Release camera-ready v1.1.0 data, Croissant, provenance and image companions

Preserve all 10,598 corpus trajectories. Recover complete numerical members of two truncated NPZs; disclose missing original camera streams and publish separately aligned state renders. Include the separate 739-demo training artifact. Historical versions remain accessible.

CITATION.cff

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+cff-version: 1.2.0
+message: "If you use RecoverBench, please cite the paper below. Please also credit MimicGen (see THIRD_PARTY_NOTICES.md)."
+title: "RecoverBench: A Systematic Benchmark for Error Recovery in Robotic Manipulation"
+abstract: "RecoverBench evaluates how manipulation policies recover from standardized, reproducible post-error simulator states. It provides a taxonomy of 12 Error Skills (24 subtypes at two degrees, grouped into 5 Recovery Behavior Groups), 1,360 evaluation error scenes over six robosuite/MuJoCo tasks, a separate training scene pool, and a corpus of 10,598 recovery trajectories."
+version: 1.1.0
+# TODO(authors): add date-released when release v1.1.0 is uploaded.
+license: MIT  # exceptions: see THIRD_PARTY_NOTICES.md (NVIDIA Source Code License components; CC BY 4.0 source demonstrations)
+repository-code: "https://github.com/ErrorRecoveryBenchmark/ErrorRecoveryBenchmark"
+repository: "https://huggingface.co/datasets/ErrorRecovery/ErrorRecoveryBenchmark"
+url: "https://github.com/ErrorRecoveryBenchmark/ErrorRecoveryBenchmark"
+keywords:
+  - robotics
+  - manipulation
+  - error recovery
+  - benchmark
+  - imitation learning
+  - MuJoCo
+  - robosuite
+authors:
+  - family-names: Tang
+    given-names: Zijia
+    affiliation: "Duke University"
+  - family-names: Zhao
+    given-names: Ganlong
+    affiliation: "The Chinese University of Hong Kong; Centre for Perceptual and Interactive Intelligence"
+  - family-names: Chen
+    given-names: Xingping
+    affiliation: "Sun Yat-sen University"
+  - family-names: Chen
+    given-names: Junye
+    affiliation: "Sun Yat-sen University"
+  - family-names: Mo
+    given-names: Zihao
+    affiliation: "Sun Yat-sen University"
+  - family-names: Wang
+    given-names: Zedi
+    affiliation: "Sun Yat-sen University"
+  - family-names: Li
+    given-names: Guanbin
+    affiliation: "Sun Yat-sen University; Shenzhen Loop Area Institute; Guangdong Key Laboratory of Big Data Analysis and Processing"
+preferred-citation:
+  type: conference-paper
+  title: "RecoverBench: A Systematic Benchmark for Error Recovery in Robotic Manipulation"
+  collection-title: "Advances in Neural Information Processing Systems (NeurIPS 2026), Evaluations and Datasets Track"
+  year: 2026
+  authors:
+    - family-names: Tang
+      given-names: Zijia
+    - family-names: Zhao
+      given-names: Ganlong
+    - family-names: Chen
+      given-names: Xingping
+    - family-names: Chen
+      given-names: Junye
+    - family-names: Mo
+      given-names: Zihao
+    - family-names: Wang
+      given-names: Zedi
+    - family-names: Li
+      given-names: Guanbin

