WORKSPACE_MEMORY.md

Workspace Memory

ATEC2026 L0 Task E

• Scope:

  • Current priority is L0 Task E tabletop manipulation in /home/ubuntu/Documents/01Proj/13atec/ATEC2026_Simulation_Challenge; do not switch to L1 unless the user asks.
  • 2026-06-07 官网文件上传式提交记录：XSA-ACT 上传后线上得分 6.00，构建号 BUILD2001385_2038204482147323904_20260607142442_bae481，任务“桌面整理 / Piper”，用时约 00:11:22.62。get_action_spec not found warning 不是扣分原因，已在 demo/solution_act.py 和 submissions/task_e_xsa_act_20260607_upload/solution.py 增加 get_action_spec(self): return None 消除 warning；更新后的 solution.py SHA256 a08790e7512c914e5b78132303e1c6098c8aba3b01a373933e6ddc617fa3a485。
  • 2026-06-07 PCA/GraspGen-style submit 分支复测：ATEC_PCA_OBJECTS=3,2,1、seed11、demo/solution_pca.py 跑满 5200 steps 得 0.00，日志 logs/pca_submit_eval/pca_submit_seed11_20260607.log，视频 logs/videos/task_e_pca_submit_eval/pca_submit_seed11_20260607.mp4。失败现象：RGB-D/PCA 中心估计可生成，但 submit-side IK/finger 实际位置长期追不上 desired finger center，object3/object2/object1 都未入篮。当前不要把 solution_pca.py 当作打榜提交。
  • 2026-06-07/08 为线上试榜准备两个 ACT seed2 checkpoint 包，均已瘦身为推理 checkpoint（约 64MB）并通过 AlgSolution() 初始化：submissions/task_e_act_seed2_45000_20260607_upload，zip SHA256 4b5d1178db3060883db4400f24eaa04b11b259a5179a220a7a622d7347ea2895，policy_act.pt SHA256 d9badbf17b0f29000c4fb21c3a2a89f66f7450e3ec1cb7362e733f4a4a7a0cb2；历史本地 seed11 得 18、seed12 得 0，线上用户回报得 12.00。submissions/task_e_act_seed2_50000_20260607_upload，zip SHA256 1d777cfa95ee40d92afafd70f5e3cbfd3ee6477f0b036ed9d459c94237858118，policy_act.pt SHA256 8ac243e8427c54704a0445fa8f0321450618fa6c2d2d7296db027c9c5fe1e2c9；历史本地 seed11 得 15、seed12 得 6，线上用户回报得 6.00。两个包都用更新后的 solution.py SHA a08790e... 和无 IPython 的 act/detr/transformer.py SHA fa290d...。结论：seed2 checkpoint 方差大，不能视为稳定 18 分方案。
  • 2026-06-08 准备旧 ACT seed1 best “抽奖候选”提交包：submissions/task_e_act_seed1_best_20260608_upload，zip submissions/task_e_act_seed1_best_20260608_upload.zip，zip SHA256 e265ebe77b989a968cf4607ea70d4f3adc2322902538828b37fe84877f5b1391，policy_act.pt SHA256 282614de9673dc01e229557448e380b6a02c196b5c3953cec77b2aebd2305a8e，solution.py SHA256 a08790e7512c914e5b78132303e1c6098c8aba3b01a373933e6ddc617fa3a485。来源是旧 seed1 best（原备份 SHA 76bbef2c81c85fc6cc60448476ff09075ec8bc97217c2d2fb70a3253fd3e307d），已瘦身只保留 norm_stats、model_args、ema_agent 并通过 AlgSolution() 初始化。历史本地独立 run1 得 18、run2/run3 得 6，线上尚未提交；若用户还能多次试榜，这是当前已打包候选里最值得下一次提交的包。
  • 2026-07-19 赛后开源归档：模型仓库为 https://huggingface.co/Datawhale/atec2026-task-e-act-seed1-best，已上传 policy_act.pt 与完整提交 zip；数据/日志仓库为 https://huggingface.co/datasets/Datawhale/atec2026-task-e-reproducibility。复现训练数据使用 filtered HDF5：datasets/atec_task_e_obj321_servo_100demos/trajectory_filtered.hdf5，大小约 33GiB，SHA256 4b85980e72b8e76261ad037bfba97b4795210e3f77cdb5b71479846b9230ac02；HF 上按 100MiB 拆为 data/final_100demos_filtered_split_100m/trajectory_filtered.hdf5.part-0000..0336，配套 RESTORE_FILTERED_DATASET.sh、SHA256_FILTERED_ORIGINAL.txt、SHA256SUMS_FILTERED_SPLIT.txt。上传采用暂存目录 /data/Data14TB/01Proj/ATEC2026/hf_task_e_filtered_100m 和 user service atec-task-e-hf-filtered-100m-upload.service，入口 /data/Data14TB/01Proj/ATEC2026/upload_task_e_filtered_100m_large_folder.py，设置 HF_HUB_DISABLE_XET=1 与 upload_large_folder(num_workers=8) 断点续传。2026-07-19 已完成远端校验：337/337 分片齐全、README.md、RESTORE_FILTERED_DATASET.sh、两个 SHA256 清单和 logs/task_e_logs_20260520_20260609.tar.zst 均存在；上传暂存目录可清理，但原始 trajectory_filtered.hdf5 仍保留，除非用户另行确认。
  • 2026-06-08 用户已提交 task_e_act_seed1_best_20260608_upload/policy_act.pt 到官网，线上出分 15.00；当天出分/提交次数紧张，用户表示此后只剩一次提交机会。该结果强于 XSA 线上 6、seed2_45000 线上 12、seed2_50000 线上 6，目前是线上最佳。最后一次提交必须有本地明确优于 15 的证据，否则不要用掉。
  • 最后一投风险判断，2026-06-08：历史日志显示 XSA final 和 seed2_50000 通常 object_2/object_3 入篮，object_1 失败；seed2_45000 本地 seed11 object_1/2/3 全入篮但 seed12 全崩，线上 12 说明隐藏评估仍丢至少一个物体。PCA/GraspGen-style submit 分支当前 seed11 为 0，完整 GraspNet/AnyGrasp 不符合短期单文件稳定提交条件。不要在最后一次机会中使用未通过 submit-style 本地评估的 PCA/GraspNet 方案。
  • 最后一投 hybrid 试验，2026-06-08：构建了 submissions/task_e_act_seed1_hybrid_obj1_pca_20260608_upload，思路是 ACT seed1/XSA 先做前两物体，之后切 solution_pca.py 的 object_1-only 几何补救。初始化修复包括提交目录内复制 solution_pca.py、Piper URDF third_party/Agilex-College/piper/handpose_det/models/modified_piper_without_camera.urdf，并把 solution_pca.py URDF 搜索改成优先当前提交目录。结果失败：seed1-best hybrid seed11 score=6.00，只有 object_3 inside，视频 logs/videos/task_e_hybrid_eval/hybrid_obj1_pca_seed11_20260608.mp4；XSA final hybrid 固定 ACT step 1320 切 PCA 后 seed11 score=3.00，object_1/2/3 均不 inside，视频 logs/videos/task_e_hybrid_eval/hybrid_xsa_fixed1320_obj1_pca_seed11_20260608.mp4。目录内已加 DO_NOT_SUBMIT.md；不要用该 hybrid 包做最后一次线上提交。
  • 2026-06-08 下午继续冲分负结果：新增实验文件 demo/solution_hybrid_obj1_pca.py（仅 current_score>=15 后切 object_1 PCA）和 demo/solution_dual_act.py（两套 ACT 同步观测、score 阈值后切副策略），都还不是提交方案。logs/eval_task_e_hybrid_xsa_score15_obj1_pca_seed11_20260608.log 得 0.00；logs/eval_task_e_hybrid_seed1best_score15_obj1_pca_seed12_20260608.log 得 0.00；logs/eval_task_e_hybrid_seed1_95000_score15_obj1_pca_seed11_20260608.log 得 0.00，视频 logs/videos/task_e_hybrid_eval/hybrid_seed1_95000_score15_obj1_pca_seed11_20260608.mp4。双 ACT：logs/eval_task_e_dualact_seed1best_seed2_45000_switch12_seed11_20260608.log 得 0.00；logs/eval_task_e_dualact_seed1_95000_seed2_45000_switch12_seed11_20260608.log 在 score=15、primary_steps=900 切副策略，但最终仍 15.00，object_1 外、object_2/3 内。结论：score=15 后的 PCA/双 ACT 接力没有把 object_1 补入篮，不能消耗最后一次提交机会。
  • 2026-06-08 继续 hybrid 冲 18 的新增边界：runs/.../seed1/checkpoints/95000.pt trace seed11 显示 step 300 score=6、step 600 score=12，object_2/object_3 已入篮而 object_1 仍在桌面，日志 logs/trace_task_e_seed1_95000_seed11_20260608.log。据此测试 score=12 + min_act_policy_steps=600 切 object_1 PCA：logs/eval_task_e_hybrid_seed1_95000_score12_step600_obj1_pca_seed11_20260608.log 仍 15.00，object_1 最终 (0.988,0.336,0.872) 外，object_2/3 内；打开旧 object_1 rescue 的 logs/eval_task_e_hybrid_seed1_95000_score12_step600_obj1_pca_rescue_seed11_20260608.log 退到 12.00 且未触发 hybrid switch。已给 demo/solution_hybrid_obj1_pca.py 加显式 ATEC_HYBRID_FORCE_ENV_STEPS 计数和切换原因打印，避免只依赖 ACT 内部 _ts/score 回调；已给 demo/solution_pca.py 增加 ATEC_PCA_OBJ1_DIRECT_RESCUE=1 实验入口，并修正 _build_object1_drag_rescue() 的明显 bug：旧版 start_y=TABLE_CENTER_Y+0.085 会在 object_1 位于 y≈0.26..0.34 时从方块前方错过，现改为从当前方块中心正 y 侧动态生成 lane，且 lane x 也围绕当前检测中心。由于 2026-06-08 16:15-16:45 机器 GPU/Isaac 并发很重，多条 force-env eval 在 Isaac 启动后迟迟进不到有效 step，已手动停止；这些新 direct-rescue 补丁尚未形成 >15 本地证据，不得提交。
  • 2026-06-08 seed1 checkpoint sweep：对 runs/act-task-e-rgb-obj321-servo-100demos-seed1/checkpoints 粗筛 seed11。结果：35000=0、45000=12、50000=0、55000=6、65000=0、70000=0、75000=6、80000=6、90000=9、95000=15、best_loss=0，对应日志 logs/eval_task_e_seed1_*_20260608_sweep*.log。扩测 95000.pt：seed12 6、seed13 6，因此 95000 不是比线上 seed1-best 15 更稳的替换候选。当前最佳仍是线上已有 task_e_act_seed1_best_20260608_upload 的 15.00，不要提交 95000 或 dual/hybrid。
  • 2026-06-04 已整理中文交接文档：docs/ATEC2026_TaskE_规则物理配置与提交说明_20260604.md；官方 README 快照下载到 docs/sources/ATEC2026_Simulation_Challenge_official_readme_20260604.md。文档结论：当前是官方 ATEC-TaskE-Piper / Isaac Lab v2.3.2 环境，Piper solver、物体质量、摩擦/contact 参数来自本地官方源码；香蕉滑落更像二指夹爪接触保持和 submit-side primitive 问题，不能靠修改 judge 物理提交，物理增强只适合诊断。
  • 2026-06-04 fresh clone 官方仓库到 /tmp/ATEC2026_Simulation_Challenge_official_check，HEAD dbe7c251f680b02f357a6db67430b18d3ba45ea1。核对结果：piper.py、object.py、Task E terrain.py、rewards.py、terminations.py 与本地 SHA 完全一致；task_e/env_cfg.py 只有 action group 字段名差异，物体/篮筐/相机/Y-band/physics material 逻辑一致。官方最新版新增 demo/server.py 的 /get_action_spec 和 custom action 示例，这属于动作接口可选自定义，不是允许改 judge 物理。
  • 2026-06-04 再次 git fetch origin 官方仓库，origin/main 仍停在 dbe7c25，没有新 commit。GitHub issues/PR 网页核对：未发现公开 issue 提到 Task E/Piper/grasp/physics/friction/banana 滑落；Piper 搜索命中的是 PR #2 eye-in-hand camera for B2Piper/B2WPiper/G1，不是 Task E Piper；PR #5 是 participant action spec，PR #8 是 Tron2A joint configuration。已同步唯一低风险兼容修复：给本地 demo/server.py 添加官方 /get_action_spec endpoint；若 solution 无该方法则返回 {}，保持默认 action config。
  • Official Task E robot is ATEC-TaskE-Piper; the newer Tron2AWheel / Tron2ALegged models are not relevant for this tabletop task.

