File size: 8,354 Bytes
21e1acb | 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 | """Measure execution error for demo.solution_pca in Task E.
Uses simulator internals only for debugging: object root, gripper_base, link7,
and link8 positions. The submission policy still only receives observations.
"""
from __future__ import annotations
import argparse
import os
import sys
import time
from isaaclab.app import AppLauncher
parser = argparse.ArgumentParser()
parser.add_argument("--task", type=str, default="ATEC-TaskE-Piper")
parser.add_argument("--seed", type=int, default=12)
parser.add_argument("--object", type=int, default=3)
parser.add_argument("--max_steps", type=int, default=2500)
parser.add_argument("--solution_module", type=str, default="solution_pca")
AppLauncher.add_app_launcher_args(parser)
args_cli = parser.parse_args()
args_cli.enable_cameras = True
repo_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))
demo_dir = os.path.join(repo_root, "demo")
if repo_root not in sys.path:
sys.path.insert(0, repo_root)
if demo_dir not in sys.path:
sys.path.insert(0, demo_dir)
app_launcher = AppLauncher(args_cli)
simulation_app = app_launcher.app
import gymnasium as gym # noqa: E402
import importlib # noqa: E402
import numpy as np # noqa: E402
import torch # noqa: E402
from isaaclab.envs import DirectMARLEnv, multi_agent_to_single_agent # noqa: E402
from isaaclab_tasks.utils import parse_env_cfg # noqa: E402
import atec_rl_lab.tasks # noqa: F401,E402
def main() -> None:
env_cfg = parse_env_cfg(args_cli.task, device=args_cli.device, num_envs=1)
env_cfg.seed = args_cli.seed
env = gym.make(args_cli.task, cfg=env_cfg)
if isinstance(env.unwrapped, DirectMARLEnv):
env = multi_agent_to_single_agent(env)
module = importlib.import_module(args_cli.solution_module)
AlgSolution = module.AlgSolution
policy = AlgSolution()
try:
obs, _ = env.reset(seed=args_cli.seed)
policy.reset_episode()
robot = env.unwrapped.scene.articulations["robot"]
obj = env.unwrapped.scene.rigid_objects[f"object_{args_cli.object}"]
gb_id = int(robot.find_bodies("gripper_base")[0][0])
link7_id = int(robot.find_bodies("link7")[0][0])
link8_id = int(robot.find_bodies("link8")[0][0])
obj_initial = obj.data.root_pos_w[0].detach().cpu().numpy().astype(np.float64)
best = {
"finger_dist": (999.0, None, 0, None),
"gb_dist": (999.0, None, 0, None),
"max_z_gain": (-999.0, None, 0, None),
"pin_finger_err": (999.0, None, 0, None),
"pin_gb_err": (999.0, None, 0, None),
}
stage_best = {}
pin_err_sum = {"finger": 0.0, "gb": 0.0}
pin_err_n = 0
total_reward = 0.0
start = time.time()
for step in range(args_cli.max_steps):
with torch.inference_mode():
resp = policy.predicts(obs, total_reward)
action = torch.as_tensor(resp["action"], dtype=torch.float32, device=args_cli.device).view(1, -1)
obs, reward, terminated, truncated, info = env.step(action)
sim_dt = info["Step_dt"]
total_reward += reward.mean().item() / sim_dt if isinstance(reward, torch.Tensor) else float(reward) / sim_dt
obj_pos = obj.data.root_pos_w[0, :3].detach().cpu().numpy().astype(np.float64)
gb = robot.data.body_pos_w[0, gb_id, :3].detach().cpu().numpy().astype(np.float64)
f7 = robot.data.body_pos_w[0, link7_id, :3].detach().cpu().numpy().astype(np.float64)
f8 = robot.data.body_pos_w[0, link8_id, :3].detach().cpu().numpy().astype(np.float64)
finger = 0.5 * (f7 + f8)
qpos = policy._obs_qpos(obs)
pin_finger = policy.ik.finger_center_world(qpos)
pin_gb_b, _ = policy.ik.fk_base(qpos[:6])
