279 lines
12 KiB
Python
279 lines
12 KiB
Python
import numpy as np
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import os
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import collections
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import matplotlib.pyplot as plt
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from dm_control import mujoco
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from dm_control.rl import control
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from dm_control.suite import base
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from constants import DT, XML_DIR, START_ARM_POSE
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from constants import PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN
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from constants import MASTER_GRIPPER_POSITION_NORMALIZE_FN
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from constants import PUPPET_GRIPPER_POSITION_NORMALIZE_FN
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from constants import PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN
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import IPython
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e = IPython.embed
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BOX_POSE = [None] # to be changed from outside
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def make_sim_env(task_name):
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"""
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Environment for simulated robot bi-manual manipulation, with joint position control
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Action space: [left_arm_qpos (6), # absolute joint position
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left_gripper_positions (1), # normalized gripper position (0: close, 1: open)
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right_arm_qpos (6), # absolute joint position
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right_gripper_positions (1),] # normalized gripper position (0: close, 1: open)
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Observation space: {"qpos": Concat[ left_arm_qpos (6), # absolute joint position
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left_gripper_position (1), # normalized gripper position (0: close, 1: open)
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right_arm_qpos (6), # absolute joint position
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right_gripper_qpos (1)] # normalized gripper position (0: close, 1: open)
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"qvel": Concat[ left_arm_qvel (6), # absolute joint velocity (rad)
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left_gripper_velocity (1), # normalized gripper velocity (pos: opening, neg: closing)
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right_arm_qvel (6), # absolute joint velocity (rad)
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right_gripper_qvel (1)] # normalized gripper velocity (pos: opening, neg: closing)
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"images": {"main": (480x640x3)} # h, w, c, dtype='uint8'
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"""
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if 'sim_transfer_cube' in task_name:
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xml_path = os.path.join(XML_DIR, f'bimanual_viperx_transfer_cube.xml')
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physics = mujoco.Physics.from_xml_path(xml_path)
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task = TransferCubeTask(random=False)
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env = control.Environment(physics, task, time_limit=20, control_timestep=DT,
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n_sub_steps=None, flat_observation=False)
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elif 'sim_insertion' in task_name:
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xml_path = os.path.join(XML_DIR, f'bimanual_viperx_insertion.xml')
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physics = mujoco.Physics.from_xml_path(xml_path)
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task = InsertionTask(random=False)
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env = control.Environment(physics, task, time_limit=20, control_timestep=DT,
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n_sub_steps=None, flat_observation=False)
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else:
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raise NotImplementedError
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return env
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class BimanualViperXTask(base.Task):
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def __init__(self, random=None):
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super().__init__(random=random)
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def before_step(self, action, physics):
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left_arm_action = action[:6]
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right_arm_action = action[7:7+6]
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normalized_left_gripper_action = action[6]
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normalized_right_gripper_action = action[7+6]
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left_gripper_action = PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN(normalized_left_gripper_action)
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right_gripper_action = PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN(normalized_right_gripper_action)
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full_left_gripper_action = [left_gripper_action, -left_gripper_action]
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full_right_gripper_action = [right_gripper_action, -right_gripper_action]
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env_action = np.concatenate([left_arm_action, full_left_gripper_action, right_arm_action, full_right_gripper_action])
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super().before_step(env_action, physics)
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return
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def initialize_episode(self, physics):
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"""Sets the state of the environment at the start of each episode."""