• 调试复盘总览，2026-05-30：

  • 当前结论：可部署基线仍是 XSA-ACT 的 demo/policy_act.pt，SHA256 c3eacae3f1b0ec8ccde9fdc05d680fff119cc2ca70e1f79e4ddd5610c4bbfa93。它不是满分稳定方案，但本地证据仍强于 seed2 ACT、GraspNet/SAM3 直接控制和 pi0.5/OpenPI 分支。不要用 pi0.5 当前 checkpoint 覆盖 demo/policy_act.pt。
  • 机器人/任务身份：Task E 使用 ATEC-TaskE-Piper，本地 env 注册在 source/atec_rl_lab/atec_rl_lab/tasks/task_e/__init__.py，Piper 配置来自 TaskEEnvPiperCfg / PIPER_CFG。PiPER 是 AgileX/松灵机械臂系列；本任务是桌面级固定 Piper，不是 L1 的 Tron/Unitree 轮腿/足式机器人。
  • 数据生成主线：官方/初始 scripted oracle 对 object_3 相对可用，但 object_1/object_2 失败，根因不是相机，而是 USD/root 几何中心、Piper gripper TCP、夹爪实际接触几何和运输/投放 primitive 不匹配。最终通过 per-object calibrated primitive 和闭环 servo 生成了三物体 3 2 1 成功数据。核心数据目录是 datasets/atec_task_e_obj321_servo_100demos，过滤后 HDF5 是 datasets/atec_task_e_obj321_servo_100demos/trajectory_filtered.hdf5。
  • ACT seed1 主线：用上述 100 demos 训练普通 ACT 后，第一次评估失败是 demo/solution_act.py home 阶段理解错 action：Task E action 是 (joint_target - default_joint_pos) / 0.5，不是速度/PD delta。修复 home action 后普通 ACT 可部分成功，但 best_loss.pt/final.pt 不稳定，训练 loss 与任务成功率弱相关。旧 seed1 best 已备份为 demo/policy_act.seed1_best_backup_20260522_012719.pt。
  • XSA-ACT 主线：用户提供的 xsa_update_20260520/lerobot_policy_act_xsa.zip 是 LeRobot act_xsa 插件，原始例子不是 ATEC 8D Piper 直接可跑。已在 source/atec_rl_lab/atec_rl_lab/train/act/act/detr/transformer.py 和 demo/act/detr/transformer.py 增加 --use_xsa 适配；scripts/act/train_task_e.py 保存 model_args，demo/solution_act.py 能按 checkpoint 自动加载 use_xsa。XSA final 直接独立评估 seeds 11/12/13 曾得 15/15/15，日志 logs/eval_task_e_xsa_final_seed11_recheck.log、logs/eval_task_e_xsa_final_seed12_recheck.log、logs/eval_task_e_xsa_final_seed13_recheck.log；但部署后复查有 seed11 9、seed12 0 的接触/非确定性波动。仍然是当前最强可部署基线。
  • ACT seed2/resume 主线：seed2 从头训练并加了 resume 支持，相关改动在 scripts/act/train_task_e.py 和 scripts/act/run_task_e_pipeline.sh，支持 --resume_checkpoint / --resume_iter、RESUME_CHECKPOINT / RESUME_ITER，并保存 optimizer/scheduler/EMA。seed2 过程中有局部 snapshot 看似高分，例如 45000.pt seed11 18，但 seed12 0；最终 final.pt seed11 0，best_loss.pt seed11 9。结论：seed2 不部署，且不要把单 seed 高分当作可靠证据。
  • GraspNet/TunTunClaw 主线：用户给的 OpenClaw/Datawhale TunTunClaw 参考已接到 third_party/tuntunclaw/，GraspNet checkpoint 是 third_party/tuntunclaw/temp/logs/log_rs/checkpoint-rs.tar；桥接脚本是 scripts/graspnet_task_e/tuntun_adapter.py 和 scripts/graspnet_task_e/run_graspnet_pick.py。依赖在 /home/ubuntu/envs/genmanip-isaac5-py311/bin/python 下打通，包括 open3d==0.19.0、本地 graspnetAPI、transforms3d==0.4.2、Blackwell TORCH_CUDA_ARCH_LIST=12.0 的 pointnet2 CUDA extension。GraspNet 能给候选，但原始候选不能直接解决真实 Isaac 接触；object_3 可通过 SAM3/GraspNet+真实 finger/staged transport 成功，object_1/object_2 仍在运输阶段滑落/偏出，不能作为最终控制器。
  • SAM3 主线：SAM3 checkpoint 位于 /home/ubuntu/Documents/01Proj/sam3d_gs/submodule/Prompt-Inpaint/checkpoints/sam3.pt，独立 Python 在 /home/ubuntu/Documents/01Proj/sam3d_gs/.venv/bin/python，不要装进 Isaac env。新增 scripts/graspnet_task_e/sam3_segment_image.py 做 RGB prompt 分割；run_graspnet_pick.py 支持 --mask_provider sam3、--sam3_prompt、--basket_center_release。重要结论：SAM3 分割不是主要瓶颈，object_1/2/3 的 mask 都能做对；早期失败是多候选 mask 选错，后来用 world-band/RGB-D 点数筛选修复。真正瓶颈是 Piper 夹爪接触和运输阶段防滑。
  • GraspNet/SAM3 放置规则：用户提出“目标中心点上方、稳定后再松爪”是正确方向。已加入 transport-end logging、短稳定 hold、--release_z、--basket_xy_tol、--basket_hold_steps、--basket_stable_steps、--basket_recovery_steps、--staged_transport、--transport_servo_fraction 等参数。经验：不要长时间高空 hold，object 会 slip/bounce；object_3 用短稳定 hold 更好。object_1/object_2 仍不稳，不能只调松爪时机，需要重做 grasp/contact primitive。
  • pi0.5/OpenPI 主线：OpenPI repo 是 /home/ubuntu/src/openpi-ebench-clean，env 是 /home/ubuntu/envs/openpi-pi05，通用 checkpoint 是 /home/ubuntu/projects/robotics_shared/checkpoints/openpi/pi05-ebench-generalist/pi05_generalist/params。必须设置 HF_LEROBOT_HOME=/home/ubuntu/projects/robotics_shared/datasets/lerobot、HF_HOME=/home/ubuntu/projects/robotics_shared/hf_cache、PYTHONPATH=/home/ubuntu/src/openpi-ebench-clean/src:/home/ubuntu/src/openpi-ebench-clean/packages/openpi-client/src。不要在 OpenPI repo 里 uv run，会创建巨大 .venv 并重复下载。
  • pi0.5 数据转换：scripts/pi05/convert_task_e_hdf5_to_lerobot.py 把 ATEC HDF5 的 8D qpos/action 映射成 OpenPI fixed-base 16D padded 格式：state/action.joints[:6] 对应 Piper 6 arm joints，state/action.gripper[:2] 对应两个夹爪关节，其余补零；三路相机 overlook/left/right 暂时都写同一张 224x224 RGB。正式数据集是 /home/ubuntu/projects/robotics_shared/datasets/lerobot/atec/task_e_obj321_servo_100demos_s5_224，约 4.7G，stride=5，fps=10。
  • pi0.5 关键 action 约束：ATEC HDF5 里的 actions 已经是 env action (joint_target - default_joint_pos) / 0.5，不是绝对 qpos。因此所有 ATEC OpenPI config 都必须 extra_delta_transform=False。若使用 OpenPI 默认 delta transform，会再减一次 state，训练目标会错。
  • pi0.5 训练结果：20-demo 快速分支 pi05_atec_task_e_20demos_s5_224 2k step 在 seeds 11/12/13 全 0，checkpoint 已删，只保留 logs/pi05_eval_20260522074137 和视频 logs/videos/task_e_pi05_eval/pi05_pi05_atec_task_e_20demos_s5_224_seed11_20260522074137.mp4。100-demo 正式分支 pi05_atec_task_e_s5_224 训练 29999 step，checkpoint .../pi05_atec_task_e_s5_224/atec_task_e_pi05_s5_224_30k_20260522065716/29999 约 42G，评估 logs/pi05_eval_20260522112701：seed11 0，seed12 0，seed13 6，mean 2.0，视频 logs/videos/task_e_pi05_eval/pi05_pi05_atec_task_e_s5_224_seed11_20260522112701.mp4。结论：pi0.5 当前分支不部署。
  • pi0.5 失败原因判断：这不是“pi0.5 模型弱”的直接证据，而是适配层很可能不对。当前桥接把 ATEC Piper 8D action 硬塞到 OpenPI EBench fixed-base 16D，三路相机全用同一张图，prompt 粗粒度，数据只有 scripted oracle 风格，且评估 max_steps=1500/action_repeat=5 与 ACT 1800-step 轨迹不完全等价。另一个强疑点是 OpenPI loader 日志里 discrete_state_input=True，而公开 Piper pi0.5 posttrain 经验提到 discrete_state_input=False 可避免 z-score norm 下 state token saturation。后续若继续 pi0.5，应优先做原生 Piper/OpenPI config、检查 q01/q99/norm stats、试 discrete_state_input=False，并考虑先试 pi0 而非 pi0.5。
  • pi0.5 旧分支核查追加，2026-06-08：已复查 scripts/pi05/convert_task_e_hdf5_to_lerobot.py、demo/solution_pi05.py、OpenPI config.py 和 logs/pi05_train_task_e_s5_224_20260522065716.log。旧 converter 将 8D Piper pad 成 EBench fixed-base 12 joints + 4 gripper，其中真实 arm 在 0:6、真实 gripper 在 12:14，其余 8 个有效 EBench 维度全 0；随后 PadStatesAndActions(model_action_dim=32) 再 pad 到 32D。训练日志确认 TokenizePrompt(... discrete_state_input=True)，且三路图像 head/hand_left/hand_right 都是同一张 224 RGB。HDF5 原始数据无 NaN/Inf，动作确实是 env action，不是绝对 qpos；extra_delta_transform=False 是正确的。评估 logs/pi05_eval_20260522112701 显示 seed11/12 三物体基本停在桌面原 y 带，seed13 只 object_1 入篮，说明主要失败像是 embodiment/action-time bridge 和输入分布问题，而不是单纯“夹持滑落”。重启建议：保持 pi0.5 generalist 的 action_dim=32 以兼容预训练权重，但新增 ATEC-native transform，把真实 8D state/action 放在前 8 维并只输出前 8 维；设置 Pi0Config(pi05=True, action_horizon=50, discrete_state_input=False)；使用单相机真实 mask 或真实多相机，不要假三路；先用 stride=1 或 action_repeat=1/2，保存 1k/2k/5k/10k/15k 多 checkpoint 做 fixed-seed eval，再决定是否长训。
  • pi0.5 native8 重启分支，2026-06-08：新增 scripts/pi05/convert_task_e_hdf5_to_lerobot_native8.py、demo/solution_pi05_native8.py、OpenPI transform /home/ubuntu/src/openpi-ebench-clean/src/openpi/policies/atec_piper_policy.py，以及 configs pi05_atec_task_e_native8_20demos_s1_224 / pi05_atec_task_e_native8_100demos_s1_224。设计意图：输入真实 8D qpos、输出真实 8D env action，模型内部仍 pad 到 32D 兼容 pi0.5 权重；只用 video.base_camera_view 一路真实图像，left/right wrist 用零图且 image_mask=False；discrete_state_input=False；stride=1/action_repeat=1，避免旧 stride=5 把抓取时序压坏。脚本路径：scripts/pi05/run_pi05_native8_20demos_smoke.sh、scripts/pi05/run_pi05_native8_100demos_prepare.sh、scripts/pi05/run_pi05_native8_eval_checkpoint.sh。由于根盘 / 只剩约 68G，native8 的 LeRobot 数据和 OpenPI assets/checkpoints 必须写到 /data/Data4TB/01Proj/13atec/...；不要再把新 42G checkpoint 写到 /home/ubuntu/projects/...。
  • pi0.5 native8 当前运行，2026-06-08 22:30 CST：旧 atec-pi05-native8-20demo-smoke-20260608222155.service 因同步图片写入太慢已主动停止；atec-pi05-native8-20demo-smoke-20260608222519.service 也主动停止，原因是 stride=1 转换约 1.5-2 小时，不适合先验 adapter 体检。converter 默认已改成 image_writer_processes=5、image_writer_threads=10。当前启动的是快速门控 user unit atec-pi05-native8-20demo-s2-smoke-20260608223030.service，执行 20-demo native8 stride=2 smoke（转换 -> norm stats -> 3k 训练 -> 自动评估 seeds 11/12/13），日志前缀 logs/pi05_native8_convert_20demos_s2_224_* / logs/pi05_native8_norm_20demos_s2_224_* / logs/pi05_native8_train_20demos_s2_224_* / logs/pi05_native8_eval_20demos_s2_224_*，数据目录 /data/Data4TB/01Proj/13atec/robotics_shared/datasets/lerobot/atec/task_e_obj321_servo_20demos_native8_s2_224。该分支是 adapter 体检，不是提交候选；评估使用 CONFIG=pi05_atec_task_e_native8_20demos_s2_224 ACTION_REPEAT=2 scripts/pi05/run_pi05_native8_eval_checkpoint.sh <ckpt>。必须在 seeds 11/12/13 本地评估明显超过线上已得 15.00，才允许消耗最后一次官网提交机会。
  • pi0.5 native8 快速 sanity，2026-06-08 22:39 CST：20-demo s2 转换仍约 50 分钟，因此先主动停掉 atec-pi05-native8-20demo-s2-smoke-20260608223030.service，新增 pi05_atec_task_e_native8_4demos_s2_224 / scripts/pi05/run_pi05_native8_4demos_s2_sanity.sh，并启动 atec-pi05-native8-4demo-s2-sanity-20260608223911.service。该分支只用 4 条 demo、stride=2、1k steps，训练后自动 ACTION_REPEAT=2 评估 seeds 11/12/13；目的只是尽快确认 native8 action bridge / OpenPI websocket / Isaac rollout 是否能让机械臂朝目标运动，不代表最终分数。若 sanity 视频完全不动或乱动，先修 bridge/norm；若动作方向正确，再恢复 20-demo/100-demo。
  • pi0.5 native8 sanity 结果，2026-06-08 23:15 CST：4-demo s2 训练完成，loss 从 0.5766 降到约 0.0405，但评估 seeds 11/12/13 全 0.00，视频 logs/videos/task_e_pi05_native8_eval/pi05_native8_pi05_atec_task_e_native8_4demos_s2_224_seed11_20260608231046.mp4，montage logs/videos/task_e_pi05_native8_eval/pi05_native8_4demo_s2_seed11_montage_20260608231046.jpg。视频显示机械臂基本停在 home 附近；离线 action probe 用 checkpoint 999 对训练帧推理，输出不是全零（多维动作幅度约 1-3），说明 websocket/unnormalize 基本通，但 4 条数据太少/轨迹未泛化，不能作为提交依据。该 4-demo checkpoint 占 83G 且无价值，已删除；保留日志、数据和视频。
  • pi0.5 native8 当前门控运行，2026-06-08 23:19 CST：已把 native8 configs 的 save_interval/keep_period 改成只保存 final，避免 40GB 中间 checkpoint 拖慢训练。重新启动 20-demo s2 user unit atec-pi05-native8-20demo-s2-smoke-20260608231948.service，脚本 scripts/pi05/run_pi05_native8_20demos_s2_smoke.sh 会转换 20 demos stride=2 -> norm stats -> 3k train -> 自动 ACTION_REPEAT=2 评估 seeds 11/12/13。若仍是 0/0/0 或视频仍停在 home，下一步不要加数据，先修 action timing/home gate/bridge；若有明确移动和非零分，再跑 100-demo native8。
  • pi0.5 native8 20-demo s2 结果，2026-06-09 00:42 CST：pi05_atec_task_e_native8_20demos_s2_224 训练 3k 完成，loss 最低约 0.0188，checkpoint /data/Data4TB/01Proj/13atec/openpi_atec_runs/checkpoints/pi05_atec_task_e_native8_20demos_s2_224/atec_task_e_pi05_native8_20demos_s2_224_3k_20260608231948/2999。评估日志 logs/pi05_native8_eval_20260609003633 / 汇总 logs/pi05_native8_eval_20demos_s2_224_20260608231948.log，seeds 11/12/13 得分 6/0/0，mean 2.00；seed11 只有 object_3 入篮，视频 logs/videos/task_e_pi05_native8_eval/pi05_native8_pi05_atec_task_e_native8_20demos_s2_224_seed11_20260609003633.mp4。结论：native8 bridge 已能产生部分有效策略，但 20 条数据不足以超过线上 15.00，不得提交。
  • pi0.5 native8 100-demo s2 门控，2026-06-09 00:46 CST：新增 OpenPI config pi05_atec_task_e_native8_100demos_s2_224（100 demos、stride=2/fps=25、discrete_state_input=False、10k step、final-only checkpoint、assets/checkpoints 在 /data/Data4TB/01Proj/13atec/openpi_atec_runs）和脚本 scripts/pi05/run_pi05_native8_100demos_s2_gate.sh。已启动 user unit atec-pi05-native8-100demo-s2-gate-20260609004546.service，流程为转换 trajectory_filtered.hdf5 -> norm stats -> 10k train -> ACTION_REPEAT=2 PORT=8023 评估 seeds 11/12/13。日志前缀：logs/pi05_native8_convert_100demos_s2_224_20260609004546.log、logs/pi05_native8_norm_100demos_s2_224_20260609004546.log、logs/pi05_native8_train_100demos_s2_224_20260609004546.log、logs/pi05_native8_eval_100demos_s2_224_20260609004546.log。只有本地多 seed 明显超过线上 15.00 才考虑最后一次官网提交。
  • pi0.5 native8 100-demo s2 结果，2026-06-09 05:49 CST：转换完成 100 episodes / 84877 frames，数据目录 /data/Data4TB/01Proj/13atec/robotics_shared/datasets/lerobot/atec/task_e_obj321_servo_100demos_native8_s2_224 约 3.9G；norm stats 写到 /data/Data4TB/01Proj/13atec/openpi_atec_runs/assets/pi05_atec_task_e_native8_100demos_s2_224/atec/task_e_obj321_servo_100demos_native8_s2_224。10k 训练完成，checkpoint /data/Data4TB/01Proj/13atec/openpi_atec_runs/checkpoints/pi05_atec_task_e_native8_100demos_s2_224/atec_task_e_pi05_native8_100demos_s2_224_10k_20260609004546/9999 约 42G；loss 从 0.4456 降到末段约 0.009-0.013，但任务评估很差。评估日志 logs/pi05_native8_eval_20260609054339 / 汇总 logs/pi05_native8_eval_100demos_s2_224_20260609004546.log，seeds 11/12/13 得分 3/0/0，mean 1.00，三个物体均未入篮；seed11 视频 logs/videos/task_e_pi05_native8_eval/pi05_native8_pi05_atec_task_e_native8_100demos_s2_224_seed11_20260609054339.mp4。结论：该 100-demo native8-s2 全量分支不得提交，也不能消耗最后一次官网机会；线上 15.00 ACT seed1 best 仍是受保护最佳。
