File size: 8,346 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 | """ATEC Task E pi0.5 native-8D websocket policy bridge."""
from __future__ import annotations
from collections import deque
import os
import sys
from pathlib import Path
import numpy as np
import torch
import torchvision.transforms.functional as TF
_OPENPI_REPO = Path(os.environ.get("OPENPI_REPO", "/home/ubuntu/src/openpi-ebench-clean"))
_OPENPI_CLIENT = _OPENPI_REPO / "packages" / "openpi-client" / "src"
if str(_OPENPI_CLIENT) not in sys.path:
sys.path.insert(0, str(_OPENPI_CLIENT))
from openpi_client import websocket_client_policy # noqa: E402
class AlgSolution:
_QPOS_SLICE = slice(0, 8)
_DEFAULT_PROMPT = "identify all objects, pick them up, and place them into the target basket"
def __init__(self):
self.device = "cuda" if torch.cuda.is_available() else "cpu"
host = os.environ.get("ATEC_PI05_HOST", "127.0.0.1")
port = int(os.environ.get("ATEC_PI05_PORT", "8000"))
self.policy = websocket_client_policy.WebsocketClientPolicy(host=host, port=port)
self.prompt = os.environ.get("ATEC_PI05_PROMPT", self._DEFAULT_PROMPT)
self.default_joint_pos = torch.tensor(
[[0.0, 1.2, -1.5, 0.0, 1.2, 0.0, 0.035, -0.035]],
dtype=torch.float32,
device=self.device,
)
self.teleop_home_joint_pos = torch.tensor(
[[-0.000033, 0.924525, -1.514983, 0.000011, 1.219900, -0.000033, 0.035000, -0.035000]],
dtype=torch.float32,
device=self.device,
)
self._home_action = torch.clamp(
(self.teleop_home_joint_pos - self.default_joint_pos) / 0.5,
-1.0,
1.0,
)
self._startup_zero_steps = int(os.environ.get("ATEC_PI05_STARTUP_ZERO_STEPS", "25"))
self._home_qpos_tolerance = float(os.environ.get("ATEC_PI05_HOME_QPOS_TOLERANCE", "0.10"))
self._home_hold_steps = int(os.environ.get("ATEC_PI05_HOME_HOLD_STEPS", "5"))
self._action_repeat = max(1, int(os.environ.get("ATEC_PI05_ACTION_REPEAT", "1")))
self._action_clip = float(os.environ.get("ATEC_PI05_ACTION_CLIP", "5.0"))
self._chunk_exec_steps = max(1, int(os.environ.get("ATEC_PI05_CHUNK_EXEC_STEPS", "10")))
self._zero_noise = os.environ.get("ATEC_PI05_ZERO_NOISE", "0").lower() in ("1", "true", "yes")
self._action_horizon = max(1, int(os.environ.get("ATEC_PI05_ACTION_HORIZON", "10")))
self._model_action_dim = max(8, int(os.environ.get("ATEC_PI05_MODEL_ACTION_DIM", "32")))
self._debug = os.environ.get("ATEC_PI05_DEBUG", "0").lower() in ("1", "true", "yes")
self._resize_size = (224, 224)
self.reset_episode()
def reset_episode(self):
self._startup_step = 0
self._home_stable_steps = 0
self._home_done = False
self._action_queue: deque[np.ndarray] = deque()
self._held_action: np.ndarray | None = None
self._held_remaining = 0
self._debug_step = 0
self._policy_calls = 0
def _compute_home_action(self, proprio: torch.Tensor) -> tuple[torch.Tensor, bool]:
qpos = proprio[:, self._QPOS_SLICE] + self.default_joint_pos
qerr = self.teleop_home_joint_pos - qpos
within_tolerance = torch.all(torch.abs(qerr) <= self._home_qpos_tolerance, dim=1)
self._home_stable_steps = self._home_stable_steps + 1 if bool(torch.all(within_tolerance)) else 0
home_reached = self._home_stable_steps >= self._home_hold_steps
if self._debug and self._debug_step % 50 == 0:
print(
"[PI05_DEBUG] "
f"home step={self._debug_step} max_abs_qerr={torch.max(torch.abs(qerr)).item():.4f} "
f"stable={self._home_stable_steps}/{self._home_hold_steps} "
f"qpos={qpos[0].detach().cpu().numpy()[:8]}",
flush=True,
)
return self._home_action.repeat(proprio.shape[0], 1), home_reached
