_place_dist = dist
         reward = self.last_place_dist - dist
 
         rot = self.task.calc_rot_quat(object, goal)
-        self.env.p.addUserDebugText("Rotation: {}".format(round(rot,3)), [0.57,1,0.7], lifeTime=0.5, textColorRGB=[0, 222, 100])
+        self.env.p.addUserDebugText("Rotation: {}".format(round(rot,3)), [0.65,1,0.6], lifeTime=0.5, textColorRGB=[0, 222, 100])
         if self.last_rot_dist is None or self.last_owner != 2:
             self.last_rot_dist = rot
         rewardrot = self.last_rot_dist - rot
@@ -1574,7 +1574,7 @@ def rotate_compute(self, object, goal):
         #    self.env.episode_info = "Task finished in initial configuration"
         #    self.env.episode_over = True
         self.network_rewards[-1] += reward
-        self.env.p.addUserDebugText(f"Rewards:{self.network_rewards[-1]}", [0.59,1,0.6], lifeTime=0.5, textColorRGB=[0,0,125])
+        self.env.p.addUserDebugText(f"Rewards:{self.network_rewards[-1]}", [0.65,1,0.7], lifeTime=0.5, textColorRGB=[0,0,125])
         return reward
 
     def object_near_goal(self, object, goal):
@@ -1671,7 +1671,7 @@ def move_compute(self, object, pregoal):
         self.last_place_dist = dist
         ix = 1 if self.num_networks > 1 else 0
         self.network_rewards[ix] += reward
-        self.env.p.addUserDebugText(f"Rewards:{self.network_rewards[ix]}", [0.59,1,0.6], lifeTime=0.5, textColorRGB=[0,0,125])
+        self.env.p.addUserDebugText(f"Rewards:{self.network_rewards[ix]}", [0.65,1,0.7], lifeTime=0.5, textColorRGB=[0,0,125])
         return reward
 
 
@@ -1710,7 +1710,7 @@ def rotate_compute(self, object, goal):
         #    self.env.episode_info = "Task finished in initial configuration"
         #    self.env.episode_over = True
         self.network_rewards[2] += reward
-        self.env.p.addUserDebugText(f"Rewards:{self.network_rewards[-1]}", [0.59,1,0.6], lifeTime=0.5, textColorRGB=[0,0,125])
+        self.env.p.addUserDebugText(f"Rewards:{self.network_rewards[-1]}", [0.65,1,0.7], lifeTime=0.5, textColorRGB=[0,0,125])
         return reward
 
     def object_ready_swipe(self, object, goal):