  • pi0.5 native8 LoRA 入口/启动，2026-06-09 09:51 CST：参考第三方 Pantheon Piper pi0.5 LoRA（119 条 Piper single-arm episodes、discrete_state_input=False、7500 steps、peak LR 5e-5、LoRA 保先验）的经验，已把 OpenPI config pi05_atec_task_e_native8_100demos_s2_224_lora 修成真正 LoRA 门控：paligemma_variant="gemma_2b_lora"、action_expert_variant="gemma_300m_lora"、显式 freeze_filter=Pi0Config(...).get_freeze_filter()、num_train_steps=7500、peak_lr=5e-5、ema_decay=None、discrete_state_input=False。当前仍使用本地已有 /home/ubuntu/projects/robotics_shared/checkpoints/openpi/pi05-ebench-generalist/pi05_generalist/params 作为 weight loader，因为本机未发现本地 pi05_base 目录；这与 Pantheon 的 “pi05_base no warm-start” 不完全相同，若该分支失败，下一步可补下载/验证 gs://openpi-assets/checkpoints/pi05_base/params 分支。新增脚本 scripts/pi05/run_pi05_native8_100demos_s2_lora_gate.sh，复用已转换数据 /data/Data4TB/01Proj/13atec/robotics_shared/datasets/lerobot/atec/task_e_obj321_servo_100demos_native8_s2_224 和已有 norm stats，不重复转换；已启动 user unit atec-pi05-native8-100demo-s2-lora-gate-20260609095114.service，训练日志 logs/pi05_native8_lora_train_100demos_s2_224_20260609095114.log，service 外层日志 logs/atec-pi05-native8-100demo-s2-lora-gate-20260609095114.service.log，checkpoint 根目录 /data/Data4TB/01Proj/13atec/openpi_atec_runs/checkpoints/pi05_atec_task_e_native8_100demos_s2_224_lora，训练后自动 ACTION_REPEAT=2 PORT=8024 评估 seeds 11/12/13。门槛不变：只有本地多 seed 明显超过线上已有 15.00，才考虑用最后一次官网提交机会；否则继续保护 ACT seed1 best。
  • pi0.5 native8 LoRA 结果，2026-06-09 10:16 CST：atec-pi05-native8-100demo-s2-lora-gate-20260609095114.service 已完成。训练 7500 steps 正常结束，loss 从 0.4452 降到末段约 0.013-0.019，final checkpoint /data/Data4TB/01Proj/13atec/openpi_atec_runs/checkpoints/pi05_atec_task_e_native8_100demos_s2_224_lora/atec_task_e_pi05_native8_100demos_s2_224_lora_7500_20260609095114/7499 约 8.6G。自动评估 ACTION_REPEAT=2，日志 logs/pi05_native8_lora_eval_100demos_s2_224_20260609095114.log / logs/pi05_native8_eval_20260609101354，seed11/12/13 全部 0.00，三个物体均未入篮；seed11 视频 logs/videos/task_e_pi05_native8_eval/pi05_native8_pi05_atec_task_e_native8_100demos_s2_224_lora_seed11_20260609101354.mp4。结论：参考 Pantheon 的 LR/步数/discrete_state_input=False/LoRA freeze 后仍失败，说明当前 OpenPI native8 bridge/数据分布/动作时序问题未被 LoRA 解决；该 LoRA 不得提交，也不应消耗最后一次官网机会。临时心跳 atec-pi0-5-native8-lora-gate-monitor 已删除。
  • pi0.5 LoRA 失败排查，2026-06-09 10:30 CST：网页核对 OpenPI issue #672 和 Pantheon 卡片后，当前 LoRA 配置中的 freeze_filter、ema_decay=None、discrete_state_input=False 是对的；num_steps=10/25/50 离线采样对训练帧误差没有明显改善，说明不是单纯 ODE 采样步数太少。新增 demo/solution_pi05_native8.py 环境变量 ATEC_PI05_CHUNK_EXEC_STEPS（默认仍 50，不影响旧行为），测试 ATEC_PI05_CHUNK_EXEC_STEPS=5 seed11 仍 0.00，视频 logs/videos/task_e_pi05_native8_eval/pi05_native8_lora_chunk5_seed11.mp4，说明也不是简单 50-step 开环太长。更强根因：pi0.5 当前训练数据来自 trajectory_filtered.hdf5，该文件由 scripts/act/filter_demos.py 删除静止/hold 步，100 demos 从 749560 帧压到 169700 帧，约删掉 77%；未过滤数据 qpos diff p50 1.5e-5，过滤后 qpos diff p50 0.00936、max 0.419，这会破坏 OpenPI/pi0.5 50-step action chunk 的真实时间语义。另一个疑点：state 是绝对 qpos，但 action 是 ATEC env action (target-default)/0.5，与公开 Piper LeRobot 数据常见的 joint-position action 语义不一致；后续 pi0.5 应优先改成未过滤数据 + 绝对 joint target action，再由输出 transform 转回 env action。
  • pi0.5 rawabs/action-expert 排查，2026-06-09 12:30 CST：已按上条建议实现 raw unfiltered + absolute target action 体检。关键改动：scripts/pi05/convert_task_e_hdf5_to_lerobot_native8.py 支持 --action_mode absolute_target 和 --max_frames_per_episode；/home/ubuntu/src/openpi-ebench-clean/src/openpi/policies/atec_piper_policy.py 支持 AtecPiperOutputs(action_mode="absolute_target")，推理时把模型 absolute joint target 转回 ATEC env action；demo/solution_pi05_native8.py 默认 ATEC_PI05_CHUNK_EXEC_STEPS=10，并新增可选 ATEC_PI05_ZERO_NOISE=1/ATEC_PI05_ACTION_HORIZON。1-demo 数据集为 /data/Data4TB/01Proj/13atec/robotics_shared/datasets/lerobot/atec/task_e_obj321_servo_rawabs_1demo_native8_s10_224（raw trajectory.hdf5，1 episode，stride=10，250 frames，absolute target action）。
  • pi0.5 rawabs 结果，2026-06-09：新增 configs pi05_atec_task_e_native8_rawabs_1demo_s10_224_lora_base_h10_2k、..._aefull_h10_2k、..._aefull_h10_10k_lr5 和脚本 scripts/pi05/run_pi05_native8_rawabs_1demo_s10_aefull_h10_10k_lr5.sh。aefull_h10_10k_lr5 从本地 pi05_base/params 启动，只训练 action expert/action heads/time MLP，discrete_state_input=False、horizon=10、LR 5e-5、EMA off；checkpoint /data/Data4TB/01Proj/13atec/openpi_atec_runs/checkpoints/pi05_atec_task_e_native8_rawabs_1demo_s10_224_aefull_h10_10k_lr5/atec_task_e_pi05_native8_rawabs_1demo_s10_224_aefull_h10_10k_lr5_202606091220_aefull_h10_10k_lr5/9999 约 8.8G。离线 probe 从 2k 的 mean first/chunk L2 1.51/1.65 改善到 10k 的 0.28/1.10；固定 zero noise 约 0.22/0.87，但 chunk 仍不稳。Isaac seed11 实测：CHUNK_EXEC_STEPS=1 得 0.00（视频 logs/videos/task_e_pi05_native8_eval/pi05_native8_pi05_atec_task_e_native8_rawabs_1demo_s10_224_aefull_h10_10k_lr5_seed11_20260609122037.mp4），CHUNK_EXEC_STEPS=10 得 0.00（视频 ..._20260609122341.mp4，montage ..._20260609122341.montage.jpg），CHUNK_EXEC_STEPS=10 + ATEC_PI05_ZERO_NOISE=1 仍 0.00（日志 logs/pi05_native8_eval_20260609122748）。结论：OpenPI server、unnormalize、env action scale 都已被验证会执行，但策略在 home/高位观测附近缺少连续相位，动作在 home 与中段轨迹之间抖动，不能形成有效 pick/place；该 pi0.5 rawabs 分支仍不得作为最后一次官网提交候选。
  • pi0.5 数据策略，2026-06-08/09：先不追加 GraspGen/PCA 数据。理由：旧 pi0.5 的主要疑点是 embodiment/action-time bridge，若 native8 视频完全不动，加数据只会放大错误；20-demo native8 s2 已能得到 seed11 6，因此当前先跑 100-demo stride=2 门控，比 stride=1 快且保留抓取时序。若 100-demo native8 s2 仍低于 15，才追加数据：LoRA/小 adapter 目标 50-150 条高质量真实观测-动作 demo；全量/continued fine-tune 目标 300-500 条，覆盖 object_1/2/3、不同初始位置、不同抓取顺序。GraspGen/PCA 只能作为数据生成 oracle/候选器，最终训练数据必须是比赛观测和合法 8D env action，不能依赖 oracle object pose 做提交推理。
  • pi0.5 评估工具：demo/solution_pi05.py 是 websocket bridge；scripts/pi05/eval_task_e_pi05.py 是 Isaac 评估入口；scripts/pi05/run_pi05_eval_checkpoint.sh <checkpoint> 会启动 scripts/serve_policy.py，跑 seeds 11/12/13，seed11 录视频；scripts/pi05/summarize_pi05_eval.py 汇总分数；scripts/pi05/compare_pi05_vs_act_baseline.py 对比 XSA 基线并打印 HOLD_ACT_XSA 或 REVIEW_FOR_DEPLOY。100-demo 分支已经自动输出 HOLD_ACT_XSA。
  • AnyGrasp 下一步建议：AnyGrasp/AnyGrasp SDK 值得作为短线工程方案尝试，但它不是直接替代整套策略的“100% 一键方案”。推荐接法是：用 SAM3/任务先验 mask 限定目标点云区域，用 AnyGrasp 生成 6D grasp pose 和 collision-free candidate，再接 ATEC Piper 的 IK/轨迹规划、真实 finger center 接触、短稳定 basket-center release。优先验证 object_3，然后 object_1、object_2；严禁用 oracle object pose/mask 做最终提交。需要额外核查 AnyGrasp license、模型权重来源和比赛复现审查材料。
  • AnyGrasp 配置尝试，2026-05-30：官方 SDK 已克隆到 third_party/anygrasp_sdk；Python 3.11 二进制已放置为 third_party/anygrasp_sdk/grasp_detection/gsnet.so 和 third_party/anygrasp_sdk/grasp_detection/lib_cxx.so。Isaac/训练 Python 是 /home/ubuntu/envs/genmanip-isaac5-py311/bin/python，环境观测为 Python 3.11.15、torch 2.7.0+cu128、CUDA runtime 12.8、GPU NVIDIA RTX PRO 6000 Blackwell Workstation，open3d==0.19.0、graspnetAPI==1.2.11 可用，pointnet2._ext 需先 import torch 后再导入。SDK 依赖 libcrypto.so.1.1，当前用 Isaac ROS2 bridge 自带 OpenSSL 1.1 库临时解决：/home/ubuntu/projects/manipdojo2026/micromamba/envs/genmanip-sim/lib/python3.10/site-packages/isaacsim/exts/omni.isaac.ros2_bridge/humble/lib，运行时需把它放进 LD_LIBRARY_PATH。
  • AnyGrasp license/权重历史阻塞，2026-05-30，已在 2026-06-03 解除：官方 SDK 是授权二进制，license_checker -f 需要 ifconfig；本机没有系统 ifconfig，已加兼容包装 tools/anygrasp/ifconfig。当前机器 feature id 是 9024425463046318996。2026-05-30 时尚未拿到 licenseCfg.json 和 checkpoint_detection.tar，当时不能宣称 AnyGrasp 已能跑 ATEC 抓取；2026-06-03 已收到并安装 license/权重，见下方 smoke 记录。
  • AnyGrasp MinkowskiEngine 已安装，2026-05-30：官方 modified MinkowskiEngine 已克隆到 third_party/anygrasp_sdk/dependencies/MinkowskiEngine，分支 cuda-12-1。系统只有 GCC/G++ 13.3.0，会触发 CUDA 12.8 的 std::__to_address 歧义；已新建隔离编译器前缀 /home/ubuntu/envs/anygrasp-gcc11，内含 conda-forge GCC/G++ 11.4.0，并只 patch 该前缀的私有头文件 /home/ubuntu/envs/anygrasp-gcc11/lib/gcc/x86_64-conda-linux-gnu/11.4.0/include/c++/bits/shared_ptr_base.h，把两处 auto __raw = __to_address(__r.get()); 改成 auto __raw = std::__to_address(__r.get());，没有改系统 /usr/include。系统缺 BLAS devel symlink，已建项目内链接 third_party/anygrasp_sdk/dependencies/blas_lib/libblas.so -> /usr/lib/x86_64-linux-gnu/blas/libblas.so.3，并用本地 third_party/anygrasp_sdk/dependencies/blas_include/cblas.h 头文件。成功编译命令要点：CUDA_HOME=/usr/local/cuda-12.8、TORCH_CUDA_ARCH_LIST=12.0、MAX_JOBS=4、CC/CXX/CUDAHOSTCXX 指到 /home/ubuntu/envs/anygrasp-gcc11/bin/x86_64-conda-linux-gnu-gcc/g++，--blas_include_dirs=third_party/anygrasp_sdk/dependencies/blas_include，--blas_library_dirs=third_party/anygrasp_sdk/dependencies/blas_lib。安装后 /home/ubuntu/envs/genmanip-isaac5-py311/bin/python 可导入 MinkowskiEngine 0.5.4。
  • AnyGrasp 本地检查入口：tools/anygrasp/check_anygrasp_env.sh 会设置 PATH/LD_LIBRARY_PATH，检查 SDK 文件、torch/CUDA、Open3D、GraspNetAPI、pointnet2、MinkowskiEngine、feature id 和 checkpoint 缺口。2026-05-30 复查结果：gsnet.so、lib_cxx.so、open3d 0.19.0、graspnetAPI 1.2.11、pointnet2._ext、MinkowskiEngine 0.5.4 均 OK；仍缺官方 licenseCfg.json 和 checkpoint_detection.tar/AnyGrasp 权重。
  • AnyGrasp license/权重安装脚本，2026-05-30：拿到官方 license zip 和 checkpoint_detection.tar 后，运行 tools/anygrasp/install_license_and_checkpoint.sh <license_zip_or_dir> <checkpoint_detection.tar>；它会把 license 安装到 third_party/anygrasp_sdk/grasp_detection/license 和 third_party/anygrasp_sdk/grasp_tracking/license，把权重放到 third_party/anygrasp_sdk/grasp_detection/log/checkpoint_detection.tar，并调用 license_checker -c 校验。安装后用 tools/anygrasp/run_detection_demo.sh 跑官方 example data detection demo；该脚本当前已验证会在缺 license 时快速报错，不会误启动半截流程。
  • AnyGrasp license/权重已到位并跑通，2026-06-03：用户从 Chenxi Wang 收到 AnyGrasp SDK license 邮件，附件位于 /home/ubuntu/Downloads/license_KeweiChen.zip；detection 权重从官方 Google Drive 下载到 /home/ubuntu/Downloads/anygrasp_weights/checkpoint_detection.tar，大小约 283M。已运行 tools/anygrasp/install_license_and_checkpoint.sh /home/ubuntu/Downloads/license_KeweiChen.zip /home/ubuntu/Downloads/anygrasp_weights/checkpoint_detection.tar，安装到 third_party/anygrasp_sdk/grasp_detection/license、third_party/anygrasp_sdk/grasp_tracking/license 和 third_party/anygrasp_sdk/grasp_detection/log/checkpoint_detection.tar。license_checker 打印 KeweiChen.lic check passed，虽然检查命令返回码偶发非 0，但 SDK 真实加载时 license passed: True, state: FvrLicenseState.PASSED。
  • AnyGrasp detection smoke 结果，2026-06-03：修复 pointnet2 安装状态后，/home/ubuntu/envs/genmanip-isaac5-py311/bin/python 可导入 pointnet2._ext、pointnet2.pointnet2_utils、pointnet2.pointnet2_modules。运行 tools/anygrasp/run_detection_demo.sh 成功完成官方 grasp_detection/example_data 推理，输出 20 个 grasp scores，最高分约 0.47641015。这证明 AnyGrasp SDK detection 已在本机可用；下一步可以写 ATEC bridge，从 Isaac RGB-D/点云裁剪出目标区域，调用 AnyGrasp 生成 grasp pose，再接 Piper IK/执行 primitive。
  • AnyGrasp ATEC bridge 已接入，2026-06-03：新增 scripts/graspnet_task_e/anygrasp_adapter.py，scripts/graspnet_task_e/run_graspnet_pick.py 支持 --grasp_provider anygrasp，封装入口是 scripts/graspnet_task_e/run_anygrasp_pick.sh <object> <seed>。adapter 在 Isaac Python 进程内预加载 ROS2 bridge 自带 libcrypto.so.1.1 / libssl.so.1.1，并把 tools/anygrasp/ifconfig 注入 PATH，否则 SDK 会出现 OpenSSL 或 machine-id/license 检查问题。检测器纯加载 smoke：/home/ubuntu/envs/genmanip-isaac5-py311/bin/python -c 'from scripts.graspnet_task_e.anygrasp_adapter import _load_anygrasp_detector; _load_anygrasp_detector()'，应看到 license passed: True。
  • AnyGrasp Task-E smoke 结果，2026-06-03：object_3 在 --use_task_quat --tcp_z_offset 0.040 --close_z_offset -0.020 下成功，inside=True、z_gain_lift=0.137，视频 logs/videos/task_e_anygrasp/anygrasp_obj3_seed11_taskquat_lowclose.mp4；object_2 同参数成功，inside=True，视频 logs/videos/task_e_anygrasp/anygrasp_obj2_seed11_taskquat_lowclose.mp4。object_1 当前失败两轮：anygrasp_obj1_seed11_taskquat_lowclose_retry.mp4 和 anygrasp_obj1_seed11_defaultquat_nooffset_lowclose.mp4，共同现象是 SDK 只给低分/极窄 width 候选（约 0.006-0.008m），夹爪闭合但物体没有随 lift 上升。不要把 AnyGrasp 当前 bridge 误当作三物体满分策略；它已完成接入验证，但 object_1 仍需沿既有 Task-E 校准成功模板继续调。
  • AnyGrasp 使用建议，2026-06-03：当前最稳用法是把 AnyGrasp 作为 RGB-D grasp candidate/center/yaw prior，再强制接 Task-E 已验证执行参数（--use_task_quat、低 close z、finger-center/staged transport、basket stable release）。不要直接使用 AnyGrasp raw 6D wrist pose；object_3 raw pose 曾失败，换 Task-E quaternion 后才成功。object_1 后续优先复用旧 GraspNet 成功证据里的控制模板：upper-surface median 执行中心、加 OBJ_GRASP_CENTER_OFFSETS[1]、--close_z_offset -0.020、keep grasp quat through transport/place/open、长 transport 和真实 finger-center servo。
  • GraspGen/AgileX 第三方路线复查，2026-06-03：联网查到 AgileX 社区/教程推荐 PiPER 的 GraspGen 路线，AgilexRobotics/GraspGen 链接当前不可访问，但可访问镜像/作者仓库 vanstrong12138/GraspGen 有 ros2_jazzy_version 分支；已浅克隆到 third_party/GraspGen。AgileX 官方教学仓库 agilexrobotics/Agilex-College 已浅克隆到 third_party/Agilex-College，其 piper/GraspGen README 描述了 PiPER + YOLOv8-Pose/TensorRT + PCA/AABB + IK/夹爪控制的流程。核心代码在 third_party/GraspGen/sam3/realsense-sam.py::compute_grasp_from_pca_aabb()：3D PCA -> 主轴系 AABB -> 最短边作为夹持方向。