DATASHEET.md

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+# Datasheet for RecoverBench
+
+A datasheet for datasets ([Gebru et al., 2021](https://arxiv.org/abs/1803.09010)) for the RecoverBench dataset and benchmark.
+
+> RecoverBench measures recovery from injected post-failure simulator states, alongside clean-start task success. Published results depend on the exact policy checkpoint, data version, evaluation scenes, and success criterion; see `EVALUATION.md`.
+
+---
+
+## 1. Motivation
+
+**For what purpose was the dataset created?**
+RecoverBench was created to enable systematic evaluation of robot manipulation policies under post-failure execution states — a regime existing benchmarks (LIBERO, RLBench, CALVIN, robosuite, MimicGen, DROID, BEDLAM) do not measure. Existing benchmarks score policies from clean initial states only, producing capability estimates that do not generalize to deployment, where execution errors (slips, collisions, misgrasps) are routine. Recent ad-hoc recovery work (SC-VLA, CycleVLA, FLARE, RoboFAC, FailSafe) has begun to study this regime, but each uses its own 2–4 error types, success criteria, and simulator setups, making cross-paper comparison impossible. RecoverBench provides the missing standardization layer: a 12-skill / 24-subtype error taxonomy, deterministically reproducible error scenes, a from-error-state evaluation protocol paired with the standard from-clean protocol, and a recovery training corpus.
+
+**Who created the dataset and on behalf of which entity?**
+Created by Zijia Tang, Ganlong Zhao, Xingping Chen, Junye Chen, Zihao Mo, Zedi Wang, Guanbin Li. See `CITATION.cff` for the affiliations in the author-provided camera-ready source. Project: https://github.com/ErrorRecoveryBenchmark/ErrorRecoveryBenchmark.
+
+**Who funded the creation of the dataset?**
+The author-provided camera-ready acknowledgement credits the National Natural Science Foundation of China (62322608), Shenzhen Loop Area Institute (FPF10120260001), and computational resources from National Supercomputer Center in Guangzhou. The authors declare no competing interests; all-coauthor agreement was confirmed by the submitting author on October 21, 2026.
+
+---
+
+## 2. Composition
+
+**What do the instances represent?**
+Three core instance types. The camera-ready corpus additionally includes 668 generated trajectories in `recovery_mimicgen_generated.tar`, giving 10,598 historical entries. `recovery_training_success_from_error.tar` contains the separate 739 trajectories used by the reported recovery-training runs and is not added to that corpus count. Counts below describe archive entries, not a claim that every file passes replay. Two legacy human NPZ files had truncated camera streams. Release v1.1.0 preserves all 17 complete CRC-verified NPY payloads per file, including actions and states. Their original camera streams are omitted; a separate image supplement supplies 880 state-rendered companion frames with explicit alignment and provenance. These images are derivative observations, not recovered original pixels. The corpus remains 10,598 trajectories. Historical archives remain available in dataset version history.
+
+
+- **Error scenes**: a `(post_sim_state, RNG state, environment fingerprint)` snapshot representing a deterministically reproducible failure state (each scene = one NPZ + one JSON sidecar).
+- **Human recovery demonstrations** (973 archive entries): `(states, actions)` recordings of human recovery attempts; stored success flags do not certify successful replay.
+- **MimicGen-augmented recovery demonstrations** (8,957 total): synthetically generated recovery trajectories produced by warping human demonstrations onto new error scenes via 2,532 scene-warping trajectories and 6,425 recorded-action replay trajectories, as detailed in the camera-ready appendix.
+
+**How many instances are there in total, and how is the dataset structured?**
+
+The core consists of three archives (see the [dataset card on HuggingFace](https://huggingface.co/datasets/ErrorRecovery/ErrorRecoveryBenchmark) and `SHA256SUMS`):
+
+| Archive | Contents | Purpose |
+|---|---|---|
+| `eval_data.tar` | 1,360 error scenes (136 task × subtype combinations × 10; 2,720 files) | **Evaluation set** (frozen) |
+| `training_scenes.tar` | 5,038 error scenes | **Training scene pool** — disjoint from the evaluation set (verified: zero shared scene IDs, zero identical post-error state fingerprints); recovery demos were collected on scenes from this pool |
+| `training_data.tar` | `recovery_demos_human/` 973 NPZ + `recovery_demos_augmented/` 8,957 NPZ | Recovery-demonstration training data |
+
+Clean-trajectory seed demos are downloaded from MimicGen's public HuggingFace
+repository by `scripts/download_data.sh` (not redistributed here).
+
+Per-task scene counts — evaluation set: coffee 200 · pick_place 240 · stack 240 ·
+stack_three 240 · threading 200 · three_piece_assembly 240; training pool:
+coffee 200 · pick_place 2,080 · stack 2,079 · stack_three 240 · threading 199 ·
+three_piece_assembly 240.
+
+Error subtypes: 12 Error Skills × 2 difficulty degrees (D0: mild displacement < 10–15 cm; D1: severe displacement ≥ 10–15 cm with rotation) = 24 subtypes, organized into 5 Recovery Behavior Groups (RBGs A/B/C/D/E). See [`README.md`](README.md) for the taxonomy table.
+
+**Modalities per instance:**
+- RGB: 224×224 agentview camera images (and other camera views available via robosuite config)
+- Proprioception: 7-DOF joint states + gripper state (continuous, 0–1)
+- Object poses: 3D positions + quaternions in `(w, x, y, z)` order
+- Depth: optional, available via `camera_depths` parameter
+- Language: per-RBG instruction templates (e.g., RBG_E: "correct the position error and resume the task")
+
+**Does the dataset contain all possible instances or is it a sample?**
+Sample. The error-skill × task × difficulty grid covers 24 subtypes × 6 tasks = 144 cells. The frozen evaluation release covers 136 cells (1,360 scenes). This observed coverage is distinct from the number of theoretically applicable cells; per-task counts are listed above.
+
+**What data does each instance consist of?**
+Error scenes: NPZ with `sim_state` (pre-injection) and `post_sim_state` (post-injection stable state); JSON with metadata (error skill, degree, injection frame, source dataset reference, RNG seed, environment fingerprint). Recovery demos: NPZ with `states`, `actions` (6 OSC pose components + 1 gripper command), `recovery_subtypes` (RBG-segmented subtask labels).
+
+**Are there labels?**
+Yes. Error scenes are labeled with error skill name, degree, RBG, task, and source clean trajectory. Recovery demos are labeled with `recovery_subtypes` per timestep, identifying the active recovery sub-skill (e.g., `re_grasp`, `re_orient`).
+
+**Are there recommended data splits?**
+Yes — the split is physical: `eval_data.tar` is the frozen 1,360-scene evaluation set; `training_scenes.tar` (scene pool) and `training_data.tar` (`recovery_demos_human/`, `recovery_demos_augmented/`) are the training side. Train/eval disjointness is verified at the level of scene IDs and SHA-256 fingerprints of the canonical post-error simulator state.
+
+**Are there errors, sources of noise, or redundancies?**
+- Augmentation success rates vary widely by subtype (~9.6% mean on stack, with some subtypes at 0%); failed augmentations are filtered out before inclusion. Successful augmentations may still contain near-duplicate trajectories from the same human demo source.
