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add realman shadow src
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yutang
2025-06-07 11:29:43 +08:00
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realman_src/realman_aloha/shadow_rm_act/scripted_policy.py Normal file
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import numpy as np
import matplotlib.pyplot as plt
from pyquaternion import Quaternion
from constants import SIM_TASK_CONFIGS
from ee_sim_env import make_ee_sim_env
import IPython
e = IPython.embed
class BasePolicy:
def __init__(self, inject_noise=False):
self.inject_noise = inject_noise
self.step_count = 0
self.left_trajectory = None
self.right_trajectory = None
def generate_trajectory(self, ts_first):
raise NotImplementedError
@staticmethod
def interpolate(curr_waypoint, next_waypoint, t):
t_frac = (t - curr_waypoint["t"]) / (next_waypoint["t"] - curr_waypoint["t"])
curr_xyz = curr_waypoint['xyz']
curr_quat = curr_waypoint['quat']
curr_grip = curr_waypoint['gripper']
next_xyz = next_waypoint['xyz']
next_quat = next_waypoint['quat']
next_grip = next_waypoint['gripper']
xyz = curr_xyz + (next_xyz - curr_xyz) * t_frac
quat = curr_quat + (next_quat - curr_quat) * t_frac
gripper = curr_grip + (next_grip - curr_grip) * t_frac
return xyz, quat, gripper
def __call__(self, ts):
# generate trajectory at first timestep, then open-loop execution
if self.step_count == 0:
self.generate_trajectory(ts)
# obtain left and right waypoints
if self.left_trajectory[0]['t'] == self.step_count:
self.curr_left_waypoint = self.left_trajectory.pop(0)
next_left_waypoint = self.left_trajectory[0]
if self.right_trajectory[0]['t'] == self.step_count:
self.curr_right_waypoint = self.right_trajectory.pop(0)
next_right_waypoint = self.right_trajectory[0]
# interpolate between waypoints to obtain current pose and gripper command
left_xyz, left_quat, left_gripper = self.interpolate(self.curr_left_waypoint, next_left_waypoint, self.step_count)
right_xyz, right_quat, right_gripper = self.interpolate(self.curr_right_waypoint, next_right_waypoint, self.step_count)
# Inject noise
if self.inject_noise:
scale = 0.01
left_xyz = left_xyz + np.random.uniform(-scale, scale, left_xyz.shape)
right_xyz = right_xyz + np.random.uniform(-scale, scale, right_xyz.shape)
action_left = np.concatenate([left_xyz, left_quat, [left_gripper]])
action_right = np.concatenate([right_xyz, right_quat, [right_gripper]])
self.step_count += 1
return np.concatenate([action_left, action_right])
class PickAndTransferPolicy(BasePolicy):
def generate_trajectory(self, ts_first):
init_mocap_pose_right = ts_first.observation['mocap_pose_right']
init_mocap_pose_left = ts_first.observation['mocap_pose_left']
box_info = np.array(ts_first.observation['env_state'])
box_xyz = box_info[:3]
box_quat = box_info[3:]
# print(f"Generate trajectory for {box_xyz=}")
gripper_pick_quat = Quaternion(init_mocap_pose_right[3:])
gripper_pick_quat = gripper_pick_quat * Quaternion(axis=[0.0, 1.0, 0.0], degrees=-60)
meet_left_quat = Quaternion(axis=[1.0, 0.0, 0.0], degrees=90)
meet_xyz = np.array([0, 0.5, 0.25])
self.left_trajectory = [
{"t": 0, "xyz": init_mocap_pose_left[:3], "quat": init_mocap_pose_left[3:], "gripper": 0}, # sleep
{"t": 100, "xyz": meet_xyz + np.array([-0.1, 0, -0.02]), "quat": meet_left_quat.elements, "gripper": 1}, # approach meet position
{"t": 260, "xyz": meet_xyz + np.array([0.02, 0, -0.02]), "quat": meet_left_quat.elements, "gripper": 1}, # move to meet position
{"t": 310, "xyz": meet_xyz + np.array([0.02, 0, -0.02]), "quat": meet_left_quat.elements, "gripper": 0}, # close gripper
{"t": 360, "xyz": meet_xyz + np.array([-0.1, 0, -0.02]), "quat": np.array([1, 0, 0, 0]), "gripper": 0}, # move left
{"t": 400, "xyz": meet_xyz + np.array([-0.1, 0, -0.02]), "quat": np.array([1, 0, 0, 0]), "gripper": 0}, # stay
]
self.right_trajectory = [
{"t": 0, "xyz": init_mocap_pose_right[:3], "quat": init_mocap_pose_right[3:], "gripper": 0}, # sleep
{"t": 90, "xyz": box_xyz + np.array([0, 0, 0.08]), "quat": gripper_pick_quat.elements, "gripper": 1}, # approach the cube
{"t": 130, "xyz": box_xyz + np.array([0, 0, -0.015]), "quat": gripper_pick_quat.elements, "gripper": 1}, # go down
{"t": 170, "xyz": box_xyz + np.array([0, 0, -0.015]), "quat": gripper_pick_quat.elements, "gripper": 0}, # close gripper