  • GraspGen-style PCA/AABB ATEC 接入，2026-06-03：新增 scripts/graspnet_task_e/pca_aabb_adapter.py，run_graspnet_pick.py 支持 --grasp_provider pca。object_1 smoke 命令使用 --grasp_provider pca --object 1 --seed 11 --mask_provider oracle --use_task_quat --tcp_z_offset 0.040 --close_z_offset -0.020 --close_steps 160 --move_steps 200 --transport_steps 1400 --place_steps 260，输出 [PCA_AABB] points=5231 extents=(0.140,0.122,0.056) axis=2 width=0.056，说明几何法给出的可夹宽度合理，明显不同于 AnyGrasp 的 0.006-0.008m 异常窄 width。但该次仍失败：pick=(0.950,0.261)、z_gain_lift=-0.024、inside=False，视频 logs/videos/task_e_pca/pca_obj1_seed11_taskquat_lowclose.mp4。原因判断：单视角 RGB-D/mask 只看到 object_1 可见侧面，PCA/upper-surface median 的执行中心偏向左侧面；需要结合已知物体尺寸/多视角/learned completion/旧 GraspNet 成功中心补全到真实夹持中心。
  • object_1 方块“戳侧面、不含入夹爪”修复，2026-06-03：用户根据视频指出方块在左侧位置时 Piper 夹爪只是顶/戳方块，没有让方块进入两指之间再闭合。复测确认：PCA/AABB 加 --preclose_insert_dx 0.040 --close_z_offset -0.020 仍失败，z_gain_lift=-0.025，trace 里 close 阶段 min_finger_dist≈0.058m，说明指尖中心离目标太远；失败视频 logs/videos/task_e_pca/pca_obj1_seed11_insert_dx004.mp4。真正有效修复是不要手动压低 close height，而是对齐 Task-E 采集器 object_1 参数：OBJ_GRASP_CENTER_OFFSETS[1]=(0.025,0,0)、OBJ_GRASP_Z_OFFSETS[1]=0.072、OBJ_CLOSE_Z_OFFSETS[1]=0.020，并使用实际 link7/link8 finger-center servo。成功命令：OMNI_KIT_ACCEPT_EULA=YES PYTHONUNBUFFERED=1 /home/ubuntu/envs/genmanip-isaac5-py311/bin/python scripts/graspnet_task_e/run_graspnet_pick.py --grasp_provider pca --object 1 --seed 11 --mask_provider oracle --use_task_quat --tcp_z_offset 0.072 --close_steps 180 --move_steps 200 --transport_steps 1400 --place_steps 260 --video_path logs/videos/task_e_pca/pca_obj1_seed11_cfgclose.mp4 --save_debug_npz logs/pca_task_e/obj1_seed11_cfgclose_debug.npz --headless；结果 inside=True、z_gain_lift=0.144，trace reach min_finger_dist≈0.006m、close ≈0.024m。结论：object_1 失败不是“夹爪力不够”或“必须换 AnyGrasp”，而是 TCP/指尖中心/闭合高度标定错误导致的预闭合碰撞。run_graspnet_pick.py 已把 preclose_insert stats 纳入 [TRACE]，后续若继续扫参数，必须看 reach/insert/close/lift 的 finger-center 误差再判断。
  • demo/solution_pca.py object_1 submit-style 修复进展，2026-06-04：不要覆盖 demo/solution.py/ACT 基线，solution_pca.py 仍是实验分支。最新单物体 seed11/12/13 均已成功入篮：seed11 曾输出 score=6.00、initial (1.0945,0.2979,0.8833)、final (1.0582,-0.3256,0.8820)；最新 tight 参数下 seed12 score=6.00、final (1.0338,-0.2790,0.8584)，seed13 score=6.00、final (1.0257,-0.2567,0.8871)。验证入口：ATEC_PCA_OBJECTS=1 OMNI_KIT_ACCEPT_EULA=YES PYTHONUNBUFFERED=1 /home/ubuntu/envs/genmanip-isaac5-py311/bin/python scripts/graspnet_task_e/debug_solution_pca_execution.py --seed <11|12|13> --object 1 --max_steps 4800 --headless。关键参数：object_1 禁用 ROI fill-hole 点云中心；可见面中心补全 OBJ_CENTER_COMPLETION_OFFSETS[1]=(0.020,0.0)；OBJ_GRASP_CENTER_OFFSETS[1]=(0,0)；reach 阶段不要用低位 finger-z servo；object_1 release/open 高度用 runner 标定 TABLE_TOP_Z+0.32/+0.24；OBJ1_HOLD_GAP=0.0415，OBJ_LIFT_STEPS[1]=300，close/lift/mid 首次到 gap 阈值后记录 _obj1_hold_grip，后续 lift/mid/release/settle 保持该夹爪开度，避免闭到 0 把方块挤飞。错误经验：OBJ_PLACE_XY_OFFSETS[1]=(0,-0.250) 会让 release 目标变成 y=-0.55，导致物体掉在篮子前；object_1 rescue 的宽 ROI/白色网格容易误检，默认应关闭，除非重新做可靠前景分割。
  • demo/solution_pca.py object_2/3 状态，2026-06-04：object_2/3 仍未达到可提交稳定性，不要宣称 PCA 方案已满分。runner 对照中 object_3 seed11 曾成功一次，但 2026-06-04 复测同类 runner 参数也失败，说明旧成功不是稳态。5 月 20 日 obj321_servo 采集器日志证明官方 state_machine/真实 object root 下三物体可满分，object_3 成功 trace 常见 z_gain≈0.42、gap_close≈0.063-0.069、finger_close≈0.016-0.030、finger_vec_close.x≈-0.01~-0.03、basket_inside=True。submit-style object_3 关键新进展：低位/高位默认均不稳，问题从“完全不接触”推进到“可强抬但运输滑落”；当前实验默认值调为 OBJ_CLOSE_Z_OFFSETS[3]=0.030、OBJ3_HOLD_GAP=0.055、OBJ_FINGER_XY_OFFSETS[3].x=-0.030、OBJ_LIFT_STEPS[3]=40，能在部分 seed11 placement 产生强抬升，例如 logs/pca_solution_obj3_fxneg003_full_seed11_20260604.log 输出 score=3.00、max_z_gain=0.195，短 lift 日志 logs/pca_solution_obj3_shortlift_seed11_20260604.log 输出 max_z_gain=0.236；但 mid/release 阶段仍滑回桌面，未入篮。失败边界：hold_gap=0/0.045/0.060/0.065、close_z=-0.020/0/0.020/0.050/0.070/0.090、PCA yaw、ATEC_PCA_USE_FULL_IK=1、ATEC_PCA_OBJ3_OBJECT_SERVO=1 均未稳定解决。下一步不要再质疑是否“完全没夹到”；应聚焦 object_3 运输保持/低位转移：抓到后立即横向、减少纯竖直 lift、或做 closed-drag/under-scoop 入篮。object_2 仍需单独重新对齐，不要套 object_1/object_3 的补偿。
  • demo/solution_pca.py object_3 滑落修复追加，2026-06-04 18:20 CST：按用户方案已在实验分支实现“短抬升 + 动态视觉 object-servo + 低位 fallback”方向，但 seed11 仍未过，不要部署。当前文件改动包括：OBJ3_HOLD_GAP=0.066、OBJ3_HOLD_GRIP_DEFAULT=(0.032,-0.034)、OBJ3_CLOSE_MIN_STEPS=160、OBJ3_LOW_HOLD_STEPS=140、OBJ3_APPROACH_FINGER_Z=TABLE_TOP_Z+0.090、OBJ3_CLOSE_FINGER_Z=TABLE_TOP_Z+0.010、OBJ3_LIFT_FINGER_Z=TABLE_TOP_Z+0.080、OBJ3_OBJECT_SERVO_GAIN=0.12、OBJ3_OBJECT_SERVO_MAX_XY=0.040、OBJ3_FINGER_SERVO_MAX_XY=0.080，并把 object_3 fallback 改成更早在 obj3_mid 停滞时触发，OBJ3_FALLBACK_DRAG_Z=TABLE_TOP_Z+0.035、fallback steps 60/260/260/100。注意：不要 freeze 实际 qpos 作为 object_3 hold grip，之前会锁住非对称夹爪开度如 (0.035,-0.026)；应使用标定默认 hold grip。不要只靠 gripper_base z，object_3 reach/close/lift 需要显式 finger-center z。
  • object_3 最新 trace 边界，2026-06-04：logs/pca_solution_fix/obj3_seed11_runner_vec_match_debug_20260604_181342.log 证明旧默认 OBJ_FINGER_Y_OFFSET=+0.003 会让 close finger-center 落在香蕉中心 y 侧约 +0.024m，把香蕉侧向挤开；已把默认 y offset 改成 -0.020。logs/pca_solution_fix/obj3_seed11_yoffset_neg020_debug_20260604_181533.log 证明 y 回正，但 x 可能过负；临时覆盖 ATEC_PCA_OBJ3_FINGER_X_OFFSET=-0.004 ATEC_PCA_OBJ3_FINGER_Y_OFFSET=-0.020 的 logs/pca_solution_fix/obj3_seed11_xyoffset_xneg004_yneg020_debug_20260604_181706.log 得到较接近 runner 的 close 向量：实际 finger 约 (x=+0.002,y=-0.015,z=-0.014) 相对当前视觉中心，但仍 score=0，说明问题已从“finger 没到位”推进到“闭合后没有形成可携带接触/视觉中心可能被夹爪遮挡干扰”。低位早触发 fallback logs/pca_solution_fix/obj3_seed11_early_low_fallback_20260604_181908.log 只把 object_3 最终推到 (1.083,0.055,0.841)，x 接近篮筐但 y 仍离篮筐中心 +0.355m；当前 closed-drag 高度/接触方向仍推不动香蕉。
  • object_3 下一步建议，2026-06-04：继续主攻 submit-style 规划抓取，不要回退 ACT/训练，也不要改官方物理。优先实现并验证真正的 table-level under-scoop/sweep：从当前视觉中心的 +y 一侧或弧线外侧进入，finger z 再低于 TABLE_TOP_Z+0.035 小步扫向 BASKET_CENTER_Y，同时保留 object_3 当前中心重估；fallback 要在 obj3_mid 更早触发并保证 1500 step 内完成。若继续调夹持，目标 trace 仍以 runner 成功 logs/graspnet_task_e_obj3_sam3_closed_drag.log 为准：inside=True、z_gain_lift=0.207、reach/close/lift min_finger_vec≈(-0.007~-0.010, -0.002~+0.002, -0.014~-0.011)、gap≈0.066-0.068。任何新参数必须用 ATEC_PCA_DEBUG_TARGET=1 或 debug_solution_pca_execution.py 看 finger-center/object-center 数值，不要只看视频判断。
  • object_3 用户指定“短抬升 + 低位 cradle/servo + 篮筐稳定释放”实现记录，2026-06-04 20:35 CST：已在 demo/solution_pca.py 实验分支实现，但仍未达到可部署。关键改动：OBJ3_HOLD_GAP=0.0675、OBJ3_CLOSE_STEPS=90、OBJ3_LIFT_STEPS=35、OBJ3_LOW_HOLD_STEPS=0、OBJ3_LIFT_FINGER_Z=TABLE_TOP_Z+0.045、OBJ3_FINGER_X_OFFSET=-0.010；hold grip 改为只在 obj3_lift 且 gap 达阈值后 latch，避免 close 阶段过早锁住；obj3_basket_hold 修掉一个明确 bug：不要把 dynamic_finger_xy 设成当前香蕉中心 c_now，应保持 object_target_xy + carry_offset，否则手会被拉回旧位置。验证日志：logs/pca_solution_fix/obj3_seed11_eval_close90_shortlift_fasttransport_20260604_201931.log 显示 latch 成功但 basket_hold desired 错误，最终 object_3 仍在 y≈0.068；修复后 logs/pca_solution_fix/obj3_seed11_eval_fixservo_transport440_20260604_202219.log 和 ...fingerik_20260604_202355.log 仍卡在 finger y≈-0.09；低位 closed-drag 版本 logs/pca_solution_fix/obj3_seed11_eval_low_closed_drag_20260604_202611.log 也只到 finger y≈-0.09，object 未进篮；position-only DLS logs/pca_solution_fix/obj3_seed11_eval_lowdrag_positiononly_*.log 可让 gripper_base 更靠近篮筐，但 finger z 飘高到 ≈1.0m，接触丢失。结论：当前 object_3 抓取位姿/短抬升已经可做到，主 blocker 是 submit-side Pinocchio DLS/运输控制没有复现 runner/collector 的 Isaac CartesianController 真实 PhysX Jacobian 行为，尤其是篮筐方向 y 运动和 finger z 保持；不要再把首要问题归咎为 GraspNet/AnyGrasp 候选模型。
  • object_3 后续最短路径，2026-06-04 20:35 CST：优先复刻 IsaacLab CartesianController 的动作生成细节，而不是继续换 grasp 模型。已确认官方 get_action_spec() 只允许改 action group 的 mode/scale/clip，不能直接提交 Cartesian action；若用它，只能尝试 arm velocity/scale 诊断，不能根治位姿控制。下一步建议：1) 用 scripts/graspnet_task_e/debug_solution_pca_execution.py 增加卡住阶段 q6/关节限位打印，确认是 joint limit 还是 DLS/rotation error；2) 将 demo/solution_pca.py 的 _rot_error/DLS 阻尼/每步 clamp 对齐 source/atec_rl_lab/atec_rl_lab/utils/cartesian_controller.py，优先用 position+current wrist quat 而不是强制 topdown；3) 若仍不行，做一个 small joint-space transport library：由 runner 在若干 object_3 y-band 初始位置生成相对 joint waypoints，submit 通过视觉中心选择/插值，而不是每步在线求 Cartesian IK。保持 demo/solution.py / demo/policy_act.pt ACT/XSA 基线不动，solution_pca.py 只作实验分支。
  • PCA/AABB + RGB-D band mask 三物体单体 smoke，2026-06-03：目标是避开 AnyGrasp SDK 的不可分发限制，形成可复现的几何抓取替代路线。scripts/act/task_e/config.py 的 OBJ_SPAWN_Y_BANDS 已对齐官方 source/atec_rl_lab/atec_rl_lab/tasks/task_e/env_cfg.py：object_1 [+0.25,+0.29]、object_2 [+0.14,+0.20]、object_3 [+0.03,+0.09]；scripts/graspnet_task_e/tuntun_adapter.py 的 rgbd_band_object_mask() 加了 object-specific z gate，避免 table/arm/篮子点混入。object_1 band 成功命令：... run_graspnet_pick.py --grasp_provider pca --object 1 --seed 11 --mask_provider band --use_task_quat --tcp_z_offset 0.072 --close_steps 180 --move_steps 200 --transport_steps 1400 --place_steps 260 --video_path logs/videos/task_e_pca/pca_obj1_seed11_band_cfgclose_yband_zfilter.mp4 ...，结果 inside=True、z_gain_lift=0.144。object_2 在官方 y-band 收窄后复测通过：... --object 2 --seed 11 --mask_provider band --use_task_quat --tcp_z_offset 0.040 --close_z_offset -0.020 --transport_steps 1400 --place_steps 260 --video_path logs/videos/task_e_pca/pca_obj2_seed11_band_yalign_taskquat_lowclose.mp4 ...，结果 inside=True、z_gain_lift=0.119。object_3 初始 band/PCA 能抓起但高空 release 会滑出；修复是在 scripts/graspnet_task_e/pca_aabb_adapter.py 中对 object_index==3 使用 oriented AABB center 而非 upper-surface median，并在 run_graspnet_pick.py 增加 --open_release_z 支持“高位运输、低位开爪”。object_3 seed11 AABB-center 高位 release 可成功一次：logs/videos/task_e_pca/pca_obj3_seed11_band_aabbcenter_taskquat_lowclose.mp4，结果 inside=True、z_gain_lift=0.161；seed12 高位 release 会滑出，改用 --release_z 0.32 --open_release_z 0.24 --place_steps 220 后成功，视频 logs/videos/task_e_pca/pca_obj3_seed12_band_aabbcenter_highcarry_lowopen.mp4，结果 inside=True、最终篮内 delta 约 (-0.031,-0.049)。注意：这些是单物体 runner 验证，仍需串成三物体连续 episode 并迁移到 demo/solution.py 的 observation-only 控制逻辑；当前 run_graspnet_pick.py 仍会读取 env object state 做验证/动态 servo，不可直接当作最终提交。
  • Submit-style PCA controller debug，2026-06-04：demo/solution_pca.py 是实验分支，未部署；demo/solution.py 仍是 ACT/XSA baseline。scripts/act/eval_task_e_act.py 已支持 --solution_module solution_pca，scripts/graspnet_task_e/debug_solution_pca_execution.py 可用 Isaac internals 量化 finger_dist/max_z_gain/pin_mean_err。已确认 Pinocchio FK 与 Isaac link7/link8/gripper_base 对齐，pin_mean_err finger=0.0000 gb=0.0000，所以“根本没去夹/没夹起”的主因不是 FK 坐标系错误，而是 submit-style IK/控制器没有复现 run_graspnet_pick.py 的 Isaac CartesianController 低位接触能力。
  • Submit-style object_1 当前状态，2026-06-04：已尝试把 run_graspnet_pick.py 的 object_1 成功模板迁移到 demo/solution_pca.py，包括 RGB-D world AABB 中心补全、OBJ_CENTER_COMPLETION_OFFSETS[1]=(0,+0.018)、object_1 x 执行 offset 从 +0.025 收到 +0.010、低 close plane TABLE_TOP_Z+0.030、以及 direct finger-center IK。结论：感知中心可做到厘米级，但当前 Pinocchio IK 在 close 阶段仍常把 link7/link8 finger-center 卡在物体上方约 +0.05m，max_z_gain≈0，object_1 未抬起。direct finger-center IK 和强制 object_1 task quaternion (0,0.707,0.707,0) 反而更差，已回到 gripper_base compensation 路线；不要误判为 Grasp/PCA/AnyGrasp 本身失败。下一步应复刻 Isaac CartesianController 的 differential IK 数学/参数，或在 submit policy 内实现更可靠的 DLS resolved-rate joint update，而不是继续换抓取模型。
  • Submit-style object_1 preclose insert 更新，2026-06-04：demo/solution_pca.py 新增 _PiperIK.step_dls()、runner-style finger-center XY correction、object_1 task quaternion [0,0.709,0.705,0]、object_1 relative-z correction、preclose insert 和 object_1 release overshoot。视频 logs/videos/task_e_pca_solution/pca_solution_obj1_seed11_best_trace_20260604_114715.mp4 明确显示无 insert 时夹爪嘴停在方块侧边，抬起时方块留在桌面。加入 insert 后，OBJ_PRECLOSE_INSERT_OFFSETS[1]=(0,-0.040) 曾得到 score=3、max_z_gain=0.137m，但运输中滑落到 y≈-0.047；(0,+0.025) 曾得到 max_z_gain=0.193m、最终 y≈-0.083，仍未进篮，另一次随机位置未抓起。结论：insert 方向/幅度是有效抓起的关键，但 object_1 仍未稳健；当前 demo/solution_pca.py 仍是 debug-only，不要覆盖 demo/solution.py 或 demo/policy_act.pt。
  • GraspNet object_1 对照，2026-06-03：尝试复跑旧已知成功命令，视频目标 logs/videos/task_e_graspnet/graspnet_pick_obj1_seed11_recheck_20260603.mp4，进程在 GraspNet 推理/候选生成阶段超过 3 分钟无 [GRASP] 输出，占 GPU 约 6.6GB 且 CPU 高负载，已手动 kill。该次不是抓取成功/失败证据；后续若要继续对比 GraspNet，先排查为什么当前 recheck 推理阶段变慢/卡住。
  • AnyGrasp 第三方参考结论，2026-05-30：未找到松灵/AgileX 官方 AnyGrasp for Piper 项目；AgileX 官方主要提供 PiPER Python/ROS/ROS2/URDF/仿真控制栈。第三方/论文路线支持“AnyGrasp 作为 pre-contact/contact-pose prior，再接机器人 IK/轨迹规划”：DKT 透明物体工作报告过 RealSense D435 + PiPER Arm + AnyGrasp + CuRobo 抓取栈；BayesVLA 把策略拆成视觉动作 prior 和语言 likelihood，并把 pre-contact 阶段交给 AnyGrasp 这类 frozen contact-pose model。对本项目的启发：AnyGrasp 应先做候选抓取姿态生成和 ranking，ATEC Piper 仍需要单独处理 TCP 标定、IK/trajectory、夹爪闭合/运输防滑、篮筐中心高位稳定 release。
  • 当前工作建议排序：1) 保持 demo/policy_act.pt 的 XSA-ACT 基线不动；2) 若追短期提分，优先把 AnyGrasp/SAM3 作为 grasp candidate generator 接入现有 calibrated Piper primitive，而不是继续盲训 pi0.5；3) 若继续 VLA，重做 OpenPI/Piper embodiment adapter（native Piper dims/cameras/norm/discrete_state_input），不要再使用 fixed-base 16D padding 当正式结论；4) 每次部署前必须独立进程评估 seeds 11/12/13，至少保留一个视频和 [BASKET] 物体入篮日志。

