RoboGen Decompose and Generate Reward or Primitive
このGPTは、シミュレータ内で家庭用の物体操作タスクを学ぶためのロボットアームをサポートするよう設計されています。その主な役割は、与えられたタスクを実行可能なサブステップに分解し、それぞれのステップに対してプリミティブなアクションを呼び出すか、ロボットが学習するための報酬関数を設計することです。
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- Name: AKIRA KINOSE (View More GPTs By This Builder)
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- Another example: Task Name: Fetch item from refrigerator Description: The robotic arm will open a refrigerator door reach inside to grab an item, place it on the table, and then close the door Initial config: ```yaml - use_table: true - center: (1.2, 0, 0) lang: a common two-door refrigerator name: Refrigerator on_table: false path: refrigerator.urdf size: 1.8 type: urdf - center: (1.2, 0, 0.5) lang: a can of soda name: Item on_table: false path: soda_can.obj size: 0.2 type: mesh ``` ```Refrigerator articulation tree links: base link_0 link_1 link_2 joints: joint_name: joint_0 joint_type: fixed parent_link: base child_link: link_0 joint_name: joint_1 joint_type: revolute parent_link: link_0 child_link: link_1 joint_name: joint_2 joint_type: revolute parent_link: link_0 child_link: link_2 ``` ```Refrigerator semantics link_0 heavy refrigerator_body link_1 hinge door link_2 hinge door ``` Links: link_1: This link is one of the refrigerator doors, which the robot neesd to reach for the item inside. Joints: joint_1: This joint connects link_1, representing one of the doors. The robot needs to actuate this joint to open the door, reach for the item, and close the door. This task can be decomposed as follows: substep 1: grasp the refrigerator door ```primitive rgbs, final_state = grasp_object_link(self, "Refrigerator", "link_1") success = check_grasped(self, "Refrigerator", "link_1") ``` substep 2: open the refrigerator door ```reward def _compute_reward(self): # this reward encourages the end-effector to stay near door to grasp it. eef_pos = get_eef_pos(self)[0] door_pos = get_link_state(self, "Refrigerator", "link_1") reward_near = -np.linalg.norm(eef_pos - door_pos) # Get the joint state of the door. We know from the semantics and the articulation tree that joint_1 connects link_1 and is the joint that controls the rotation of the door. joint_angle = get_joint_state(self, "Refrigerator", "joint_1") # The reward is the negative distance between the current joint angle and the joint angle when the door is fully open (upper limit). joint_limit_low, joint_limit_high = get_joint_limit(self, "Refrigerator", "joint_1") diff = np.abs(joint_angle - joint_limit_high) reward_joint = -diff reward = reward_near + 5 * reward_joint success = diff < 0.35 * (joint_limit_high - joint_limit_low) # for opening, we think 65 percent is enough return reward, success ``` ```action space delta-translation ``` In the last substep the robot already grasps the door, thus only local movements are needed to open it. substep 3: grasp the item ```primitive rgbs, final_state = grasp_object(self, "Item") success = check_grasped(self, "Item") ``` substep 4: move the item out of the refrigerator ```reward def _compute_reward(self): # Get the current item position item_pos = get_position(self, "Item") # The first reward encourages the end-effector to stay near the item eef_pos = get_eef_pos(self)[0] reward_near = -np.linalg.norm(eef_pos - item_pos) # The reward is to encourage the robot to grasp the item and move the item to be on the table. # The goal is not to just move the soda can to be at a random location out of the refrigerator. Instead, we need to place it somewhere on the table. # This is important for moving an object out of a container style of task. table_bbox_low, table_bbox_high = get_bounding_box(self, "init_table") # the table is referred to as "init_table" in the simulator. table_bbox_range = table_bbox_high - table_bbox_low # target location is to put the item at a random location on the table target_location = np.zeros(3) target_location[0] = table_bbox_low[0] + 0.2 * table_bbox_range[0] # 0.2 is a random chosen number, any number in [0, 1] should work target_location[1] = table_bbox_low[1] + 0.3 * table_bbox_range[1] # 0.3 is a random chosen number, any number in [0, 1] should work target_location[2] = table_bbox_high[2] + 0.05 # target height is slightly above the table diff = np.linalg.norm(item_pos - target_location) reward_distance = -diff reward = reward_near + 5 * reward_distance success = diff < 0.06 return reward, success ``` ```action space normalized-direct-translation ``` Since this substep requires moving the item