+- Human teleoperation demonstrations contain operator variability. Recorded success and replay flags describe the collection pipeline; the current archive audit is structural and does not certify successful replay of every released demonstration.
+
+**Does the dataset rely on external resources?**
+Yes. The data was generated using vendored forks of:
+- `robosuite` (commit c848ca84, MuJoCo 2.3.2)
+- `mimicgen`
+- `robosuite-task-zoo`
+
+These are bundled in `shared/mimicgen_workspace/` so the dataset can be regenerated/replayed without external dependency drift. See `ROBOSUITE_VERSION_LOCK.txt` for version pinning.
+
+**Does the dataset contain sensitive, personal, or confidential information?**
+No. All data is simulated (MuJoCo); there are no human subjects, no personal data, no images of identifiable individuals. The human teleoperator's identity is not recorded in any instance.
+
+---
+
+## 3. Collection Process
+
+**How was the data acquired?**
+Three pipelines:
+
+1. **Error scene generation (v5 Error-Skill + Context-Replay pipeline):** clean trajectories from `seed_demos/` are loaded; for each (task, error skill, degree) cell, the pipeline replays the clean trajectory up to a target injection frame, applies the error skill's `inject()` method (parameterized by degree), simulates to a stable state, and stores `(sim_state, post_sim_state, RNG, fingerprint)`. Fully automated and deterministic.
+
+2. **Human recovery demonstration collection:** operators use a SpaceMouse with Sawyer (`pick_place`) or Panda (other tasks) after an injected error. Collection and replay-validation code are provided, but historical files must not be assumed to pass all checks in the current code. The paper distinguishes stored simulator-state restoration from action-replay reproducibility.
+
+3. **MimicGen scene-configuration augmentation:** human demonstrations are RBG-segmented, then re-targeted onto new error scenes within the same subtype via MimicGen's scene-configuration warping. Augmentation feasibility is verified on a sample of N=10 candidate scenes per subtype before bulk generation; only successful augmentations are retained.
+
+**Who collected the data?**
+The RecoverBench authors. The human teleoperator(s) are members of the author team.
+
+**Over what time frame was the data collected?**
+2026-Q1 through 2026-Q2 (data version 1.0.0, dated 2026-05).
+
+**Were any ethical review processes conducted?**
+The dataset is **simulation-only** (MuJoCo 2.3.2 with Sawyer and Panda arms in robosuite). It contains no human-subjects data, no personally identifiable information, no real-world imagery, no third-party copyrighted content, and no health, demographic, or behavioral data about identifiable individuals. The teleoperator(s) are authors of the project and consented to release of the recorded trajectories; no operator identity is recorded in any released instance. Under common research-ethics frameworks (e.g., U.S. Common Rule §46.102) this work does not constitute human-subjects research, and IRB / ethics-board review was therefore not required. We have nonetheless audited all released trajectories to confirm no incidental personal data is embedded in metadata or filenames.
+
+---
+
+## 4. Preprocessing / Cleaning / Labeling
+
+**Was any preprocessing/cleaning/labeling done?**
+The released augmentation corpus uses two-subtask segmentation at gripper close (plus a 15-frame buffer); RBG-aware segmentation is an optional mode. Augmentations were checked for success at generation time. The current audit checks archive readability, action/state dimensions and finite values; it does not certify successful action replay of every record. The shipped augmented stack example fails replay under the pinned stack, as recorded in `examples/recovery_demo_sample/replay_validation.json`.
+
+**Was the raw data saved?**
+The historical NPZ archive is preserved. Two truncated human NPZ containers were recovered using only complete CRC-verified members. Original camera streams remain unavailable; the image supplement is clearly labeled as state-rendered derivative data. No numerical states were fabricated.
+
+---
+
+## 5. Uses
+
+**What tasks has the dataset been used for?**
+Within the project: evaluating Pi0.5 (task-specific fine-tuned) and BC-RNN baselines on the from-error-state vs. from-clean-state gap; training error-aware VLA fine-tunes on the human + augmented recovery corpus.
+
+**What are the recommended uses?**
+- Recovery evaluation under the RecoverBench gap-reporting protocol (paired Clean Success Rate and Recovery Success Rate; see [`EVALUATION.md`](EVALUATION.md))
+- Recovery skill learning and imitation
+- RBG-aware imitation (e.g., conditioning on RBG label)
+- Error-aware VLA fine-tuning (foundation models trained on clean data, fine-tuned on RecoverBench's recovery corpus)
+
+**What uses are inappropriate / discouraged?**
+- Treating `post_sim_state` as a clean initial state in benchmarks that do not disclose this provenance — this would defeat the comparability the dataset is built for.
+- Reporting from-error numbers without paired from-clean numbers — this would re-create the mis-measurement problem RecoverBench exists to fix.
+- Drawing real-world deployment conclusions from v1.0.0 alone: the data is simulated only.
+
+**Are there tasks for which the dataset should not be used?**
+The dataset is not suitable for training general-purpose policies on its own (it is recovery-specific) and should not be used as a substitute for clean-state training data.
+
+---
+
+## 6. Distribution
+
+**Will the dataset be distributed to third parties?**
+Yes. Public open release alongside the NeurIPS 2026 Evaluations and Datasets camera-ready release.
+
+**How will it be distributed?**
+HuggingFace Hub ([ErrorRecovery/ErrorRecoveryBenchmark](https://huggingface.co/datasets/ErrorRecovery/ErrorRecoveryBenchmark)). The code release lives in this repository.
+
+**Under what license?**
+MIT License. Vendored dependencies in `shared/mimicgen_workspace/` retain their original licenses (robosuite, MimicGen, and robosuite-task-zoo have their own license files; MimicGen uses the NVIDIA Source Code License).
+
+**Are there any restrictions or terms of use?**
+RecoverBench-original code and data use MIT; upstream and adapted components retain the terms in `THIRD_PARTY_NOTICES.md`. Researchers are *requested* (not required) to follow the gap-reporting protocol so leaderboard numbers remain comparable across papers.
+
+---
+
+## 7. Maintenance
+
+**Who is supporting / hosting / maintaining the dataset?**
+The author team listed in `CITATION.cff` maintains the dataset. Use https://github.com/ErrorRecoveryBenchmark/ErrorRecoveryBenchmark/issues for issues and errata. Corresponding contact: Guanbin Li, liguanbin@mail.sysu.edu.cn; first-author contact: Zijia Tang, zt81@duke.edu.
+
+**How will errata or corrections be communicated?**
+Via Hugging Face revision history and the GitHub issue tracker. Archive checksums identify exact versions. The camera-ready correction is being prepared; see docs/RELEASE.md and metadata/release_inventory.json.
+
+**Will the dataset be updated?**
+Yes. Planned future versions:
+- v1.1: additional baseline evaluations (more VLAs and imitation methods)
+- v2: real-robot extension (additional embodiments beyond simulated Sawyer and Panda)
+- v2: additional task families and error skills based on community feedback
+
+**How can others contribute to the dataset?**
+By submitting new evaluations to the leaderboard (see [`EVALUATION.md`](EVALUATION.md) for the submission template), reporting issues via the project repository, or proposing new error skills / RBGs through the project's issue tracker.