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super().initialize_episode(physics)
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@staticmethod
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def get_qpos(physics):
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qpos_raw = physics.data.qpos.copy()
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left_qpos_raw = qpos_raw[:8]
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right_qpos_raw = qpos_raw[8:16]
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left_arm_qpos = left_qpos_raw[:6]
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right_arm_qpos = right_qpos_raw[:6]
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left_gripper_qpos = [PUPPET_GRIPPER_POSITION_NORMALIZE_FN(left_qpos_raw[6])]
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right_gripper_qpos = [PUPPET_GRIPPER_POSITION_NORMALIZE_FN(right_qpos_raw[6])]
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return np.concatenate([left_arm_qpos, left_gripper_qpos, right_arm_qpos, right_gripper_qpos])
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@staticmethod
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def get_qvel(physics):
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qvel_raw = physics.data.qvel.copy()
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left_qvel_raw = qvel_raw[:8]
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right_qvel_raw = qvel_raw[8:16]
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left_arm_qvel = left_qvel_raw[:6]
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right_arm_qvel = right_qvel_raw[:6]
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left_gripper_qvel = [PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN(left_qvel_raw[6])]
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right_gripper_qvel = [PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN(right_qvel_raw[6])]
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return np.concatenate([left_arm_qvel, left_gripper_qvel, right_arm_qvel, right_gripper_qvel])
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@staticmethod
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def get_env_state(physics):
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raise NotImplementedError
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def get_observation(self, physics):
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obs = collections.OrderedDict()
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obs['qpos'] = self.get_qpos(physics)
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obs['qvel'] = self.get_qvel(physics)
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obs['env_state'] = self.get_env_state(physics)
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obs['images'] = dict()
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obs['images']['top'] = physics.render(height=480, width=640, camera_id='top')
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obs['images']['angle'] = physics.render(height=480, width=640, camera_id='angle')
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obs['images']['vis'] = physics.render(height=480, width=640, camera_id='front_close')
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return obs
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def get_reward(self, physics):
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# return whether left gripper is holding the box
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raise NotImplementedError
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class TransferCubeTask(BimanualViperXTask):
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def __init__(self, random=None):
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super().__init__(random=random)
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self.max_reward = 4
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def initialize_episode(self, physics):
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"""Sets the state of the environment at the start of each episode."""
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# TODO Notice: this function does not randomize the env configuration. Instead, set BOX_POSE from outside
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# reset qpos, control and box position
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with physics.reset_context():
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physics.named.data.qpos[:16] = START_ARM_POSE
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np.copyto(physics.data.ctrl, START_ARM_POSE)
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assert BOX_POSE[0] is not None
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physics.named.data.qpos[-7:] = BOX_POSE[0]
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# print(f"{BOX_POSE=}")
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super().initialize_episode(physics)
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@staticmethod
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def get_env_state(physics):
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env_state = physics.data.qpos.copy()[16:]
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return env_state
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def get_reward(self, physics):
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# return whether left gripper is holding the box
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all_contact_pairs = []
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for i_contact in range(physics.data.ncon):
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id_geom_1 = physics.data.contact[i_contact].geom1
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id_geom_2 = physics.data.contact[i_contact].geom2
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name_geom_1 = physics.model.id2name(id_geom_1, 'geom')
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name_geom_2 = physics.model.id2name(id_geom_2, 'geom')
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contact_pair = (name_geom_1, name_geom_2)
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all_contact_pairs.append(contact_pair)
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touch_left_gripper = ("red_box", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
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touch_right_gripper = ("red_box", "vx300s_right/10_right_gripper_finger") in all_contact_pairs
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touch_table = ("red_box", "table") in all_contact_pairs
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reward = 0
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if touch_right_gripper:
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reward = 1
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if touch_right_gripper and not touch_table: # lifted
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reward = 2
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if touch_left_gripper: # attempted transfer
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reward = 3
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if touch_left_gripper and not touch_table: # successful transfer
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reward = 4
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return reward
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class InsertionTask(BimanualViperXTask):
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def __init__(self, random=None):
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super().__init__(random=random)
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self.max_reward = 4
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def initialize_episode(self, physics):
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"""Sets the state of the environment at the start of each episode."""