pin_gb = module.BASE_POS_W + module.R_W_B @ pin_gb_b
gap = float(np.linalg.norm(f7 - f8))
fd = float(np.linalg.norm(finger - obj_pos))
gd = float(np.linalg.norm(gb - obj_pos))
pfe = float(np.linalg.norm(pin_finger - finger))
pge = float(np.linalg.norm(pin_gb - gb))
pin_err_sum["finger"] += pfe
pin_err_sum["gb"] += pge
pin_err_n += 1
zg = float(obj_pos[2] - obj_initial[2])
plan_idx = getattr(policy, "plan_idx", -1)
target_step = getattr(policy, "step_in_target", -1)
label = "none"
plan = getattr(policy, "plan", None)
if isinstance(plan, list) and 0 <= plan_idx < len(plan):
label = getattr(plan[plan_idx], "label", str(plan_idx))
stage = stage_best.setdefault(
label,
{
"min_finger": (999.0, None, 0, None),
"max_z_gain": (-999.0, None, 0, None),
"min_gap": (999.0, 0),
"last": None,
},
)
if fd < stage["min_finger"][0]:
stage["min_finger"] = (fd, finger - obj_pos, step, (plan_idx, target_step, gap, qpos[6], qpos[7]))
if zg > stage["max_z_gain"][0]:
stage["max_z_gain"] = (zg, obj_pos.copy(), step, (plan_idx, target_step, gap, qpos[6], qpos[7]))
if gap < stage["min_gap"][0]:
stage["min_gap"] = (gap, step)
stage["last"] = (finger - obj_pos, obj_pos.copy(), gap, qpos[6], qpos[7], step)
if fd < best["finger_dist"][0]:
best["finger_dist"] = (fd, finger - obj_pos, step, (plan_idx, target_step, gap))
if gd < best["gb_dist"][0]:
best["gb_dist"] = (gd, gb - obj_pos, step, (plan_idx, target_step, gap))
if zg > best["max_z_gain"][0]:
best["max_z_gain"] = (zg, obj_pos.copy(), step, (plan_idx, target_step, gap))
if pfe < best["pin_finger_err"][0]:
best["pin_finger_err"] = (pfe, pin_finger - finger, step, (plan_idx, target_step, gap))
if pge < best["pin_gb_err"][0]:
best["pin_gb_err"] = (pge, pin_gb - gb, step, (plan_idx, target_step, gap))
if bool(terminated.item() or truncated.item()):
break
final = obj.data.root_pos_w[0, :3].detach().cpu().numpy().astype(np.float64)
print(f"[EXEC_DEBUG] seed={args_cli.seed} obj={args_cli.object} score={total_reward:.2f} wall={time.time()-start:.1f}s")
print(f"[EXEC_DEBUG] initial=({obj_initial[0]:.4f},{obj_initial[1]:.4f},{obj_initial[2]:.4f}) final=({final[0]:.4f},{final[1]:.4f},{final[2]:.4f})")
if pin_err_n:
print(f"[EXEC_DEBUG] pin_mean_err finger={pin_err_sum['finger']/pin_err_n:.4f} gb={pin_err_sum['gb']/pin_err_n:.4f}")
for key, (value, vec, step, meta) in best.items():
if vec is None:
print(f"[EXEC_DEBUG] {key}=none")
elif key == "max_z_gain":
print(f"[EXEC_DEBUG] {key}={value:.4f} obj=({vec[0]:.4f},{vec[1]:.4f},{vec[2]:.4f}) step={step} meta={meta}")
else:
print(f"[EXEC_DEBUG] {key}={value:.4f} vec=({vec[0]:+.4f},{vec[1]:+.4f},{vec[2]:+.4f}) step={step} meta={meta}")
for label, stats in stage_best.items():
if not any(k in label for k in ("reach", "insert", "close", "lift", "mid", "release")):
continue
fd, fvec, fstep, fmeta = stats["min_finger"]
zg, zobj, zstep, zmeta = stats["max_z_gain"]
last = stats["last"]
if fvec is None or zobj is None or last is None:
continue
lvec, lobj, lgap, lq7, lq8, lstep = last
print(
f"[STAGE_DEBUG] label={label} min_fd={fd:.4f} "
f"min_vec=({fvec[0]:+.4f},{fvec[1]:+.4f},{fvec[2]:+.4f}) "
f"min_meta={fmeta} max_z_gain={zg:.4f} "
f"z_obj=({zobj[0]:.4f},{zobj[1]:.4f},{zobj[2]:.4f}) z_meta={zmeta} "
f"last_vec=({lvec[0]:+.4f},{lvec[1]:+.4f},{lvec[2]:+.4f}) "
f"last_obj=({lobj[0]:.4f},{lobj[1]:.4f},{lobj[2]:.4f}) "
f"last_gap={lgap:.4f} last_q=({lq7:.4f},{lq8:.4f}) last_step={lstep}"
)
finally:
env.close()
if __name__ == "__main__":
try:
main()
finally:
simulation_app.close()
|