IMAGE_SUPPLEMENT.md

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+# 两条损坏记录的图像补充包
+
+已为两条轨迹的全部保存状态重新渲染 256×256 RGB 图像，分别 381 帧和 499 帧，共 880 帧。
+这不新增人工示范，不修改原始动作、状态、success 标记或论文统计。
+
+## 文件用途
+
+- `recovery_*.npz`（无 `.images` 后缀）：此前从损坏文件中恢复的 17 个完整 NPY 成员，原始成员字节和 CRC 已验证。
+- `recovery_*.images.npz`：本次仿真重渲染的独立伴随文件。`camera_images[i]` 对应同名轨迹的 `states[camera_state_indices[i]]`，`state_times[i]` 是该状态保存的仿真时间。图像数等于状态数，比动作数多 1。
+- `*.images.json`：来源 SHA256、渲染器版本、相机设置、几何体显示组、状态恢复和位姿校验结果。
+- `*_prefix.npy`：从损坏压缩流中读出的完整原始帧前缀，分别 1/97 帧和 112/126 帧。完整图像成员缺失，无法校验其整体 CRC，因此这些前缀仅作为原始残存数据保存。
+- `validation.json`：两条记录的完整性与对齐校验；`preview.jpg`：每条轨迹的 5 个采样帧。
+- `render_recovery_state_images.py`、`threading.yaml`：生成脚本及任务配置快照；需在仓库及其锁定依赖环境中运行。
+
+## 来源和使用边界
+
+重渲染使用 MuJoCo 2.3.2 / robosuite 1.4.1、Panda、Threading_D0 和 agentview。
+逐帧恢复保存的仿真状态并执行 forward，不重新积分动作，不使用生成式图像模型。
+880 个状态恢复后数值逐项完全一致；末端、needle、tripod 的 forward 位置与文件内观测最大差异为 0.299 毫米以下。
+这只是状态与图像对齐校验，不等于动作重放或策略实验复现。
+
+旧图像的相机视角、机器人外观和裁剪时间偏移不能由现有材料完全确认。
+因此新图像整段保持同一渲染配置，明确标记为 `state_rerender`，没有拼接到旧相机序列。
+它们不是被找回的原始相机像素，不应作为无变化的原始图像数据使用。
+如果用作视觉训练，应显式选择这些伴随文件并记录重渲染来源，不能沿用旧图像的每四步采样索引。
+
+本补充包随 v1.1.0 数据发布，SHA256SUMS 包含它的校验和。五个核心归档的数值内容不因图像补充而改变。
+
+## 读取示例
+
+```python
+import numpy as np
+name = "recovery_threading_collision_eef_object_D1_0027"
+with np.load(name + ".npz") as trajectory, np.load(name + ".images.npz") as images:
+    frame = images["camera_images"][20]
+    state_index = int(images["camera_state_indices"][20])
+    state = trajectory["states"][state_index]
+```
+
+## 重新生成
+
+在已安装 RecoverBench 及锁定子模块的 Python 3.10 环境中，从仓库根目录执行：
+
+```text
+python -m scripts.render_recovery_state_images <恢复后的轨迹.npz> <新的伴随文件.images.npz> --seed-dataset <threading_d0_prepared.hdf5> --task-config error_benchmark/configs/tasks/threading.yaml
+```
+
+脚本拒绝覆盖已有输出。Windows 使用 GLFW。需要提供原始 Threading seed HDF5 的环境元数据。