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# TODO Notice: this function does not randomize the env configuration. Instead, set BOX_POSE from outside
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# reset qpos, control and box position
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with physics.reset_context():
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physics.named.data.qpos[:16] = START_ARM_POSE
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np.copyto(physics.data.ctrl, START_ARM_POSE)
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assert BOX_POSE[0] is not None
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physics.named.data.qpos[-7*2:] = BOX_POSE[0] # two objects
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# print(f"{BOX_POSE=}")
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super().initialize_episode(physics)
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@staticmethod
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def get_env_state(physics):
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env_state = physics.data.qpos.copy()[16:]
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return env_state
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def get_reward(self, physics):
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# return whether peg touches the pin
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all_contact_pairs = []
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for i_contact in range(physics.data.ncon):
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id_geom_1 = physics.data.contact[i_contact].geom1
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id_geom_2 = physics.data.contact[i_contact].geom2
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name_geom_1 = physics.model.id2name(id_geom_1, 'geom')
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name_geom_2 = physics.model.id2name(id_geom_2, 'geom')
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contact_pair = (name_geom_1, name_geom_2)
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all_contact_pairs.append(contact_pair)
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touch_right_gripper = ("red_peg", "vx300s_right/10_right_gripper_finger") in all_contact_pairs
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touch_left_gripper = ("socket-1", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \
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("socket-2", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \
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("socket-3", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \
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("socket-4", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
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peg_touch_table = ("red_peg", "table") in all_contact_pairs
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socket_touch_table = ("socket-1", "table") in all_contact_pairs or \
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("socket-2", "table") in all_contact_pairs or \
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("socket-3", "table") in all_contact_pairs or \
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("socket-4", "table") in all_contact_pairs
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peg_touch_socket = ("red_peg", "socket-1") in all_contact_pairs or \
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("red_peg", "socket-2") in all_contact_pairs or \
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("red_peg", "socket-3") in all_contact_pairs or \
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("red_peg", "socket-4") in all_contact_pairs
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pin_touched = ("red_peg", "pin") in all_contact_pairs
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reward = 0
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if touch_left_gripper and touch_right_gripper: # touch both
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reward = 1
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if touch_left_gripper and touch_right_gripper and (not peg_touch_table) and (not socket_touch_table): # grasp both
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reward = 2
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if peg_touch_socket and (not peg_touch_table) and (not socket_touch_table): # peg and socket touching
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reward = 3
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if pin_touched: # successful insertion
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reward = 4
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return reward
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def get_action(master_bot_left, master_bot_right):
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action = np.zeros(14)
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# arm action
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action[:6] = master_bot_left.dxl.joint_states.position[:6]
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action[7:7+6] = master_bot_right.dxl.joint_states.position[:6]
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# gripper action
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left_gripper_pos = master_bot_left.dxl.joint_states.position[7]
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right_gripper_pos = master_bot_right.dxl.joint_states.position[7]
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normalized_left_pos = MASTER_GRIPPER_POSITION_NORMALIZE_FN(left_gripper_pos)
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normalized_right_pos = MASTER_GRIPPER_POSITION_NORMALIZE_FN(right_gripper_pos)
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action[6] = normalized_left_pos
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action[7+6] = normalized_right_pos
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return action
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def test_sim_teleop():
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""" Testing teleoperation in sim with ALOHA. Requires hardware and ALOHA repo to work. """
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from interbotix_xs_modules.arm import InterbotixManipulatorXS
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BOX_POSE[0] = [0.2, 0.5, 0.05, 1, 0, 0, 0]
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# source of data
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master_bot_left = InterbotixManipulatorXS(robot_model="wx250s", group_name="arm", gripper_name="gripper",
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robot_name=f'master_left', init_node=True)
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master_bot_right = InterbotixManipulatorXS(robot_model="wx250s", group_name="arm", gripper_name="gripper",
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robot_name=f'master_right', init_node=False)
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# setup the environment
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env = make_sim_env('sim_transfer_cube')
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ts = env.reset()
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episode = [ts]
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# setup plotting
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ax = plt.subplot()
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plt_img = ax.imshow(ts.observation['images']['angle'])
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plt.ion()
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for t in range(1000):
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action = get_action(master_bot_left, master_bot_right)
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ts = env.step(action)
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episode.append(ts)
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plt_img.set_data(ts.observation['images']['angle'])
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plt.pause(0.02)
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if __name__ == '__main__':
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test_sim_teleop()
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