unitree_rl_gym/legged_gym/scripts/play.py

143 lines
4.6 KiB
Python

import sys
from legged_gym import LEGGED_GYM_ROOT_DIR
import os
import sys
from legged_gym import LEGGED_GYM_ROOT_DIR
import isaacgym
from legged_gym.envs import *
from legged_gym.utils import get_args, export_policy_as_jit, task_registry, Logger
import numpy as np
import torch
def play(args):
env_cfg, train_cfg = task_registry.get_cfgs(name=args.task)
# override some parameters for testing
env_cfg.env.num_envs = min(env_cfg.env.num_envs, 100)
env_cfg.terrain.num_rows = 5
env_cfg.terrain.num_cols = 5
env_cfg.terrain.curriculum = False
env_cfg.noise.add_noise = False
env_cfg.domain_rand.randomize_friction = False
env_cfg.domain_rand.push_robots = False
env_cfg.env.test = True
# prepare environment
env, _ = task_registry.make_env(name=args.task, args=args, env_cfg=env_cfg)
obs = env.get_observations()
# load policy
train_cfg.runner.resume = True
ppo_runner, train_cfg = task_registry.make_alg_runner(env=env, name=args.task, args=args, train_cfg=train_cfg)
policy = ppo_runner.get_inference_policy(device=env.device)
# Define the joint orders for sim A and sim B
sim_a_joints = [
'left_hip_pitch_joint',
'right_hip_pitch_joint',
'waist_yaw_joint',
'left_hip_roll_joint',
'right_hip_roll_joint',
'waist_roll_joint',
'left_hip_yaw_joint',
'right_hip_yaw_joint',
'waist_pitch_joint',
'left_knee_joint',
'right_knee_joint',
'left_shoulder_pitch_joint',
'right_shoulder_pitch_joint',
'left_ankle_pitch_joint',
'right_ankle_pitch_joint',
'left_shoulder_roll_joint',
'right_shoulder_roll_joint',
'left_ankle_roll_joint',
'right_ankle_roll_joint',
'left_shoulder_yaw_joint',
'right_shoulder_yaw_joint',
'left_elbow_joint',
'right_elbow_joint',
'left_wrist_roll_joint',
'right_wrist_roll_joint',
'left_wrist_pitch_joint',
'right_wrist_pitch_joint',
'left_wrist_yaw_joint',
'right_wrist_yaw_joint'
]
sim_b_joints = [
'left_hip_pitch_joint',
'left_hip_roll_joint',
'left_hip_yaw_joint',
'left_knee_joint',
'left_ankle_pitch_joint',
'left_ankle_roll_joint',
'right_hip_pitch_joint',
'right_hip_roll_joint',
'right_hip_yaw_joint',
'right_knee_joint',
'right_ankle_pitch_joint',
'right_ankle_roll_joint',
'waist_yaw_joint',
'waist_roll_joint',
'waist_pitch_joint',
'left_shoulder_pitch_joint',
'left_shoulder_roll_joint',
'left_shoulder_yaw_joint',
'left_elbow_joint',
'left_wrist_roll_joint',
'left_wrist_pitch_joint',
'left_wrist_yaw_joint',
'right_shoulder_pitch_joint',
'right_shoulder_roll_joint',
'right_shoulder_yaw_joint',
'right_elbow_joint',
'right_wrist_roll_joint',
'right_wrist_pitch_joint',
'right_wrist_yaw_joint'
]
# Create a mapping tensor
mapping_tensor = torch.zeros((len(sim_b_joints), len(sim_a_joints)), device=env.device)
# Fill the mapping tensor
for b_idx, b_joint in enumerate(sim_b_joints):
if b_joint in sim_a_joints:
a_idx = sim_a_joints.index(b_joint)
# mapping_tensor[b_idx, a_idx] = 1.0
mapping_tensor[a_idx, b_idx] = 1.0
# export policy as a jit module (used to run it from C++)
if EXPORT_POLICY:
path = os.path.join(LEGGED_GYM_ROOT_DIR, 'logs', train_cfg.runner.experiment_name, 'exported', 'policies')
export_policy_as_jit(ppo_runner.alg.actor_critic, path)
print('Exported policy as jit script to: ', path)
for i in range(10*int(env.max_episode_length)):
obs[..., 9:38] = obs[..., 9:38] @ mapping_tensor.transpose(0, 1)
obs[..., 38:67] = obs[..., 38:67] @ mapping_tensor.transpose(0, 1)
obs[..., 67:96] = obs[..., 67:96] @ mapping_tensor.transpose(0, 1)
# from icecream import ic
# ic(
# obs[..., :9],
# obs[..., 9:38],
# obs[..., 38:67],
# obs[..., 67:96],
# )
actions = policy(obs.detach())
# ic(
# actions
# )
reordered_actions = actions @ mapping_tensor
# obs, _, rews, dones, infos = env.step(actions.detach())
obs, _, rews, dones, infos = env.step(reordered_actions.detach())
if __name__ == '__main__':
EXPORT_POLICY = True
# EXPORT_POLICY = False
RECORD_FRAMES = False
MOVE_CAMERA = False
args = get_args()
play(args)