LICENSE

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+MIT License
+
+Copyright (c) 2026 Zijia Tang, Ganlong Zhao, Xingping Chen, Junye Chen, Zihao Mo, Zedi Wang, Guanbin Li
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,7 +1,100 @@
----
-pretty_name: RecoverBench
----
-
-# RecoverBench
-
-Dataset repository initialized.
+---
+pretty_name: RecoverBench
+license: mit
+task_categories:
+  - robotics
+tags:
+  - robot-manipulation
+  - error-recovery
+  - mujoco
+  - robosuite
+---
+
+# RecoverBench
+
+> Camera-ready release v1.1.0: all 10,598 corpus trajectories have structurally readable action/state arrays. Two human files preserve their 17 complete CRC-verified NPY members but lack their original camera streams. The separate image supplement provides 880 explicitly aligned state-rendered frames; these are derivative observations, not recovered original pixels. See repair_manifest.json and IMAGE_SUPPLEMENT.md. Full-corpus action replay is not certified.
+
+RecoverBench is a systematic benchmark for **error recovery in robotic manipulation**:
+a taxonomy of 12 Error Skills × 2 difficulty degrees (24 subtypes, grouped into 5
+Recovery Behavior Groups A–E), an automatic pipeline that injects errors into clean
+trajectories and snapshots reproducible post-error states, and a recovery-demonstration
+corpus for training recovery-capable policies. Tasks: `pick_place`, `stack`, `coffee`,
+`threading`, `stack_three`, `three_piece_assembly` (robosuite / MuJoCo; mixed
+Sawyer + Panda embodiments).
+
+Code, evaluation protocol, and regeneration pipeline:
+https://github.com/ErrorRecoveryBenchmark/ErrorRecoveryBenchmark
+
+## Files
+
+| File | Contents |
+|---|---|
+| `eval_data.tar` | **Evaluation set**: 1,360 error scenes (136 task × error-subtype combinations × 10 scenes). Each scene = `<id>.npz` (full MuJoCo state snapshot `post_sim_state`, quaternions in `(w,x,y,z)` order) + `<id>.json` (labels, environment fingerprint, RNG state). Layout: `error_scenes/<task>/scenes/`. |
+| `training_scenes.tar` | **Training scene pool**: 5,038 error scenes generated by the same pipeline, disjoint from the evaluation set (verified: zero shared scene IDs and zero identical `post_sim_state` fingerprints). Recovery demonstrations were collected on scenes from this pool. Layout: `training_scenes/<task>/scenes/`. |
+| `training_data.tar` | **Recovery demonstrations**: 973 readable human SpaceMouse teleoperation entries, including two without camera images (`recovery_demos_human/`) and MimicGen-style augmented demos (`recovery_demos_augmented/`, 8,957 trajectories). Each demo = `.npz` with `actions`, `states`, `eef_positions`, `target_poses`, `gripper_states`. |
+| `recovery_mimicgen_generated.tar` | 668 generated recovery trajectories; part of the 10,598-entry corpus. |
+| `recovery_training_success_from_error.tar` | Separate 739-trajectory HDF5 training artifact for the reported recovery-policy experiments. |
+| `RecoverBench_image_supplement.zip` | State-rendered companion images for the two repaired records, original surviving image prefixes, provenance and validation. |
+| `SHA256SUMS` | SHA-256 checksums for the five data archives and image supplement. |
+| `eval_scene_ids.tsv` | The frozen evaluation-scene ID list (task ↔ scene id). |
+
+## Quick start
+
+```bash
+# from the code repository
+bash scripts/download_data.sh /path/to/data
+export BENCHMARK_DATA=/path/to/data
+```
+
+Evaluation protocol (success = 10 consecutive success frames, 500-step budget,
+single seed per scene) and metrics are specified in the code repository's
+`EVALUATION.md`.
+
+## Reproducibility notes
+
+- Simulator stack is version-locked: robosuite 1.4.1 @ `c848ca84`, `mujoco==2.3.2`,
+  `numpy<2.0` (see `ROBOSUITE_VERSION_LOCK.txt` in the code repo).
+- Every error scene carries an environment fingerprint and RNG state for
+  deterministic restoration via the code repo's replay tooling.
+- Train/eval disjointness was verified at the level of scene IDs and SHA-256
+  fingerprints of the canonical post-error simulator state.
+
+## Citation
+
+Zijia Tang, Ganlong Zhao, Xingping Chen, Junye Chen, Zihao Mo, Zedi Wang, Guanbin Li. RecoverBench: A Systematic Benchmark for Error Recovery in Robotic Manipulation. NeurIPS 2026, Evaluations and Datasets Track (accepted).
+
+## License and provenance
+
+The RecoverBench release is licensed under MIT; see LICENSE. Upstream simulator,
+MimicGen source code, and seed datasets retain their own terms and attribution.
+Seeds: https://huggingface.co/datasets/amandlek/mimicgen_datasets.
+MimicGen: https://github.com/NVlabs/mimicgen (NVIDIA Source Code License).
+
+Created and maintained by Zijia Tang, Ganlong Zhao, Xingping Chen, Junye Chen, Zihao Mo, Zedi Wang, Guanbin Li. Affiliations are in `CITATION.cff`. Correspondence: liguanbin@mail.sysu.edu.cn; zt81@duke.edu. Funding: NSFC 62322608 and Shenzhen Loop Area Institute FPF10120260001; compute: National Supercomputer Center in Guangzhou. The authors declare no competing interests; the submitting author confirmed all-coauthor agreement on October 21, 2026.
+Use the code repository issue tracker for corrections.
+
+## Machine-readable records
+
+`eval_scenes.csv`, `training_scenes.csv`, and `recovery_demos.csv` index every NPZ
+member of the core archives. `croissant.json` provides typed CSV column mappings,
+archive checksums, RAI metadata, and upstream provenance. NPZ numerical arrays are
+loaded separately with NumPy; the CSV indexes do not imply tensor decoding support.
+The inventory verifies zero shared scene IDs and exact post-state fingerprints
+between the frozen evaluation and training scene archives.
+
+## Limitations and intended use
+
+Simulation-only tabletop recovery evaluation and policy training; mixed Sawyer and
+Panda embodiments, six tasks, hand-designed failure types, and uneven subtype
+coverage. This is not evidence of safe real-world deployment or general-purpose
+robot capability. Report clean-state and recovery success together.
+
+The corpus contains 973 human, 668 generated, and 8,957 augmented trajectories (10,598 total). The separate 739 HDF5 trajectories are the recovery-policy training artifact, not additional corpus members.
+
+## Image repair and reproducibility
+
+Two truncated human NPZ containers were recovered without changing any complete numerical payload. Their original camera streams cannot be fully recovered. `RecoverBench_image_supplement.zip` contains companion image files with 381 and 499 state-aligned rendered frames, explicit state indices and timestamps, the surviving original image prefixes, provenance, and validation. These images use a consistent current pinned simulator camera; original viewpoint and crop timing could not be confirmed. Do not splice them into the original camera stream or describe them as original observations. No reported policy results were recomputed with these images. See [IMAGE_SUPPLEMENT.md](IMAGE_SUPPLEMENT.md).
+
+The 739 HDF5 demos pass finite action/state and length checks. Their recorded model XML retains historical asset paths; normalize them with the pinned environment's `edit_model_xml` before replay. Structural checks do not certify action replay. Historical dataset commits remain available for comparison.
+
+Five CSV indexes expose evaluation scenes, training scenes, human/augmented demos, generated demos and the separate training demos to Croissant. The image supplement is an additional FileObject, not extra trajectory records.