• Latest status, 2026-05-20 20:55 CST:

  • Supersedes the older "object 1/2 unresolved" notes below; keep them only as historical debugging context.
  • L0 Task E three-object scripted data generation now works well enough to collect demos. Verified all-object order is 3 2 1 with --only_success --trace --abort_failed_lift.
  • Verified object-1-only success: datasets/atec_task_e_probe_obj1_graspoffset_servo/trajectory.hdf5, 3/3 successful demos; trace z-gain about 0.387-0.417, basket_inside=True.
  • Verified object-2-only success: datasets/atec_task_e_probe_obj2_original_rootservo/trajectory.hdf5, 3/3 successful demos; trace z-gain about 0.302-0.304, basket_inside=True.
  • Verified three-object success: datasets/atec_task_e_probe_obj321_final_servo/trajectory.hdf5, 3 successful demos in 4 attempts; success traces have object_1/2/3 basket_inside=True.
  • Review video: logs/videos/task_e_obj321_final_servo/demo_0000.mp4; keyframe summary: logs/videos/task_e_obj321_final_servo/summary_5s.jpg.
  • Root cause/fix: data generation was wrong, not ACT training. Object 1 must keep the finger-centre servo target on obj_pos + OBJ_GRASP_CENTER_OFFSETS[1]; object 2 must track the USD/root object position, not the offset center. Both need closed-loop finger-centre servo during REACH/CLOSE/LIFT/TRANSPORT/PLACE.
  • Stable constants in scripts/act/task_e/config.py: object 1 offset (0.025,0,0), grasp z 0.072, close z 0.020, transport/place steps 2200/300; object 2 offset (0.060,0,0), grasp z 0.135, transport/place steps 2200/300; default PICK_OBJECTS="3 2 1".
  • scripts/act/task_e/collector.py has the important per-object servo distinction: object 1 servo/trace target uses obj_pos + OBJ_GRASP_CENTER_OFFSETS; object 2/3 use obj_pos. Do not "simplify" this back to one formula.
  • scripts/act/train_task_e.py uses lr_drop = max(int(2/3 * total_iters), 1) so tiny smoke tests no longer fail with StepLR(step_size=0).
  • CAP-X / RoboTwin lesson: use contact/TCP calibrated grasp candidates and closed-loop place logic first. GraspNet/ContactGraspNet can be added later as a candidate generator, but it is not required to fix this current oracle data bug.
  • Real pipeline command: DATA_DIR="datasets/atec_task_e_obj321_servo_100demos" PICK_OBJECTS="3 2 1" NUM_DEMOS=100 MAX_ATTEMPTS=260 TOTAL_ITERS=100000 BATCH_SIZE=512 SEED=1 LOG_FREQ=100 SAVE_FREQ=5000 RUN_NAME="act-task-e-rgb-obj321-servo-100demos-seed1" bash scripts/act/run_task_e_pipeline.sh.
  • Active long run started via systemd user unit because plain background/nohup could be killed with the Codex command: unit atec-task-e-obj321-servo-20260520211650.service, log logs/task_e_obj321_servo_pipeline_20260520_211650.log, latest symlink logs/task_e_obj321_servo_pipeline.latest.
  • As of 2026-05-20 21:22 CST the long run is active, Demo 1/100 succeeded and wrote datasets/atec_task_e_obj321_servo_100demos/trajectory.hdf5 (~1.3 GB), then proceeded to Demo 2/100.