{"t": 200, "xyz": meet_xyz + np.array([0.05, 0, 0]), "quat": gripper_pick_quat.elements, "gripper": 0}, # approach meet position
{"t": 220, "xyz": meet_xyz, "quat": gripper_pick_quat.elements, "gripper": 0}, # move to meet position
{"t": 310, "xyz": meet_xyz, "quat": gripper_pick_quat.elements, "gripper": 1}, # open gripper
{"t": 360, "xyz": meet_xyz + np.array([0.1, 0, 0]), "quat": gripper_pick_quat.elements, "gripper": 1}, # move to right
{"t": 400, "xyz": meet_xyz + np.array([0.1, 0, 0]), "quat": gripper_pick_quat.elements, "gripper": 1}, # stay
]
class InsertionPolicy(BasePolicy):
def generate_trajectory(self, ts_first):
init_mocap_pose_right = ts_first.observation['mocap_pose_right']
init_mocap_pose_left = ts_first.observation['mocap_pose_left']
peg_info = np.array(ts_first.observation['env_state'])[:7]
peg_xyz = peg_info[:3]
peg_quat = peg_info[3:]
socket_info = np.array(ts_first.observation['env_state'])[7:]
socket_xyz = socket_info[:3]
socket_quat = socket_info[3:]
gripper_pick_quat_right = Quaternion(init_mocap_pose_right[3:])
gripper_pick_quat_right = gripper_pick_quat_right * Quaternion(axis=[0.0, 1.0, 0.0], degrees=-60)
gripper_pick_quat_left = Quaternion(init_mocap_pose_right[3:])
gripper_pick_quat_left = gripper_pick_quat_left * Quaternion(axis=[0.0, 1.0, 0.0], degrees=60)
meet_xyz = np.array([0, 0.5, 0.15])
lift_right = 0.00715
self.left_trajectory = [
{"t": 0, "xyz": init_mocap_pose_left[:3], "quat": init_mocap_pose_left[3:], "gripper": 0}, # sleep
{"t": 120, "xyz": socket_xyz + np.array([0, 0, 0.08]), "quat": gripper_pick_quat_left.elements, "gripper": 1}, # approach the cube
{"t": 170, "xyz": socket_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_left.elements, "gripper": 1}, # go down
{"t": 220, "xyz": socket_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_left.elements, "gripper": 0}, # close gripper
{"t": 285, "xyz": meet_xyz + np.array([-0.1, 0, 0]), "quat": gripper_pick_quat_left.elements, "gripper": 0}, # approach meet position
{"t": 340, "xyz": meet_xyz + np.array([-0.05, 0, 0]), "quat": gripper_pick_quat_left.elements,"gripper": 0}, # insertion
{"t": 400, "xyz": meet_xyz + np.array([-0.05, 0, 0]), "quat": gripper_pick_quat_left.elements, "gripper": 0}, # insertion
]
self.right_trajectory = [
{"t": 0, "xyz": init_mocap_pose_right[:3], "quat": init_mocap_pose_right[3:], "gripper": 0}, # sleep
{"t": 120, "xyz": peg_xyz + np.array([0, 0, 0.08]), "quat": gripper_pick_quat_right.elements, "gripper": 1}, # approach the cube
{"t": 170, "xyz": peg_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_right.elements, "gripper": 1}, # go down
{"t": 220, "xyz": peg_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_right.elements, "gripper": 0}, # close gripper
{"t": 285, "xyz": meet_xyz + np.array([0.1, 0, lift_right]), "quat": gripper_pick_quat_right.elements, "gripper": 0}, # approach meet position
{"t": 340, "xyz": meet_xyz + np.array([0.05, 0, lift_right]), "quat": gripper_pick_quat_right.elements, "gripper": 0}, # insertion
{"t": 400, "xyz": meet_xyz + np.array([0.05, 0, lift_right]), "quat": gripper_pick_quat_right.elements, "gripper": 0}, # insertion
]
def test_policy(task_name):
# example rolling out pick_and_transfer policy
onscreen_render = True
inject_noise = False
# setup the environment
episode_len = SIM_TASK_CONFIGS[task_name]['episode_len']
if 'sim_transfer_cube' in task_name:
env = make_ee_sim_env('sim_transfer_cube')
elif 'sim_insertion' in task_name:
env = make_ee_sim_env('sim_insertion')
else:
raise NotImplementedError
for episode_idx in range(2):
ts = env.reset()
episode = [ts]
if onscreen_render:
ax = plt.subplot()
plt_img = ax.imshow(ts.observation['images']['angle'])
plt.ion()
policy = PickAndTransferPolicy(inject_noise)
for step in range(episode_len):
action = policy(ts)
ts = env.step(action)
episode.append(ts)
if onscreen_render:
plt_img.set_data(ts.observation['images']['angle'])
plt.pause(0.02)
plt.close()
episode_return = np.sum([ts.reward for ts in episode[1:]])
if episode_return > 0:
print(f"{episode_idx=} Successful, {episode_return=}")
else:
print(f"{episode_idx=} Failed")
if __name__ == '__main__':
test_task_name = 'sim_transfer_cube_scripted'
test_policy(test_task_name)