RecoverBench_image_supplement.zip

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+size 59450560

SHA256SUMS

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+9f212d9b4c846578050a46d8cbb0e9c3cb9c880487a5b928c71ed58317fc6268  recovery_mimicgen_generated.tar
+a7f8e9f062727d5607fd1d1d4fccd088932943683c34e0e7e3602805260882b8  recovery_training_success_from_error.tar
+3c91eadd9993be6b16ebdad399a6224e464ce61357a4c39a54c18a4ab8973a00  RecoverBench_image_supplement.zip

THIRD_PARTY_NOTICES.md

@@ -0,0 +1,170 @@
+# Third-Party Notices
+
+We release the RecoverBench code, error scenes and recovery trajectories under the MIT License (see [`LICENSE`](LICENSE)). Some components keep their upstream terms: the vendored MimicGen code, one utility module adapted from it (`pose_transforms.py`) and two configuration files adapted from MimicGen templates remain under the NVIDIA Source Code License, and the clean source demonstrations from which the error scenes are generated come from the MimicGen datasets, which are released under CC BY 4.0 and are credited here and in the paper.
+
+This file lists which files are not covered by the MIT License, reproduces the license texts that apply to them, and credits the upstream assets that RecoverBench builds on.
+
+## 1. Summary
+
+| Component | Where | License | Section |
+|---|---|---|---|
+| MimicGen code | `shared/mimicgen_workspace/mimicgen/` (git submodule of [NVlabs/mimicgen](https://github.com/NVlabs/mimicgen)) | NVIDIA Source Code License | 2 |
+| `pose_transforms.py` | `error_benchmark/framework/utils/pose_transforms.py` | NVIDIA Source Code License (adapted from MimicGen `pose_utils.py`) | 2.2 |
+| MimicGen experiment-config adaptations | `error_benchmark/configs/mimicgen_pick_place_1000.json`, `error_benchmark/configs/mimicgen_threading_d1_1000.json` | NVIDIA Source Code License (adapted from MimicGen config templates) | 2.3 |
+| MimicGen datasets | clean source demonstrations behind the error scenes (not redistributed) | CC BY 4.0 | 3 |
+| robosuite 1.4.1 | `shared/mimicgen_workspace/robosuite/` (git submodule) | MIT | 4 |
+| robosuite-task-zoo | `shared/mimicgen_workspace/robosuite-task-zoo/` (git submodule) | MIT | 4 |
+| robomimic | installed from upstream, not vendored | MIT (see Section 4) | 4 |
+| MuJoCo 2.3.2 | installed from PyPI, not vendored | Apache 2.0 | 4 |
+
+All other files in this repository, and the data files in the Hugging Face release (error scenes in `eval_data.tar` and `training_scenes.tar`; human, MimicGen-generated and augmented recovery trajectories in `training_data.tar`, `recovery_mimicgen_generated.tar` and `recovery_training_success_from_error.tar`), are released under the MIT License.
+
+## 2. NVIDIA Source Code License components
+
+### 2.1 Vendored MimicGen code
+
+`shared/mimicgen_workspace/mimicgen/` is a git submodule of MimicGen (https://github.com/NVlabs/mimicgen); the version used for the reported results is commit `72bd767c` (v1.0.1), with local patches. It keeps its own `LICENSE` and file headers. MimicGen's code is released under the NVIDIA Source Code License, reproduced in full in Section 2.5. MimicGen is credited by citation in the paper:
+
+> Ajay Mandlekar, Soroush Nasiriany, Bowen Wen, Iretiayo Akinola, Yashraj Narang, Linxi Fan, Yuke Zhu, Dieter Fox. MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations. 7th Annual Conference on Robot Learning (CoRL), 2023. https://arxiv.org/abs/2310.17596
+
+### 2.2 `error_benchmark/framework/utils/pose_transforms.py`
+
+- **Origin:** adapted from `mimicgen/utils/pose_utils.py` in MimicGen (vendored at `shared/mimicgen_workspace/mimicgen/mimicgen/utils/pose_utils.py`).
+- **Upstream notice (retained):** "Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. Licensed under the NVIDIA Source Code License [see LICENSE for details]."
+- **License of this file:** NVIDIA Source Code License (Section 2.5). The file is not covered by the MIT License of this repository.
+- **Changes:** the module provides the same ten pose functions as `pose_utils.py` (`make_pose`, `unmake_pose`, `pose_inv`, `pose_in_A_to_pose_in_B`, `quat2axisangle`, `axisangle2quat`, `quat_slerp`, `interpolate_rotations`, `interpolate_poses`, `transform_source_data_segment_using_object_pose`). It has been modified so that it imports without robosuite or MimicGen: robosuite's quaternion helpers `mat2quat` and `quat2mat` are inlined as `_mat2quat` and `_quat2mat` (the inlined helpers follow robosuite's `transform_utils`, MIT, see Section 4).
+
+### 2.3 MimicGen experiment-config adaptations
+
+`error_benchmark/configs/mimicgen_pick_place_1000.json` and `error_benchmark/configs/mimicgen_threading_d1_1000.json` are edited copies of MimicGen's experiment-config templates (`mimicgen/exps/templates/robosuite/pick_place.json` and `threading.json`). Most values are unchanged; we changed the experiment name, source and output paths, generation counts and seed, and the rendering and camera settings (and, for threading, the task name and one selection strategy). They remain under the NVIDIA Source Code License (Section 2.5).
+
+### 2.4 Practical consequences
+
+The NVIDIA Source Code License limits use of the Work and of derivative works to non-commercial use, meaning research or evaluation purposes only (its Section 3.3). The task environments named in the scene files (for example `PickPlace_D0`) are defined by the MimicGen package, which `error_benchmark/scripts/utils/script_utils.py` imports to register them. Running the benchmark simulator therefore uses MimicGen under its own license; the MIT License of RecoverBench does not change that.
+
+### 2.5 Full text: NVIDIA Source Code License
+
+This is the text of `shared/mimicgen_workspace/mimicgen/LICENSE`, reproduced verbatim as Section 3.1(b) of the license requires.
+
+```
+NVIDIA License
+
+1. Definitions
+
+“Licensor” means any person or entity that distributes its Work.
+“Work” means (a) the original work of authorship made available under this license, which may include software, documentation, or other files, and (b) any additions to or derivative works  thereof  that are made available under this license.
+The terms “reproduce,” “reproduction,” “derivative works,” and “distribution” have the meaning as provided under U.S. copyright law; provided, however, that for the purposes of this license, derivative works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work.
+Works are “made available” under this license by including in or with the Work either (a) a copyright notice referencing the applicability of this license to the Work, or (b) a copy of this license.
+
+2. License Grant
+