def _rgb_from_obs(self, obs: dict) -> np.ndarray:
rgb = obs["image"]["video_rgb"]
if isinstance(rgb, torch.Tensor):
rgb = rgb[0].detach().cpu()
if rgb.ndim == 3 and rgb.shape[0] in (3, 4):
rgb = rgb[:3].permute(1, 2, 0)
if rgb.ndim == 3 and rgb.shape[-1] == 4:
rgb = rgb[..., :3]
if rgb.dtype != torch.uint8:
rgb = (rgb.float() * 255.0).clamp(0, 255).to(torch.uint8)
if tuple(rgb.shape[:2]) != self._resize_size:
rgb = TF.resize(
rgb.permute(2, 0, 1),
list(self._resize_size),
interpolation=TF.InterpolationMode.BILINEAR,
antialias=True,
).permute(1, 2, 0)
return rgb.numpy()
rgb = np.asarray(rgb[0])
if rgb.shape[-1] == 4:
rgb = rgb[..., :3]
if np.issubdtype(rgb.dtype, np.floating):
rgb = (rgb * 255.0).clip(0, 255).astype(np.uint8)
return rgb.astype(np.uint8, copy=False)
def _openpi_obs(self, obs: dict, proprio: torch.Tensor) -> dict:
qpos = (proprio[:, self._QPOS_SLICE] + self.default_joint_pos).detach().cpu().numpy()[0]
openpi_obs = {
"state": qpos.astype(np.float32, copy=False),
"image": self._rgb_from_obs(obs),
"prompt": self.prompt,
}
if self._zero_noise:
openpi_obs["noise"] = np.zeros((self._action_horizon, self._model_action_dim), dtype=np.float32)
return openpi_obs
def _next_pi05_action(self, obs: dict, proprio: torch.Tensor) -> np.ndarray:
if self._held_action is not None and self._held_remaining > 0:
self._held_remaining -= 1
return self._held_action
if not self._action_queue:
response = self.policy.infer(self._openpi_obs(obs, proprio))
actions = np.asarray(response["actions"], dtype=np.float32)
if actions.ndim != 2 or actions.shape[-1] < 8:
raise ValueError(f"Expected OpenPI native8 actions with shape (T, >=8), got {actions.shape}")
self._policy_calls += 1
if self._debug:
print(
"[PI05_DEBUG] "
f"policy_call={self._policy_calls} actions_shape={actions.shape} "
f"first_action={actions[0, :8]}",
flush=True,
)
for action8 in actions[: self._chunk_exec_steps]:
self._action_queue.append(np.clip(action8[:8], -self._action_clip, self._action_clip))
self._held_action = self._action_queue.popleft()
self._held_remaining = self._action_repeat - 1
return self._held_action
def predicts(self, obs, current_score):
if not isinstance(obs, dict) or "proprio" not in obs:
raise ValueError("Expected obs dict with 'proprio' key.")
proprio = obs["proprio"].to(self.device)
if proprio.shape[0] != 1:
raise ValueError("solution_pi05_native8 supports num_envs=1.")
if self._startup_step < self._startup_zero_steps:
self._startup_step += 1
self._debug_step += 1
if self._debug and self._startup_step in (1, self._startup_zero_steps):
print(f"[PI05_DEBUG] startup step={self._startup_step}/{self._startup_zero_steps}", flush=True)
return {"action": np.zeros((1, 8), dtype=np.float32).tolist(), "giveup": False}
if not self._home_done:
home_action, home_reached = self._compute_home_action(proprio)
if home_reached:
self._home_done = True
self._action_queue.clear()
self._held_action = None
self._held_remaining = 0
if self._debug:
print(f"[PI05_DEBUG] home_done at step={self._debug_step}", flush=True)
self._debug_step += 1
return {"action": home_action.detach().cpu().numpy().tolist(), "giveup": False}
action = self._next_pi05_action(obs, proprio)
if self._debug and self._debug_step % 50 == 0:
print(f"[PI05_DEBUG] execute step={self._debug_step} action={action[:8]}", flush=True)
self._debug_step += 1
return {"action": action.reshape(1, -1).tolist(), "giveup": False}
|