to a target location, we use the normalized-direct-translation. substep 5: grasp the refrigerator door again ```primitive rgbs, final_state = grasp_object_link(self, "Refrigerator", "link_1") success = check_grasped(self, "Refrigerator", "link_1") ``` substep 6: close the refrigerator door ```reward def _compute_reward(self): # this reward encourages the end-effector to stay near door eef_pos = get_eef_pos(self)[0] door_pos = get_link_state(self, "Refrigerator", "link_1") reward_near = -np.linalg.norm(eef_pos - door_pos) # Get the joint state of the door. joint_angle = get_joint_state(self, "Refrigerator", "joint_1") # The reward encourages the robot to make joint angle of the door to be the lower limit to clost it. joint_limit_low, joint_limit_high = get_joint_limit(self, "Refrigerator", "joint_1") diff = np.abs(joint_limit_low - joint_angle) reward_joint = -diff reward = reward_near + 5 * reward_joint success = diff < 0.1 * (joint_limit_high - joint_limit_low) # for closing, we think 10 percent is enough return reward, success ``` ```action space delta-translation ``` I will provide more examples in the following messages. Please reply yes if you understand the goal.
- Here is another example: Task Name: Put a toy car inside a box Description: The robotic arm will open a box, grasp the toy car and put it inside the box. Initial config: ```yaml - use_table: True - center: (0.2, 0.3, 0) on_table: True lang: a box name: box size: 0.25 type: urdf - center: (0.1, 0.6, 0) on_table: True lang: a toy car name: toy_car size: 0.1 type: mesh ``` ```box articulation tree links: base link_0 link_1 link_2 joints: joint_name: joint_0 joint_type: revolute parent_link: link_2 child_link: link_0 joint_name: joint_1 joint_type: revolute parent_link: link_2 child_link: link_1 joint_name: joint_2 joint_type: fixed parent_link: base child_link: link_2 ``` ```box semantics link_0 hinge rotation_lid link_1 hinge rotation_lid link_2 free box_body ``` Links: link_0: To fully open the box, the robot needs to open both box lids. We know from the semantics that link_0 is one of the lids. link_1: To fully open the box, the robot needs to open both box lids. We know from the semantics that link_1 is another lid. Joints: joint_0: from the articulation tree, joint_0 connects link_0 and is a hinge joint. Thus, the robot needs to actuate joint_0 to open link_0, which is the lid of the box. joint_1: from the articulation tree, joint_1 connects link_1 and is a hinge joint. Thus, the robot needs to actuate joint_1 to open link_1, which is the lid of the box. This task can be decomposed as follows: substep 1: grasp the first lid of the box ```primitive # The semantics shows that link_0 and link_1 are the lid links. rgbs, final_state = grasp_object_link(self, "box", "link_0") success = check_grasped(self, "box", "link_0") ``` substep 2: open the first lid of the box ```reward def _compute_reward(self): # This reward encourages the end-effector to stay near the lid to grasp it. eef_pos = get_eef_pos(self)[0] lid_pos = get_link_state(self, "box", "link_0") reward_near = -np.linalg.norm(eef_pos - lid_pos) # Get the joint state of the first lid. The semantics and the articulation tree show that joint_0 connects link_0 and is the joint that controls the rotation of the first lid link_0. joint_angle = get_joint_state(self, "box", "joint_0") # The reward is the negative distance between the current joint angle and the joint angle when the lid is fully open (upper limit). joint_limit_low, joint_limit_high = get_joint_limit(self, "box", "joint_0") diff = np.abs(joint_angle - joint_limit_high) reward_joint = -diff reward = reward_near + 5 * reward_joint success = diff < 0.35 * (joint_limit_high - joint_limit_low) return reward, success ``` ```action space delta-translation ``` substep 3: grasp the second lid of the box ```primitive # We know from the semantics that link_0 and link_1 are the lid links. rgbs, final_state = grasp_object_link(self, "box", "link_1") success = check_grasped(self, "box", "link_1") ``` substep 4: open the second lid of the box ```reward def _compute_reward(self): # This reward encourages the end-effector to stay near the lid to grasp it. eef_pos = get_eef_pos(self)[0] lid_pos = get_link_state(self, "box", "link_1") reward_near = -np.linalg.norm(eef_pos - lid_pos) # Get the joint state of the second lid. joint_angle = get_joint_state(self, "box", "joint_1") # The reward is the negative distance between the current joint angle and the joint angle when the lid is fully open (upper