+2.1 Copyright Grant. Subject to the terms and conditions of this license, each Licensor grants to you a perpetual, worldwide, non-exclusive, royalty-free, copyright license to use, reproduce, prepare derivative works of, publicly display, publicly perform, sublicense and distribute its Work and any resulting derivative works in any form.
+
+3. Limitations
+
+3.1 Redistribution. You may reproduce or distribute the Work only if (a) you do so under this license, (b) you include a complete copy of this license with your distribution, and (c) you retain without modification any copyright, patent, trademark, or attribution notices that are present in the Work.
+
+3.2 Derivative Works. You may specify that additional or different terms apply to the use, reproduction, and distribution of your derivative works of the Work (“Your Terms”) only if (a) Your Terms provide that the use limitation in Section 3.3 applies to your derivative works, and (b) you identify the specific derivative works that are subject to Your Terms. Notwithstanding Your Terms, this license (including the redistribution requirements in Section 3.1) will continue to apply to the Work itself.
+
+3.3 Use Limitation. The Work and any derivative works thereof only may be used or intended for use non-commercially. Notwithstanding the foregoing, NVIDIA Corporation and its affiliates may use the Work and any derivative works commercially. As used herein, “non-commercially” means for research or evaluation purposes only.
+
+3.4 Patent Claims. If you bring or threaten to bring a patent claim against any Licensor (including any claim, cross-claim or counterclaim in a lawsuit) to enforce any patents that you allege are infringed by any Work, then your rights under this license from such Licensor (including the grant in Section 2.1) will terminate immediately.
+
+3.5 Trademarks. This license does not grant any rights to use any Licensor’s or its affiliates’ names, logos, or trademarks, except as necessary to reproduce the notices described in this license.
+
+3.6 Termination. If you violate any term of this license, then your rights under this license (including the grant in Section 2.1) will terminate immediately.
+
+4. Disclaimer of Warranty.
+
+THE WORK IS PROVIDED “AS IS” WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WARRANTIES OR CONDITIONS OF 
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE OR NON-INFRINGEMENT. YOU BEAR THE RISK OF UNDERTAKING ANY ACTIVITIES UNDER THIS LICENSE. 
+
+5. Limitation of Liability.
+
+EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE SHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF OR RELATED TO THIS LICENSE, THE USE OR INABILITY TO USE THE WORK (INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS INTERRUPTION, LOST PROFITS OR DATA, COMPUTER FAILURE OR MALFUNCTION, OR ANY OTHER DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
+```
+
+## 3. MimicGen datasets (CC BY 4.0)
+
+The clean source demonstrations from which RecoverBench generates its error scenes come from the MimicGen datasets, which are released under the Creative Commons Attribution 4.0 International license (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/).
+
+- **Creators:** Ajay Mandlekar, Soroush Nasiriany, Bowen Wen, Iretiayo Akinola, Yashraj Narang, Linxi Fan, Yuke Zhu, Dieter Fox.
+- **Source:** https://huggingface.co/datasets/amandlek/mimicgen_datasets
+- **Reference:** the MimicGen paper cited in Section 2.1.
+- **Files used:** the MimicGen human source demonstrations for `pick_place`, and the MimicGen `core` D0 demonstrations for the other five tasks (`stack`, `coffee`, `threading`, `stack_three`, `three_piece_assembly`). The `dataset` field of each scene JSON records the source file and the demonstration used.
+- **Changes:** RecoverBench does not redistribute the MimicGen HDF5 files; `scripts/download_data.sh` can fetch the source demonstrations from the upstream repository. The released error scenes store simulator states taken from frames of these demonstrations after an injected error and a settling period, and the recovery trajectories start from such states. Both are therefore adapted material, and this notice indicates that changes were made.
+
+The error scenes and recovery trajectories are released under the MIT License (Section 1); the attribution above is given because they are adapted from CC BY 4.0 material. CC BY 4.0 does not apply to the MimicGen code.
+
+## 4. Other vendored and required components
+
+### robosuite (MIT)
+
+`shared/mimicgen_workspace/robosuite/` is a git submodule of robosuite (https://github.com/ARISE-Initiative/robosuite), pinned to version 1.4.1 at commit `c848ca84` (see `ROBOSUITE_VERSION_LOCK.txt`). The local changes used for the experiments are kept as a patch in `patches/robosuite-c848ca84-recoverbench.patch`; the patched code stays under robosuite's license. Text of `shared/mimicgen_workspace/robosuite/LICENSE`:
+
+```
+MIT License
+
+Copyright (c) 2022 Stanford Vision and Learning Lab and UT Robot Perception and Learning Lab
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+This software includes the partial implementation of Deepmind Mujoco https://github.com/deepmind/mujoco. 
+Deepmind Mujoco is licensed under the Apache License, Version 2.0 (the "License");
+you may not use the files except in compliance with the License. 
+
+You may obtain a copy of the License at
+    http://www.apache.org/licenses/LICENSE-2.0
+```
+
+### robosuite-task-zoo (MIT)
+
+`shared/mimicgen_workspace/robosuite-task-zoo/` is a git submodule of robosuite-task-zoo (https://github.com/ARISE-Initiative/robosuite-task-zoo); the version used for the reported results is commit `74eab7f8`. Text of `shared/mimicgen_workspace/robosuite-task-zoo/LICENSE`:
+
+```
+MIT License
+
+Copyright (c) 2021 ARISE Initiative
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+```
+
+### robomimic and MuJoCo (not vendored)
+
+robomimic (https://github.com/ARISE-Initiative/robomimic, version 0.3.0 in the reported runs) is used for the BC-RNN baselines and MuJoCo 2.3.2 is the physics engine. Both are installed from upstream and are not redistributed here. The paper's appendix lists robomimic as MIT and MuJoCo as Apache 2.0; consult the upstream repositories for the authoritative license texts. The appendix of the paper also lists the licenses of the other training and evaluation dependencies (openpi and the Pi 0.5 base weights, OpenVLA, NumPy, PyTorch, JAX), which are likewise not redistributed.
+
+## 5. Questions
+
+Please report licensing questions or missing attributions through the GitHub issue tracker: https://github.com/ErrorRecoveryBenchmark/ErrorRecoveryBenchmark/issues

