forked from tangger/lerobot
Add tasks without end_effector that are compatible with dataset, Eval can run (TODO: training and pretrained model)
This commit is contained in:
Cadene
@@ -1,4 +1,3 @@
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import collections
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import importlib
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import logging
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from collections import deque
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@@ -9,7 +8,6 @@ import numpy as np
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import torch
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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 tensordict import TensorDict
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from torchrl.data.tensor_specs import (
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BoundedTensorSpec,
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@@ -19,293 +17,24 @@ from torchrl.data.tensor_specs import (
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)
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from torchrl.envs import EnvBase
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from lerobot.common.utils import set_seed
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from .constants import (
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from lerobot.common.envs.aloha.constants import (
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ACTIONS,
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ASSETS_DIR,
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DT,
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JOINTS,
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PUPPET_GRIPPER_POSITION_CLOSE,
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START_ARM_POSE,
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normalize_puppet_gripper_position,
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normalize_puppet_gripper_velocity,
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unnormalize_puppet_gripper_position,
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)
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from lerobot.common.envs.aloha.tasks.sim import BOX_POSE, InsertionTask, TransferCubeTask
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from lerobot.common.envs.aloha.tasks.sim_end_effector import (
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InsertionEndEffectorTask,
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TransferCubeEndEffectorTask,
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)
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from lerobot.common.utils import set_seed
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from .utils import sample_box_pose, sample_insertion_pose
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_has_gym = importlib.util.find_spec("gym") is not None
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# def make_ee_sim_env(task_name):
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# """
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# Environment for simulated robot bi-manual manipulation, with end-effector control.
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# Action space: [left_arm_pose (7), # position and quaternion for end effector
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# left_gripper_positions (1), # normalized gripper position (0: close, 1: open)
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# right_arm_pose (7), # position and quaternion for end effector
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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 = ASSETS_DIR / "bimanual_viperx_ee_transfer_cube.xml"
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# physics = mujoco.Physics.from_xml_path(xml_path)
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# task = TransferCubeEETask(random=False)
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# env = control.Environment(
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# physics, task, time_limit=20, control_timestep=DT, n_sub_steps=None, flat_observation=False
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# )
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# elif "sim_insertion" in task_name:
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# xml_path = ASSETS_DIR / "bimanual_viperx_ee_insertion.xml"
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# physics = mujoco.Physics.from_xml_path(xml_path)
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# task = InsertionEETask(random=False)
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# env = control.Environment(
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# physics, task, time_limit=20, control_timestep=DT, n_sub_steps=None, flat_observation=False
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# )
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# else:
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# raise NotImplementedError
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# return env
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class BimanualViperXEETask(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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a_len = len(action) // 2
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action_left = action[:a_len]
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action_right = action[a_len:]
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# set mocap position and quat
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# left
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np.copyto(physics.data.mocap_pos[0], action_left[:3])
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np.copyto(physics.data.mocap_quat[0], action_left[3:7])
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# right
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np.copyto(physics.data.mocap_pos[1], action_right[:3])
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np.copyto(physics.data.mocap_quat[1], action_right[3:7])
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# set gripper
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g_left_ctrl = unnormalize_puppet_gripper_position(action_left[7])
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g_right_ctrl = unnormalize_puppet_gripper_position(action_right[7])
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np.copyto(physics.data.ctrl, np.array([g_left_ctrl, -g_left_ctrl, g_right_ctrl, -g_right_ctrl]))
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def initialize_robots(self, physics):
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# reset joint position
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physics.named.data.qpos[:16] = START_ARM_POSE
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# reset mocap to align with end effector
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# to obtain these numbers:
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# (1) make an ee_sim env and reset to the same start_pose
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# (2) get env._physics.named.data.xpos['vx300s_left/gripper_link']
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# get env._physics.named.data.xquat['vx300s_left/gripper_link']
