File size: 7,102 Bytes
1e71a55 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 | import os
import torch
class AlgSolution:
ACTION_SCALE = 0.5
EE_BODY_NAME_CANDIDATES = ("gripper_base", "piper_gripper_base")
ARM_JOINT_NAME_CANDIDATES = (
["joint1", "joint2", "joint3", "joint4", "joint5", "joint6"],
["arm_joint1", "arm_joint2", "arm_joint3", "arm_joint4", "arm_joint5", "arm_joint6"],
)
def __init__(self):
policy_path = os.path.dirname(os.path.abspath(__file__)) + '/policy.pt'
self.device = 'cuda'
self.policy = torch.jit.load(policy_path, map_location=self.device)
self.policy.eval()
self.leg_action_dim = 12
self.arm_action_dim = 8
self.leg_joint_indices = list(range(12))
self.arm_joint_indices = list(range(12, 20))
self.train_to_env_action_scale = torch.tensor(
[
0.25, 0.5, 0.5,
0.25, 0.5, 0.5,
0.25, 0.5, 0.5,
0.25, 0.5, 0.5,
],
device=self.device,
dtype=torch.float32,
).view(1, -1)
self.env_to_train_action_scale = torch.tensor(
[
4.0, 2.0, 2.0,
4.0, 2.0, 2.0,
4.0, 2.0, 2.0,
4.0, 2.0, 2.0,
],
device=self.device,
dtype=torch.float32,
).view(1, -1)
# Fixed zero base velocity command for policy input.
self.fixed_velocity_commands = torch.tensor(
[0.5, 0.0, 0.0],
device=self.device,
dtype=torch.float32,
).view(1, 3)
self.arm_default_action = torch.zeros(
(1, self.arm_action_dim),
device=self.device,
dtype=torch.float32,
)
def _resolve_joint_ids(self, candidates: tuple[list[str], ...]) -> list[int]:
last_error = None
for names in candidates:
try:
ids, found_names = self.robot.find_joints(names)
except ValueError as err:
last_error = err
continue
if len(ids) == len(names):
if candidates is self.ARM_JOINT_NAME_CANDIDATES:
self.arm_joint_names = list(found_names)
return list(ids)
raise ValueError(
f"Cannot resolve required joints from candidates: {candidates}. Last error: {last_error}"
)
def _resolve_ee_body_name(self) -> str:
last_error = None
for name in self.EE_BODY_NAME_CANDIDATES:
try:
body_ids, _ = self.robot.find_bodies(name)
except ValueError as err:
last_error = err
continue
if len(body_ids) == 1:
return name
raise ValueError(
f"Cannot resolve EE body from candidates: {self.EE_BODY_NAME_CANDIDATES}. Last error: {last_error}"
)
def _ensure_cartesian_targets(self):
self.cartesian_ctrl.reset()
def _compute_arm_overlay_action(self) -> torch.Tensor:
self._ensure_cartesian_targets()
arm_jpos_des = self.cartesian_ctrl.compute_base(
self.ee_pos_target_b,
self.ee_quat_target_b,
)
full_target = self.robot.data.joint_pos.clone()
full_target[:, self.arm_ids] = arm_jpos_des
full_target[:, self.gripper_ids] = self.gripper_open_pos.repeat(full_target.shape[0], 1)
return (full_target - self.default_joint_pos) / self.ACTION_SCALE
def _get_velocity_commands(self, proprio: torch.Tensor) -> torch.Tensor:
"""Return fixed velocity commands for policy input."""
num_envs = proprio.shape[0]
cmd = self.fixed_velocity_commands.to(dtype=proprio.dtype, device=self.device)
if num_envs > 1:
cmd = cmd.repeat(num_envs, 1)
return cmd
def _extract_policy_obs(self, obs, action_dim) -> torch.Tensor:
proprio = obs["proprio"].to(self.device)
expected_dim = 3 + 3 + 3 + 3 + action_dim + action_dim + action_dim
idx = 0
_base_lin_vel = proprio[:, idx:idx + 3]
idx += 3
base_ang_vel = proprio[:, idx:idx + 3]
idx += 3
_velocity_commands_env = proprio[:, idx:idx + 3]
idx += 3
projected_gravity = proprio[:, idx:idx + 3]
idx += 3
joint_pos_all = proprio[:, idx:idx + action_dim]
idx += action_dim
joint_vel_all = proprio[:, idx:idx + action_dim]
idx += action_dim
actions_all = proprio[:, idx:idx + action_dim]
joint_pos_leg = joint_pos_all[:, self.leg_joint_indices]
joint_vel_leg = joint_vel_all[:, self.leg_joint_indices]
actions_env_leg = actions_all[:, self.leg_joint_indices]
actions_train_leg = actions_env_leg * self.env_to_train_action_scale.to(dtype=proprio.dtype)
velocity_commands = self._get_velocity_commands(proprio)
policy_obs = torch.cat(
[
base_ang_vel * 0.25,
projected_gravity,
velocity_commands,
joint_pos_leg,
joint_vel_leg * 0.05,
actions_train_leg,
],
dim=-1,
)
return policy_obs
def _map_policy_action_to_env_action(self, action_train: torch.Tensor, action_dim: int) -> torch.Tensor:
"""Map training-time 12D leg action to current env 20D full-body action."""
if action_train.shape[-1] != self.leg_action_dim:
raise ValueError(
f"Policy output dim mismatch: got {action_train.shape[-1]}, expected {self.leg_action_dim}"
)
num_envs = action_train.shape[0]
leg_action_env = action_train * self.train_to_env_action_scale
action_env = torch.zeros(
(num_envs, action_dim),
device=self.device,
dtype=torch.float32,
)
action_env[:, self.leg_joint_indices] = leg_action_env
action_env[:, self.arm_joint_indices] = self.arm_default_action.repeat(num_envs, 1)
return action_env
def predicts(self, obs, current_score):
"""Run policy inference and return current-env full-body action."""
if current_score > 1:
return {'action': [], 'giveup': True}
proprio = obs["proprio"].to(self.device)
action_dim = (int(proprio.shape[-1]) - 12) // 3
policy_obs = self._extract_policy_obs(obs, action_dim)
with torch.inference_mode():
action_train = self.policy(policy_obs)
if not isinstance(action_train, torch.Tensor):
action_train = torch.as_tensor(
action_train, device=self.device, dtype=torch.float32
)
action_train = action_train.to(device=self.device, dtype=torch.float32)
if action_train.ndim == 1:
action_train = action_train.unsqueeze(0)
action_env = self._map_policy_action_to_env_action(action_train, action_dim)
action_env = action_env.cpu().numpy().tolist()
return {'action': action_env, 'giveup': False}
|