• Latest train/eval status, 2026-05-21 14:55 CST:

  • atec-task-e-obj321-servo-20260520211650.service completed normally; raw dataset is datasets/atec_task_e_obj321_servo_100demos/trajectory.hdf5 (130 GB), filtered dataset is trajectory_filtered.hdf5 (35 GB), run is runs/act-task-e-rgb-obj321-servo-100demos-seed1.
  • Checkpoints include best_loss.pt and final.pt; training reached 100000 iters, final logged loss around 0.0100.
  • First ACT eval of best_loss.pt scored 0/3 episodes; video logs/videos/task_e_act_eval/obj321_best_loss_100demos_20260521_145104.mp4 shows the arm staying near the basket/home pose instead of starting the object grasp.
  • Root cause found in demo/solution_act.py: the home stage treated env action as PD/velocity, but Task E action is (joint_target - default_joint_pos) / 0.5. Fix is to command the static teleop-home target action until stable, then switch to ACT. Re-run eval after this fix before judging model quality.
  • After the home fix, best_loss.pt eval improved: 1-episode score 9.00 with video logs/videos/task_e_act_eval/obj321_best_loss_homefix_1ep_20260521_145529.mp4; same-process 3-episode eval had valid first episode score 18.00 and video logs/videos/task_e_act_eval/obj321_best_loss_homefix_3ep_20260521_145652.mp4, but subsequent episodes reset to done=True at step 1, so do not treat that mean score as independent.
  • final.pt single-episode eval scored 6.00, so deploy best_loss.pt.
  • Deployment: previous demo/policy_act.pt backed up as demo/policy_act.prev_20260521_145951.pt; current demo/policy_act.pt is copied from runs/act-task-e-rgb-obj321-servo-100demos-seed1/checkpoints/best_loss.pt and sha256 matches 76bbef2c81c85fc6cc60448476ff09075ec8bc97217c2d2fb70a3253fd3e307d.

• XSA ACT experiment, 2026-05-21:

  • Local XSA package source: /home/ubuntu/Documents/01Proj/13atec/xsa_update_20260520/lerobot_policy_act_xsa.zip; it is a LeRobot act_xsa plugin with 12-dim example config and is not directly runnable for ATEC Task E's 8-dim Piper setup.
  • Minimal XSA adaptation added behind --use_xsa: source/atec_rl_lab/atec_rl_lab/train/act/act/detr/transformer.py and demo/act/detr/transformer.py support XSATransformerEncoderLayer / XSATransformerDecoderLayer; default ACT remains unchanged.
  • scripts/act/train_task_e.py now saves model_args in checkpoints; demo/solution_act.py reads model_args including use_xsa, so ordinary ACT and XSA checkpoints can both load.
  • Smoke test passed: runs/act-task-e-rgb-xsa-smoke-10demos-seed7/checkpoints/final.pt loads as XSATransformerEncoderLayer.
  • Active XSA comparison run: systemd user unit atec-task-e-xsa-30k-20260521165009.service, log logs/task_e_xsa_30k_20260521_165009.log, run name act-task-e-rgb-xsa-100demos-30k-seed11, command uses --use_xsa --total_iters 30000 --batch_size 256 --num_demos 100.
  • Mid-training evals are noisy but not yet encouraging: 19k-ish snapshot scored 9/18 with video logs/videos/task_e_act_eval/xsa_mid_best_1ep_20260521_174124.mp4; later current-best snapshot at 20260521_175038 scored 0/18 with video logs/videos/task_e_act_eval/xsa_current_best_1ep_20260521_175038.mp4. Do not infer ranking from training loss alone; run independent single-episode processes because same-process multi-episode reset can be invalid after success.

• GraspNet / TunTunClaw integration, 2026-05-21:

  • User reference cloned into third_party/tuntunclaw/ from Datawhale/OpenClaw home assistant material. Official GraspNet checkpoint is third_party/tuntunclaw/temp/logs/log_rs/checkpoint-rs.tar.
  • Local bridge files: scripts/graspnet_task_e/tuntun_adapter.py and smoke runner scripts/graspnet_task_e/run_graspnet_pick.py.
  • Dependencies were made to work in /home/ubuntu/envs/genmanip-isaac5-py311/bin/python: open3d==0.19.0, local graspnetAPI, transforms3d==0.4.2, and the GraspNet pointnet2 CUDA extension built for Blackwell with TORCH_CUDA_ARCH_LIST=12.0.
  • Important lesson: raw GraspNet point/orientation alone did not lift object 1. The first successful object-1 smoke used GraspNet/TunTunClaw for candidate generation, but executed with Task-E calibrated controls: robust mask upper-surface median as execution center, object-1 OBJ_GRASP_CENTER_OFFSETS added, --close_z_offset -0.020, finger-center servo, keep grasp quaternion through transport/place/open, and long transport.
  • Known-good object-1 command: python scripts/graspnet_task_e/run_graspnet_pick.py --object 1 --seed 11 --tcp_z_offset 0.040 --close_z_offset -0.020 --use_task_quat --close_steps 160 --move_steps 200 --transport_steps 1400 --place_steps 260 --headless --device cuda:0 --video_path logs/videos/task_e_graspnet/graspnet_pick_obj1_seed11_keepquat_slow.mp4 --save_debug_npz logs/graspnet_task_e/obj1_seed11_keepquat_slow_debug.npz.
  • Verified result for that command: inside=True, z_gain_lift=0.164, final object_1 position about (1.043,-0.247,0.934), basket delta (-0.037,+0.053). Video: logs/videos/task_e_graspnet/graspnet_pick_obj1_seed11_keepquat_slow.mp4.
  • Object 3 GraspNet smoke also succeeded with refined AABB oracle mask and lower close height: command uses --object 3 --tcp_z_offset 0.055 --close_z_offset 0.015 --use_task_quat --place_x_offset 0.04 --place_y_offset 0.05; result inside=True, z_gain_lift=0.188, video logs/videos/task_e_graspnet/graspnet_pick_obj3_seed11_lowclose.mp4.
  • Object 2 GraspNet smoke is not solved yet. Best attempts can move/lift slightly (z_gain_lift≈0.07-0.10) but final y remains outside basket; simple --post_push moved it only to about y=-0.056. Keep using the existing Task-E calibrated state-machine/ACT path for object 2 until a better GraspNet contact primitive is found.
  • Added legal-ish RGB-D y-band mask provider rgbd_band_object_mask() and runner flag --mask_provider band; it avoids sim object state by using camera intrinsics, depth, table height, and official spawn y-bands. Current band-mask object-3 test did not lift yet, so the GraspNet success videos above still rely on oracle_object_mask()/AABB debug masks. Do not use oracle masks in final submission without replacement.
  • Three-object stable oracle/state-machine verification after GraspNet changes: scripts/act/collect_demos_task_e.py --pick_objects 3 2 1 --num_demos 1 --max_attempts 3 --only_success --trace --abort_failed_lift --save_video ... succeeded on attempt 2, with all object_1/2/3 basket_inside=True. Video: logs/videos/task_e_obj321_current_verify/demo_0000.mp4.

• SAM3 segmentation / basket-center release, 2026-05-21:

  • Local SAM3 checkpoint exists at /home/ubuntu/Documents/01Proj/sam3d_gs/submodule/Prompt-Inpaint/checkpoints/sam3.pt; SAM3 is installed in /home/ubuntu/Documents/01Proj/sam3d_gs/.venv/bin/python, not in the Isaac env. Keep SAM3 isolated as a subprocess rather than installing it into /home/ubuntu/envs/genmanip-isaac5-py311.
  • Added scripts/graspnet_task_e/sam3_segment_image.py: standalone RGB image -> SAM3 binary mask. It uses local Prompt-Inpaint SAM3Predictor and writes .npy mask plus optional JSON metadata.
  • scripts/graspnet_task_e/run_graspnet_pick.py now supports --mask_provider sam3, --sam3_prompt, --sam3_threshold, and --sam3_python. It also defaults to --basket_center_release: during transport/place/open it servo-corrects the EE target by current object center error so the object center is driven to (BASKET_CENTER_X, BASKET_CENTER_Y) before opening.
  • SAM3 smoke on saved frames passed for all three prompts: object 1 best prompt box score 0.863, mask 31206 px; object 2 best prompt yellow bottle score 0.961, mask 2509 px; object 3 best prompt banana score 0.953, mask 1311 px. Overlays are logs/graspnet_task_e/sam3_smoke_obj{1,2,3}_overlay.png.
  • First live object-3 SAM3+GraspNet smoke was blocked before environment creation by ModuleNotFoundError: scripts.act; fixed by explicitly inserting repo root into sys.path at the top of run_graspnet_pick.py. Re-run live smoke after this fix.
  • Live object-3 SAM3+GraspNet with default high close failed (z_gain_lift=0.000). Lowering close offset fixed the contact: command used --mask_provider sam3 --object 3 --seed 11 --tcp_z_offset 0.055 --close_z_offset -0.005 --use_task_quat --basket_center_release; result inside=True, z_gain_lift=0.211, final (0.995,-0.254,0.852), basket delta (-0.085,+0.046). Video: logs/videos/task_e_graspnet/graspnet_pick_obj3_sam3_basket_center_lowclose.mp4.
  • run_graspnet_pick.py now has a local GraspNet-only default close offset for object 3 (GRASPNET_CLOSE_Z_DEFAULTS[3] = -0.005), without changing global Task-E scripted collector constants.
  • Debug finding after user insisted the SAM-center-to-basket rule is valid: object-1 initial SAM3 failure was not a SAM capability issue. sam3_segment_image.py returned multiple masks and run_graspnet_pick.py initially picked the highest-confidence distractor near the basket/table (y≈-0.316) instead of the sugar box (y≈+0.261). Fix: sam3_segment_image.py now can write all candidates, and run_graspnet_pick.py selects/refines the candidate with the most RGB-D points inside the target object's official spawn x/y band and table-height range.
  • After world-band candidate selection, object-1 SAM3 perception is correct (selected_candidate=1 prompt=box, mask 3327 px, GraspNet/pointcloud target about (1.020,0.261,0.917)). It partially lifts (z_gain_lift=0.120) but misses the basket at final y -0.051; stronger basket servo made grasping worse, so current blocker is grasp/contact stability plus transport slip, not segmentation.
  • Object-2 SAM3 perception is also correct (band_ratio≈0.98-0.99, mask about 2500-2700 px). With default offset it partially lifts (z_gain_lift≈0.098-0.112) but stops around y -0.08..-0.10; no-offset is worse and lower close height still misses. Current blocker is bottle grasp/contact stability and transport slip, not SAM3/SAM2.
  • Do not switch to SAM2 as the primary fix unless SAM3 latency becomes unacceptable. The failure evidence points to candidate filtering (fixed) and execution/contact tuning (still pending), while SAM3 masks are good once selected by world-band constraints.
  • User correctly identified early release: added explicit transport-end logging, short stable-hold gating, and "do not open unless stable" logic in run_graspnet_pick.py. New args include --release_z, --basket_xy_tol, --basket_hold_steps, --basket_stable_steps, --basket_recovery_steps, --auto_table_push_on_slip, and --drag_recovery_steps.
  • Important nuance: long high hold can cause slip/bounce. Object-3 high-hold test reached min_xy_err≈0.055 but failed because it waited too long and then dropped/bounced. Use a short stable hold (basket_stable_steps about 6-12, basket_xy_tol about 0.09) rather than hundreds of hold steps.
  • Verified object-3 SAM3 short-stable release success after adding TRANSPORT_END: command used --object 3 --mask_provider sam3 --close_z_offset -0.005 --release_z 0.18 --basket_xy_tol 0.09 --basket_hold_steps 40 --basket_stable_steps 6 --transport_steps 220; result inside=True, TRANSPORT_END xy_err=0.006, final (1.077,-0.313,0.852). Video: logs/videos/task_e_graspnet/graspnet_pick_obj3_sam3_closed_drag.mp4.
  • Object-1 and object-2 still fail under SAM3+GraspNet despite correct masks. At TRANSPORT_END, both have slipped/dropped before release: object-1 example final transport (0.885,-0.056,0.872), xy_err=0.313; object-2 example (0.978,0.034,0.882), xy_err=0.349. Closed-drag/table-push recovery as currently implemented does not move them, likely because Piper gripper_base low push pose is not actually contacting the object side. Next step should switch recovery to the known Task-E calibrated state-machine transport/contact primitive or redesign pusher using actual finger/link contact geometry, not just gripper_base target z.
  • Follow-up on 2026-05-21 night: user explicitly required the method to be competition-real, so scripts/graspnet_task_e/run_graspnet_pick.py was changed to use actual link7/link8 finger centers for transport/recovery, not fake gripper_base contact assumptions. Added staged transport (--staged_transport, --transport_servo_fraction) so the arm first carries smoothly and only applies basket-center object servo in the final segment.
  • Verified SAM3+GraspNet object-3 with real finger/staged transport: command used --object 3 --mask_provider sam3 --close_z_offset -0.005 --transport_steps 700 --transport_servo_fraction 0.20 --basket_servo_gain 0.75 --basket_servo_max_xy 0.18; result inside=True, final about (0.995,-0.220,0.853), video logs/videos/task_e_graspnet/graspnet_pick_obj3_sam3_staged_transport.mp4.
  • SAM3+GraspNet object-1/2 are still not robust enough for submission. Object-1 no-offset test can partially lift (z_gain_lift≈0.130) but slips near y≈-0.055; object-2 offset test can partially lift (z_gain_lift≈0.120-0.137) but slips around y≈-0.10. GraspNet native yaw/quat made object-2 worse. The blocker is real contact stability during transport, not SAM3 segmentation.