limit). joint_limit_low, joint_limit_high = get_joint_limit(self, "box", "joint_1") diff = np.abs(joint_angle - joint_limit_high) reward_joint = -diff reward = reward_near + 5 * reward_joint success = diff < 0.35 * (joint_limit_high - joint_limit_low) return reward, success ``` ```action space delta-translation ``` substep 5: grasp the toy car ```primitive rgbs, final_state = grasp_object(self, "toy_car") success = check_grasped(self, "toy_car") ``` substep 6: put the toy car into the box ```reward def _compute_reward(self): # This reward encourages the end-effector to stay near the car to grasp it. car_position = get_position(self, "toy_car") eef_pos = get_eef_pos(self)[0] reward_near = -np.linalg.norm(eef_pos - car_position) # main reward is 1 if the car is inside the box. From the semantics we know that link2 is the box body box_bbox_low, box_bbox_high = get_bounding_box_link(self, "box", "link_2") reward_in = int(in_bbox(self, car_position, box_bbox_low, box_bbox_high)) # another reward is to encourage the robot to move the car to be near the box reward_reaching = - np.linalg.norm(car_position - (box_bbox_low + box_bbox_high) / 2) # The task is considered to be successful if the car is inside the box bounding box success = reward_in # We give more weight to reward_in, which is the major goal of the task. reward = 5 * reward_in + reward_reaching + reward_near return reward, success ``` ```action space normalized-direct-translation ``` Since this substep requires moving the item to a target location, we use the normalized-direct-translation. Please decompose the following task into substeps. For each substep, write a primitive/a reward function, write the success checking function, and the action space if the reward is used. The primitives you can call: grasp_object(self, object_name): the robot arm will grasp the object specified by the argument object name. grasp_object_link(self, object_name, link_name): some object like an articulated object is composed of multiple links. The robot will grasp a link with link_name on the object with object_name. release_grasp(self): the robot will release the grasped object. Note that all primitives will return a tuple (rgbs, final_state) which represents the rgb images of the execution process and the final state of the execution process. You should always call the primitive in the following format: rgbs, final_state = some_primitive_function(self, arg1, ..., argn) The APIs you can use for writing the reward function/success checking function: get_position(self, object_name): get the position of center of mass of object with object_name. get_orientation(self, object_name): get the orientation of an object with object_name. get_joint_state(self, object_name, joint_name): get the joint angle value of a joint in an object. get_joint_limit(self, object_name, joint_name): get the lower and upper joint angle limit of a joint in an object, returned as a 2-element tuple. get_link_state(self, object_name, link_name): get the position of the center of mass of the link of an object. get_eef_pos(self): returns the position, orientation of the robot end-effector as a list. get_bounding_box(self, object_name): get the axis-aligned bounding box of an object. It returns the min and max xyz coordinate of the bounding box. get_bounding_box_link(self, object_name, link_name): get the axis-aligned bounding box of the link of an object. It returns the min and max xyz coordinate of the bounding box. in_bbox(self, pos, bbox_min, bbox_max): check if pos is within the bounding box with the lowest corner at bbox_min and the highest corner at bbox_max. check_grasped(self, object_name, link_name): return true if an object or a link of the object is grasped. link_name can be none, in which case it will check whether the object is grasped. get_initial_pos_orient(self, obj): get the initial position and orientation of an object at the beginning of the task. get_initial_joint_angle(self, obj_name, joint_name): get the initial joint angle of an object at the beginning of the task. The action space you can use for learning with the reward: delta-translation is better suited for small movements, and normalized-direct-translation is better suited for directly specifying the target location of the robot end-effector. You can assume that for objects, the lower joint limit corresponds to their natural state, e.g., a box is closed with the lid joint being 0, and a lever is unpushed when the joint angle is 0.
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