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# repeat the same for right side
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np.copyto(physics.data.mocap_pos[0], [-0.31718881, 0.5, 0.29525084])
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np.copyto(physics.data.mocap_quat[0], [1, 0, 0, 0])
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# right
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np.copyto(physics.data.mocap_pos[1], np.array([0.31718881, 0.49999888, 0.29525084]))
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np.copyto(physics.data.mocap_quat[1], [1, 0, 0, 0])
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# reset gripper control
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close_gripper_control = np.array(
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[
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PUPPET_GRIPPER_POSITION_CLOSE,
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-PUPPET_GRIPPER_POSITION_CLOSE,
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PUPPET_GRIPPER_POSITION_CLOSE,
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-PUPPET_GRIPPER_POSITION_CLOSE,
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]
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)
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np.copyto(physics.data.ctrl, close_gripper_control)
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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 = [normalize_puppet_gripper_position(left_qpos_raw[6])]
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right_gripper_qpos = [normalize_puppet_gripper_position(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 = [normalize_puppet_gripper_velocity(left_qvel_raw[6])]
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right_gripper_qvel = [normalize_puppet_gripper_velocity(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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# note: it is important to do .copy()
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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"] = {}
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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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# used in scripted policy to obtain starting pose
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obs["mocap_pose_left"] = np.concatenate(
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[physics.data.mocap_pos[0], physics.data.mocap_quat[0]]
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).copy()
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obs["mocap_pose_right"] = np.concatenate(
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[physics.data.mocap_pos[1], physics.data.mocap_quat[1]]
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).copy()
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# used when replaying joint trajectory
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obs["gripper_ctrl"] = physics.data.ctrl.copy()
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return obs
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def get_reward(self, physics):
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raise NotImplementedError
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class TransferCubeEETask(BimanualViperXEETask):
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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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self.initialize_robots(physics)
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# randomize box position
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cube_pose = sample_box_pose()
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box_start_idx = physics.model.name2id("red_box_joint", "joint")
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np.copyto(physics.data.qpos[box_start_idx : box_start_idx + 7], cube_pose)
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# print(f"randomized cube position to {cube_position}")
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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 InsertionEETask(BimanualViperXEETask):
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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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self.initialize_robots(physics)
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# randomize peg and socket position
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peg_pose, socket_pose = sample_insertion_pose()
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def id2index(j_id):
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return 16 + (j_id - 16) * 7 # first 16 is robot qpos, 7 is pose dim # hacky
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peg_start_id = physics.model.name2id("red_peg_joint", "joint")
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peg_start_idx = id2index(peg_start_id)
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np.copyto(physics.data.qpos[peg_start_idx : peg_start_idx + 7], peg_pose)
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# print(f"randomized cube position to {cube_position}")
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socket_start_id = physics.model.name2id("blue_socket_joint", "joint")
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socket_start_idx = id2index(socket_start_id)
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np.copyto(physics.data.qpos[socket_start_idx : socket_start_idx + 7], socket_pose)
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# print(f"randomized cube position to {cube_position}")
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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 = (
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("socket-1", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
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or ("socket-2", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
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or ("socket-3", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
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or ("socket-4", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
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)
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peg_touch_table = ("red_peg", "table") in all_contact_pairs
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socket_touch_table = (
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("socket-1", "table") in all_contact_pairs
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or ("socket-2", "table") in all_contact_pairs
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or ("socket-3", "table") in all_contact_pairs
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or ("socket-4", "table") in all_contact_pairs
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)
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peg_touch_socket = (
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("red_peg", "socket-1") in all_contact_pairs
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or ("red_peg", "socket-2") in all_contact_pairs
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or ("red_peg", "socket-3") in all_contact_pairs