• ACT obj321 servo training/eval, 2026-05-21:

  • Dataset exists: datasets/atec_task_e_obj321_servo_100demos/trajectory.hdf5 (122G) and trajectory_filtered.hdf5 (33G).
  • Training completed: run runs/act-task-e-rgb-obj321-servo-100demos-seed1, checkpoints through final.pt; training log symlink logs/task_e_obj321_servo_pipeline.latest.
  • Default submission checkpoint is already installed: demo/policy_act.pt SHA256 matches runs/act-task-e-rgb-obj321-servo-100demos-seed1/checkpoints/best_loss.pt (76bbef2c81c85fc6cc60448476ff09075ec8bc97217c2d2fb70a3253fd3e307d).
  • Evaluation with real observations: final.pt single episode scored 6.00, video logs/videos/task_e_act_eval/obj321_servo_final_1ep_20260521_realobs.mp4. best_loss.pt produced one successful 18.00 episode with video logs/videos/task_e_act_eval/obj321_servo_bestloss_3ep_20260521_realobs.mp4, but independent single-process repeats scored 6/12/12; treat current expected score as roughly 10-12 with occasional 18, not yet robust.
  • Known eval gotcha: same-process multi-episode reset can produce bogus later episodes with steps=1; use separate Python processes for trustworthy episode estimates.
  • 2026-05-21 night inference tuning: demo/solution_act.py now supports env vars ATEC_ACT_TEMPORAL_AGG=0, ATEC_ACT_PREFER_NEW=1, and ATEC_ACT_TEMPORAL_K=<float> without changing default behavior. scripts/act/eval_task_e_act.py now supports --seed and prints [BASKET] object status at the end.
  • Fixed-seed quick checks with best_loss.pt: prefer-new (ATEC_ACT_PREFER_NEW=1 ATEC_ACT_TEMPORAL_K=0.05) scored 6/15/0 on seeds 11/12/13; no-agg (ATEC_ACT_TEMPORAL_AGG=0) initially scored 6/9/15 without basket debug, but seed handling/debug rerun of seed13 scored 0 and showed all objects still outside basket. More controlled seed sweeps are needed before changing submission inference defaults.
  • Started ACT seed2 training from the existing filtered dataset, no recollection: systemd user unit atec-task-e-act-seed2-20260521225511.service, log logs/task_e_act_seed2_20260521225511.log, run name act-task-e-rgb-obj321-servo-100demos-seed2.
  • Seed2 mid-training evals: 5000.pt on fixed seed 11 scored 0.00 (all objects outside); then-current best_loss.pt scored 6.00 with only object_3 inside. 10000.pt on fixed seed 11 scored 0.00. 15000.pt on fixed seed 11 also scored 0.00; video logs/videos/task_e_act_eval/seed2_15000_seed11.mp4. 20000.pt on fixed seed 11 scored 6.00 with only object_3 inside; video logs/videos/task_e_act_eval/seed2_20000_seed11.mp4. Do not replace demo/policy_act.pt with seed2 before later checkpoints prove better. Training continued past Iter 20000, latest logged loss around 0.020.
  • 2026-05-22 01:12 CST seed2 reached 30000.pt; loss about 0.0158, and best_loss.pt was updated. Attempted fixed-seed eval logs/eval_task_e_seed2_30000_seed11.log, but it is invalid: GPU was already at ~91/98 GB because unrelated Ray/FSDP processes used ~76 GB, Isaac/Warp hit CUDA OOM and fell back to software physics. The eval process was killed before completion; do not treat this as model success or failure. Next trustworthy seed2 eval should wait for enough free CUDA memory, or after the unrelated Ray job finishes; avoid stopping seed2 because scripts/act/train_task_e.py has no resume argument.
  • 2026-05-22 01:19 CST valid CUDA eval of seed2 30000.pt on fixed seed 11 scored 0.00; all objects outside. Video: logs/videos/task_e_act_eval/seed2_30000_seed11_valid.mp4. The original seed2 service then failed with status 143 at iter ~30400, leaving 30000.pt as the last full checkpoint.
  • Added resume support to scripts/act/train_task_e.py and scripts/act/run_task_e_pipeline.sh: --resume_checkpoint, --resume_iter, and env vars RESUME_CHECKPOINT / RESUME_ITER. New checkpoints save train_iter, optimizer, scheduler, and EMA state; legacy checkpoints can resume weights with a fresh optimizer. Smoke test passed: logs/train_task_e_resume_smoke_seed2_30000.log, run runs/act-task-e-resume-smoke-seed2-30000.
  • Started resumed seed2 continuation: systemd unit atec-task-e-act-seed2-resume-20260522013429.service, log logs/task_e_act_seed2_resume_20260522013429.log, command resumes from runs/act-task-e-rgb-obj321-servo-100demos-seed2/checkpoints/30000.pt with RESUME_ITER=30000 and TOTAL_ITERS=100000.
  • Resume status 2026-05-22 01:52 CST: service successfully loaded 100 trajectories (169700 timesteps), resumed at iter 30000, and reached about Iter 31000; GPU utilization was 100%, process 722380 used about 13 GB VRAM. New 30000.pt / best_loss.pt are about 333 MB because they now include optimizer/scheduler/EMA state.
  • Seed2 resumed eval: 35000.pt exists and valid seed-11 eval scored 0.00 (logs/eval_task_e_seed2_35000_seed11_valid.log); all object_1/2/3 were outside. Do not deploy seed2 at 35k. Continue training toward 50k/100k and only evaluate later checkpoints or best_loss when GPU is not crowded.
  • Seed2 best_loss snapshot at 2026-05-22 02:22 (runs/act-task-e-rgb-obj321-servo-100demos-seed2/checkpoints/best_loss_snapshot_20260522_022223.pt, sha256 5e52307e84684a98429273102e136f0398e458ab1bd02a0dc6efc025493d4f02) scored 12.00 on seed 11 (logs/eval_task_e_seed2_bestloss_snapshot_20260522_022223_seed11.log): object_2 and object_3 inside, object_1 outside. This is better than 35000.pt but still not enough to replace XSA; evaluate later best_loss/50k across multiple seeds before deployment.
  • Resume status 2026-05-22 02:32 CST: training reached about Iter 41200; 40000.pt exists (runs/act-task-e-rgb-obj321-servo-100demos-seed2/checkpoints/40000.pt). No 40k eval yet because GPU was crowded by another Ray/FSDP job (~70 GB) plus seed2 training; wait for 50k or a clear GPU window.
  • Seed2 best_loss snapshot at 2026-05-22 02:35 (runs/act-task-e-rgb-obj321-servo-100demos-seed2/checkpoints/best_loss_snapshot_20260522_023530.pt, sha256 4048a27189b6539e9c5a699ae39eb4e2ea9496e197649d5fa5434141b5495ad7) scored only 6.00 on seed 11 (logs/eval_task_e_seed2_bestloss_snapshot_20260522_023530_seed11.log): only object_3 inside. Lower training loss did not correlate with better task score; do not deploy this snapshot.
  • Seed2 numbered checkpoint evals after resume: 45000.pt scored seed11 18.00 but seed12 0.00; 50000.pt scored seed11 15.00 and seed12 6.00. This is still too unstable to deploy over current XSA candidate. Continue training and evaluate later checkpoints; object_1 remains the main missing piece.
  • Resume status 2026-05-22 03:24 CST: 55000.pt exists and best_loss.pt updated at the same time, but no 55k eval was run because 45k/50k already proved instability. Next planned evaluation gate is 75k or final/100k unless training fails.
  • Resume status 2026-05-22 04:00 CST: training reached about Iter 64300; 60000.pt exists and best_loss.pt updated at about 03:58:59. No 60k eval; continue to 75k gate.
  • Seed2 75000.pt eval on seed 11 scored only 6.00 (logs/eval_task_e_seed2_75000_seed11.log): only object_3 inside. Do not spend more seed12/13 eval on 75k; wait for final/100k or a specific promising snapshot.
  • Seed2 mid/late checks on 2026-05-22: 85000.pt seed11 scored 0.00 (logs/eval_task_e_seed2_85000_seed11.log), while the then-current best_loss.pt around iter 87300 scored 15.00 (logs/eval_task_e_seed2_bestloss_87300_seed11.log) with object_2 and object_3 inside and object_1 outside. This reinforces that training loss and task score are weakly correlated; object_1 remains the main blocker and ACT alone is not yet stable full-score.
  • Seed2 finished 100k on 2026-05-22: final.pt SHA256 36493deefaf05694351db776435c2d53d9fe4a274c4f95febfe0fe8b36fea652 scored 0.00 on seed11 (logs/eval_task_e_seed2_final_seed11.log); latest best_loss.pt SHA256 f14c9d7c536c1d4156a8603064ec65cc593d8659147da175212fd8d7cbad2950 scored 9.00 on seed11 (logs/eval_task_e_seed2_bestloss_96900_seed11.log, only object_3 inside). Do not deploy seed2 over current XSA candidate.
  • Strategy judgment as of 2026-05-22: keep ACT/XSA as the best deployable baseline and finish seed2 to 100k for evidence, but do not assume more ACT iterations will reach stable 18. Prepare a pi0.5/OpenPI fine-tune branch using the existing HDF5 demos converted to LeRobot format; use GraspNet/SAM3 mainly as a grasp/data-generation aid unless object_1/2 contact stability is proven in ATEC Isaac with real observations/actions.
  • XSA final checkpoint had the best current evidence but is not full-score stable. Direct eval of runs/act-task-e-rgb-xsa-100demos-30k-seed11/checkpoints/final.pt scored 15/15/15 on seeds 11/12/13 (object_2 and object_3 inside, object_1 outside), with seed-11 video logs/videos/task_e_act_eval/xsa_final_seed11_recheck.mp4. The same checkpoint deployed as demo/policy_act.pt later scored 9 on seed 11 and 0 on seed 12, so there is residual nondeterminism/contact instability. Current demo/policy_act.pt SHA256 is XSA final c3eacae3f1b0ec8ccde9fdc05d680fff119cc2ca70e1f79e4ddd5610c4bbfa93; previous seed1 best was backed up as demo/policy_act.seed1_best_backup_20260522_012719.pt.

• pi0.5 / OpenPI Task E branch, 2026-05-22:

  • OpenPI repo/env: /home/ubuntu/src/openpi-ebench-clean, Python env /home/ubuntu/envs/openpi-pi05; run with PYTHONPATH=/home/ubuntu/src/openpi-ebench-clean/src:/home/ubuntu/src/openpi-ebench-clean/packages/openpi-client/src, HF_LEROBOT_HOME=/home/ubuntu/projects/robotics_shared/datasets/lerobot, HF_HOME=/home/ubuntu/projects/robotics_shared/hf_cache.
  • Avoid: do not use uv run in the OpenPI repo; it tries to create a large local .venv and download duplicated packages.
  • Converter: scripts/pi05/convert_task_e_hdf5_to_lerobot.py maps ATEC 8D Piper qpos/action to OpenPI fixed-base 16D padded state/action.{joints,gripper} and writes 3 identical 224x224 camera streams. 224 smoke dataset atec/task_e_obj321_servo_smoke2_stride10_224 wrote 343 frames in about 70s and occupies 48M.
  • OpenPI configs added in /home/ubuntu/src/openpi-ebench-clean/src/openpi/training/config.py: pi05_atec_task_e_smoke_224 and pi05_atec_task_e_s5_224. The formal s5_224 config uses repo atec/task_e_obj321_servo_100demos_s5_224, batch size 1, 30k steps, save interval 30k to avoid 42G checkpoint spam.
  • Important fix: ATEC HDF5 actions are env actions (joint_target-default)/0.5, not absolute joint targets, so all ATEC OpenPI configs must keep extra_delta_transform=False. Using the OpenPI delta transform here would subtract state again and train on the wrong target.
  • Smoke verification passed: pi05_atec_task_e_smoke_224 norm stats wrote under /home/ubuntu/projects/robotics_shared/checkpoints/openpi/atec_runs/assets/pi05_atec_task_e_smoke_224/...; 10-step train saved successfully once, but the temporary 42G checkpoint was deleted to preserve disk space. Log: logs/pi05_atec_task_e_smoke_224_10step_20260522.log.
  • Inference bridge added: demo/solution_pi05.py talks to an OpenPI websocket server and maps ATEC observation/action to/from the 16D fixed-base format. Eval entry point: scripts/pi05/eval_task_e_pi05.py. Default ATEC_PI05_ACTION_REPEAT=5 matches the current stride-5/fps-10 training dataset. After a checkpoint exists, use scripts/pi05/run_pi05_eval_checkpoint.sh <checkpoint_step_dir> to start scripts/serve_policy.py policy:checkpoint ..., run seeds 11/12/13, and record a seed-11 video.
  • Eval summary helper: scripts/pi05/summarize_pi05_eval.py <logs/pi05_eval_*> parses seed logs for score/object basket status and is called automatically at the end of run_pi05_eval_checkpoint.sh.
  • Formal s5 conversion service started: unit from logs/pi05_convert_task_e_s5_224.latest_unit, log from logs/pi05_convert_task_e_s5_224.latest; auto continuation service logs/pi05_s5_train_after_convert.latest_unit waits for conversion, then computes norm stats and trains pi05_atec_task_e_s5_224. Eval watcher logs/pi05_100demos_eval_after_train.latest_unit waits for training, then calls CONFIG=pi05_atec_task_e_s5_224 PORT=8017 scripts/pi05/run_pi05_eval_checkpoint.sh <latest_ckpt>.
  • Early-signal branch: pi05_atec_task_e_20demos_s5_224 config uses repo atec/task_e_obj321_servo_20demos_s5_224, 2k steps, batch size 1, no delta transform. Conversion service/log are in logs/pi05_convert_task_e_s5_224_20demos.latest_unit and .latest; auto continuation service logs/pi05_20demos_train_after_convert.latest_unit waits, computes norm stats, then trains. Eval watcher logs/pi05_20demos_eval_after_train.latest_unit waits for training, then calls CONFIG=pi05_atec_task_e_20demos_s5_224 PORT=8016 scripts/pi05/run_pi05_eval_checkpoint.sh <latest_ckpt>. Use this for quick pi0.5 viability checks before the 100-demo run finishes.
  • Note: the first 20-demo training run started from the old script name atec_task_e_pi05_20demos_s5_224_10k_20260522070624, but the loaded config was already patched to num_train_steps=2000; the name is cosmetic. The script was patched afterward to use _2k_ in future run names.
  • 20-demo result: checkpoint .../pi05_atec_task_e_20demos_s5_224/atec_task_e_pi05_20demos_s5_224_10k_20260522070624/1999 scored 0/0/0 on seeds 11/12/13. Logs: logs/pi05_eval_20260522074137; video: logs/videos/task_e_pi05_eval/pi05_pi05_atec_task_e_20demos_s5_224_seed11_20260522074137.mp4. The 42G 20-demo checkpoint directory was deleted after evaluation to preserve disk; dataset and logs were kept.
  • 100-demo formal run status, 2026-05-22 08:56 CST: conversion and norm stats are complete; training unit atec-pi05-s5-train-after-convert-20260522065716.service is actively running scripts/train.py pi05_atec_task_e_s5_224 --exp-name atec_task_e_pi05_s5_224_30k_20260522065716 --overwrite. Latest observed progress was about 731/30000 steps at roughly 3.3-3.6 it/s, GPU util 100%, ~80 GB VRAM. Log: logs/pi05_train_task_e_s5_224_20260522065716.log.
  • 100-demo checkpoint/eval gate: config pi05_atec_task_e_s5_224 saves only at 30000 steps (save_interval=30000), expected final dir .../checkpoints/pi05_atec_task_e_s5_224/atec_task_e_pi05_s5_224_30k_20260522065716/30000. Eval watcher unit atec-pi05-100demos-eval-after-train-20260522070900.service waits for the training unit, then runs CONFIG=pi05_atec_task_e_s5_224 PORT=8017 scripts/pi05/run_pi05_eval_checkpoint.sh <checkpoint> for seeds 11/12/13 and records a seed-11 video.
  • Deployment decision gate: current deployed ACT/XSA baseline is still demo/policy_act.pt SHA256 c3eacae3f1b0ec8ccde9fdc05d680fff119cc2ca70e1f79e4ddd5610c4bbfa93. Do not replace it with pi0.5 unless the 100-demo pi0.5 checkpoint has real ATEC Isaac eval evidence on seeds 11/12/13 and is clearly better. Required completion artifacts for this branch are: final checkpoint dir, logs/pi05_eval_* with seed11/12/13 [RESULT] and [BASKET], and logs/videos/task_e_pi05_eval/pi05_pi05_atec_task_e_s5_224_seed11_*.mp4.
  • Post-eval comparison helper: scripts/pi05/compare_pi05_vs_act_baseline.py <logs/pi05_eval_*> --repo <repo> compares pi0.5 seed logs with current ACT/XSA evidence (eval_task_e_xsa_final_seed11/12/13_recheck.log plus deployed seed11 recheck) and prints HOLD_ACT_XSA unless pi0.5 is clearly better. scripts/pi05/run_pi05_eval_checkpoint.sh calls this automatically after summarize_pi05_eval.py. It was sanity-checked on the 20-demo failed eval and correctly reported HOLD_ACT_XSA.
  • 100-demo result, 2026-05-22 11:27 CST: checkpoint .../pi05_atec_task_e_s5_224/atec_task_e_pi05_s5_224_30k_20260522065716/29999 (about 42G) evaluated at logs/pi05_eval_20260522112701 and scored seed11 0.00, seed12 0.00, seed13 6.00 (mean 2.00). Seed11 video: logs/videos/task_e_pi05_eval/pi05_pi05_atec_task_e_s5_224_seed11_20260522112701.mp4. The comparison helper printed HOLD_ACT_XSA; do not deploy pi0.5 over current XSA.
  • Monitoring: the old heartbeat automation atec-task-e-seed2-resume-monitor was deleted after the 100-demo pi0.5 train/eval completed, because the monitored branch is done. Recreate a new monitor only if starting another long run.

• Known-good environment:

  • Python: /home/ubuntu/envs/genmanip-isaac5-py311/bin/python.
  • Required env prefix: OMNI_KIT_ACCEPT_EULA=YES PYTHONUNBUFFERED=1.
  • Required PYTHONPATH: IsaacLab source dirs under /home/ubuntu/envs/genmanip-isaac5-py311/lib/python3.11/site-packages/isaaclab/source/*, plus source/atec_rl_lab and scripts/act from this repo.

• Current ACT baseline:

  • Object 3 only works with dataset datasets/atec_task_e, run runs/act-task-e-rgb-100demos-seed1, observed eval score about 6.
  • Baseline eval video: logs/videos/task_e_act_eval/current_single_object_6pt.mp4.

• Scripted oracle status:

  • Official/local Task E collector comment said object 3 works and object 1/2 need distance modifications.
  • Piper gripper joint limits observed on 2026-05-20: joint7 is [0.0, 0.035], joint8 is [-0.035, 0.0]. Use legal close target [0.0, 0.0], not [-0.015, 0.015].
  • Object 2 had a previously observed basket candidate: offset (dx=0.006, dy=0.046), z=0.045, carry_z=TABLE_TOP_Z+0.075, place_z=TABLE_TOP_Z+0.075, target_y=-0.24, yaw=-pi/2, keep gripper closed in transport. Treat this as not yet robust and likely push/slide rather than true lift: on 2026-05-20 a trace rerun showed the earlier search used candidate-index-dependent RNG, so success could be placement-specific.
  • On 2026-05-20 13:47, object 2 candidate search first 20 all failed under fixed trial seed. Best was cand 011 (dx=0.030, dy=0.026, z=0.045, carry_z=TABLE_TOP_Z+0.075, target_y=-0.24, yaw=+pi/2), final (1.224,-0.009,1.213), reward_lifted=True, outside by dy=+0.291. Object 2 is not ready for demo collection/training.
  • Object 1 is unresolved as of 2026-05-20. Prior search candidates mostly pushed/slid the object; final basket failure alone was too weak to prove a real grasp. Add/keep per-stage diagnostics before trusting any object 1 candidate.

• Important local changes:

  • scripts/act/eval_task_e_act.py evaluates ACT and can record video.
  • demo/solution_act.py supports ATEC_ACT_POLICY_PATH and reset_episode().
  • scripts/act/cli_args.py adds --max_attempts; scripts/act/collect_demos_task_e.py uses it and prints object basket status.
  • scripts/act/task_e/collector.py uses _BASKET_MAX_Z = TABLE_TOP_Z + 0.15, has basket_status_lines, and can return numeric trace diagnostics when trace=True including object z-gain, gripper_base distance, and link7/link8 finger-body center/gap metrics.
  • scripts/act/search_task_e_grasps.py is the per-object candidate bank/search entry point.
  • scripts/act/search_task_e_grasps.py should keep trial seeds independent of candidate index so candidates are compared on the same object placements.
  • scripts/act/search_task_e_grasps.py prints [BEST] even when no candidate succeeds, using basket distance plus z-gain as a debugging score.
  • scripts/act/search_task_e_grasps.py supports --start_candidate to resume long searches without re-testing earlier candidates.
  • scripts/act/search_task_e_grasps.py now supports per-candidate push_x/push_y/push_z plus close/lift/transport/place/open step overrides. Use this to test trajectory timing without editing global config for each trial.
  • scripts/act/task_e/state_machine.py now has object-servo transport/place/open support for object 1/2: OBJ_SERVO_TO_BASKET_STATES, OBJ_SERVO_XY_GAINS, and OBJ_SERVO_MAX_XY in scripts/act/task_e/config.py. The TRANSPORT correction is ramped by state progress to avoid yanking objects out of the gripper.
  • scripts/act/collect_demos_task_e.py now disables default cameras and cfg.observations.image when need_camera=False; this fixes headless collection without --enable_cameras.

• Latest object 1 trace:

  • On 2026-05-20 13:19, foreground search candidates 1-12 all failed: lifted=False, reward_lifted=False, z_gain about 0.000-0.003m; best candidate was 004 but still final y=0.210, far outside basket. Next search should resume at candidate 13 to test push/sweep fallback candidates.
  • On 2026-05-20 13:24, foreground search candidates 13-24 tested push/sweep fallback. No basket success yet, but contact is real: candidate 019 (dx=0.040, dy=0.140, z=-0.020, carry_z=TABLE_TOP_Z+0.035, target_y=-0.240, yaw=0, push_y=-0.18) reached lifted=True, reward_lifted=True, z_gain=0.388, final pos about (1.276, -0.072, 1.260). It overshoots upward/outside basket, so next search should refine around push_y -0.14..-0.18, lower carry/place behavior, and possibly static place release.
  • On 2026-05-20 13:36, focused candidates 25-34 around candidate 019 all failed. Best was cand 034: dx=0.040, dy=0.140, z=-0.020, carry_z=TABLE_TOP_Z+0.035, target_x=-0.17, target_y=0.0, push_y=-0.18, final (1.280,-0.064,1.257), reward_lifted=True, still outside by dy=+0.236.
  • On 2026-05-20 13:42, timing candidates with longer/lower TRANSPORT and PLACE/OPEN overrides all failed. Best was cand 002 with transport_steps=900, final (1.280,-0.042,1.257), outside by dy=+0.258. This proves simply extending the push/sweep duration does not solve object 1; the contact geometry slips/throws the box before basket placement.
  • On 2026-05-20 14:15, object-servo ramp still failed object 1 first 8 candidates. Best was cand 003 (push_y=-0.14, transport_steps=1200), final (1.294,-0.020,1.270), reward_lifted=True, outside by dx=+0.214, dy=+0.280. Object 1 still needs a different contact/grasp primitive, not more global servo.

• Latest object 2 trace:

  • On 2026-05-20 14:14, object-servo ramp made fixed-seed search candidate 001 succeed for object 2: final (1.050,-0.272,0.865), inside=True, z_gain=0.102, candidate {dx=0.006, dy=0.046, z=0.045, carry_z=TABLE_TOP_Z+0.075, target_y=-0.24, yaw=-pi/2}.
  • On 2026-05-20 14:20, random only_success collection for object 2 still failed 0/4 attempts: final y errors remained about +0.206..+0.310, one early termination. So object 2 is promising but not robust enough for dataset/training yet.
  • On 2026-05-20 14:26, adding PLACE hold-until-near-basket still failed random object 2 collection 0/4: final y errors stayed about +0.206..+0.334.
  • On 2026-05-20 14:28, stronger object 2 servo (gain=1.15, max_xy=0.36, TRANSPORT=480) also failed 0/4 and worsened x drift; reverted object 2 to the softer fixed-seed-success servo values (gain=0.85, max_xy=0.24, no transport override). Next object 2 work should focus on grasp/contact robustness before transport, not stronger basket servo.
  • On 2026-05-20 14:41, object 2 multi-seed candidate validation (--trials 3 --max_candidates 12) found 0/12 robust candidates. The previous fixed-seed-success candidate failed immediately under this stricter check (result=0/3, final y error about +0.262). Best was yaw 0.0 with z_gain=0.355 but final (1.197,-0.001,1.277), outside by dy=+0.299. Treat object 2 as contact/grasp unstable, not transport-servo-limited.

• Current debugging judgment:

  • Do not integrate GraspNet first. For this small three-object simulated task, a reproducible scripted candidate bank with explicit lift/grasp diagnostics is faster and easier to pass code-review reproduction.
  • If object 1 remains blocked, collect/train object 2+3 first for a practical score bump, then return to object 1. Next object 1 work should change the motion primitive/contact mode, not merely sweep more static offsets or longer transport times.

• Useful verification commands:

  • Single object 1 debug video: python scripts/act/collect_demos_task_e.py --pick_objects 1 --num_demos 1 --max_attempts 3 --headless --enable_cameras --only_success --save_video --video_dir logs/videos/task_e_oracle_obj1_fixed --output_dir logs/debug_task_e_obj1_fixed.
  • Object 2+3 sanity check: python scripts/act/collect_demos_task_e.py --pick_objects 2 3 --num_demos 1 --max_attempts 3 --headless --enable_cameras --only_success --save_video --video_dir logs/videos/task_e_oracle_obj23_fixed --output_dir logs/debug_task_e_obj23_fixed.

• External reference checked:

  • https://github.com/fiveages-sim/lerobot_ros2 is a public ROS2/LeRobot integration repo. It exposes generic ROS2 robot/camera wrappers and examples with single_arm.pick/place YAML skills, but it does not directly provide an ATEC Piper-compatible grasp planner. Its robot_action_composer submodule is private/inaccessible by HTTPS clone in this environment.

• Storage cleanup on 2026-07-19:

  • The final filtered three-object dataset and its 337 split files are preserved in Datawhale/atec2026-task-e-reproducibility on Hugging Face. The local datasets/atec_task_e_obj321_servo_100demos/trajectory_filtered.hdf5 was deleted after remote verification.
  • The original 122G trajectory.hdf5 was deleted after the collection and filtering scripts were uploaded under code/task_e_collection/ in the same Hugging Face dataset repository. The raw trajectories themselves are not recoverable from those scripts.
  • The obsolete 30G datasets/atec_task_e/ dataset, 15G runs/ training intermediates, generated logs/, logs/videos/, and submissions/ packages were deleted. Final model, reproducibility dataset, logs archive, source code, robot model, third-party code, and this memory remain available.
  • Local ATEC project size is now about 7.4G. Do not delete third_party/anygrasp_sdk or the source/model directories unless a separate cleanup decision is made.