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or ("red_peg", "socket-4") in all_contact_pairs
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)
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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 (
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touch_left_gripper and touch_right_gripper and (not peg_touch_table) and (not socket_touch_table)
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): # 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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class AlohaEnv(EnvBase):
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def __init__(
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self,
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@@ -320,6 +49,7 @@ class AlohaEnv(EnvBase):
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num_prev_action=0,
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):
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super().__init__(device=device, batch_size=[])
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self.task = task
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self.frame_skip = frame_skip
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self.from_pixels = from_pixels
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self.pixels_only = pixels_only
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@@ -338,27 +68,7 @@ class AlohaEnv(EnvBase):
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if not from_pixels:
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raise NotImplementedError()
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# time limit is controlled by StepCounter in factory
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time_limit = float("inf")
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if "sim_transfer_cube" in task:
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xml_path = ASSETS_DIR / "bimanual_viperx_ee_transfer_cube.xml"
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physics = mujoco.Physics.from_xml_path(str(xml_path))
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task = TransferCubeEETask(random=False)
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env = control.Environment(
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physics, task, time_limit, control_timestep=DT, n_sub_steps=None, flat_observation=False
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)
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elif "sim_insertion" in task:
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xml_path = ASSETS_DIR / "bimanual_viperx_ee_insertion.xml"
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physics = mujoco.Physics.from_xml_path(str(xml_path))
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task = InsertionEETask(random=False)
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env = control.Environment(
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physics, task, time_limit, control_timestep=DT, n_sub_steps=None, flat_observation=False
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)
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else:
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raise NotImplementedError
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self._env = env
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self._env = self._make_env_task(task)
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self._make_spec()
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self._current_seed = self.set_seed(seed)
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@@ -375,6 +85,36 @@ class AlohaEnv(EnvBase):
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image = self._env.physics.render(height=height, width=width, camera_id="top")
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return image
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def _make_env_task(self, task_name):
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# time limit is controlled by StepCounter in env factory
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time_limit = float("inf")
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if "sim_transfer_cube" in task_name:
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xml_path = ASSETS_DIR / "bimanual_viperx_transfer_cube.xml"
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physics = mujoco.Physics.from_xml_path(str(xml_path))
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task = TransferCubeTask(random=False)
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elif "sim_insertion" in task_name:
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xml_path = ASSETS_DIR / "bimanual_viperx_insertion.xml"
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physics = mujoco.Physics.from_xml_path(str(xml_path))
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task = InsertionTask(random=False)
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elif "sim_end_effector_transfer_cube" in task_name:
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raise NotImplementedError()
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xml_path = ASSETS_DIR / "bimanual_viperx_end_effector_transfer_cube.xml"
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physics = mujoco.Physics.from_xml_path(str(xml_path))
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task = TransferCubeEndEffectorTask(random=False)
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elif "sim_end_effector_insertion" in task_name:
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raise NotImplementedError()
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xml_path = ASSETS_DIR / "bimanual_viperx_end_effector_insertion.xml"
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physics = mujoco.Physics.from_xml_path(str(xml_path))
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task = InsertionEndEffectorTask(random=False)
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else:
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raise NotImplementedError(task_name)
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env = control.Environment(
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physics, task, time_limit, control_timestep=DT, n_sub_steps=None, flat_observation=False
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)
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return env
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def _format_raw_obs(self, raw_obs):
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if self.from_pixels:
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image = torch.from_numpy(raw_obs["images"]["top"].copy())
|
||||
@@ -396,6 +136,13 @@ class AlohaEnv(EnvBase):
|
||||
# we need to handle seed iteration, since self._env.reset() rely an internal _seed.
|
||||
self._current_seed += 1
|
||||
self.set_seed(self._current_seed)
|
||||
|
||||
# TODO(rcadene): do not use global variable for this
|
||||
if "sim_transfer_cube" in self.task:
|
||||
BOX_POSE[0] = sample_box_pose() # used in sim reset
|
||||
elif "sim_insertion" in self.task:
|
||||
BOX_POSE[0] = np.concatenate(sample_insertion_pose()) # used in sim reset
|
||||
|
||||
raw_obs = self._env.reset()
|
||||
# TODO(rcadene): add assert
|
||||
# assert self._current_seed == self._env._seed
|
||||
|
||||
Reference in New Issue
Block a user