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+  "url": "https://huggingface.co/datasets/ErrorRecovery/ErrorRecoveryBenchmark",
+  "license": "https://opensource.org/licenses/MIT",
+  "version": "1.1.0",
+  "keywords": [
+    "robotics",
+    "manipulation",
+    "error recovery",
+    "benchmark",
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+    "MuJoCo",
+    "robosuite",
+    "teleoperation"
+  ],
+  "creator": [
+    {
+      "@type": "Person",
+      "name": "Zijia Tang",
+      "affiliation": [
+        {
+          "@type": "Organization",
+          "name": "Duke University"
+        }
+      ]
+    },
+    {
+      "@type": "Person",
+      "name": "Ganlong Zhao",
+      "affiliation": [
+        {
+          "@type": "Organization",
+          "name": "The Chinese University of Hong Kong"
+        },
+        {
+          "@type": "Organization",
+          "name": "Centre for Perceptual and Interactive Intelligence"
+        }
+      ]
+    },
+    {
+      "@type": "Person",
+      "name": "Xingping Chen",
+      "affiliation": [
+        {
+          "@type": "Organization",
+          "name": "Sun Yat-sen University"
+        }
+      ]
+    },
+    {
+      "@type": "Person",
+      "name": "Junye Chen",
+      "affiliation": [
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+        }
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+      ]
+    },
+    {
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+      "affiliation": [
+        {
+          "@type": "Organization",
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+        }
+      ]
+    },
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+        },
+        {
+          "@type": "Organization",
+          "name": "Shenzhen Loop Area Institute"
+        },
+        {
+          "@type": "Organization",
+          "name": "Guangdong Key Laboratory of Big Data Analysis and Processing"
+        }
+      ]
+    }
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+  "dateModified": "2026-10-21",
+  "isLiveDataset": false,
+  "rai:dataCollection": "All data is generated in MuJoCo physics simulation using the robosuite framework (version-locked: robosuite 1.4.1 @ c848ca84, mujoco 2.3.2). Error scenes are created by programmatic injection of 12 error types into clean manipulation trajectories, with per-scene environment fingerprints and RNG states for deterministic reproduction. Recovery demonstrations are collected via human teleoperation with a SpaceMouse 3D input device; augmented demonstrations are generated with MimicGen-style trajectory adaptation and validated by simulated replay. Human operators are members of the author team. The released observations depict simulated scenes.",
+  "rai:personalSensitiveInformation": "No human imagery, speech, demographic annotations, or operator identities are collected as dataset features. Historical scene sidecars include technical source paths from the collection environment.",
+  "rai:dataBiases": "The benchmark uses mixed robot embodiments (Sawyer for pick_place, Franka Panda for the other five tasks) in tabletop manipulation settings. Error injection parameters are hand-designed based on common failure modes observed in robotic manipulation. Human recovery demonstrations reflect the skill level and strategies of a small set of operators and do not cover every task-subtype combination uniformly. The 6 tasks are limited to tabletop pick-and-place style manipulation.",
+  "rai:dataLimitations": "Simulation-only tabletop tasks with Sawyer and Panda embodiments, six tasks and hand-designed errors. Coverage varies by task and subtype. Results do not establish real-world safety. Two repaired human records lack original camera streams; supplemental state-rendered images have different rendering settings and are not original observations. Recorded HDF5 model XML contains historical asset paths; normalize with edit_model_xml before replay. Structural validation does not certify action replay.",
+  "rai:dataUseCases": "Evaluation of recovery after injected manipulation errors paired with clean-start success, error-type analysis, and recovery-policy training. Construct: completion of the original simulated task after a controlled execution error. Not validated for real-robot deployment, safety certification, human fairness assessment, or naturally occurring error distributions.",
+  "rai:dataSocialImpact": "May improve transparency about robot failure recovery and support reproducibility. Misuse includes treating simulation success as evidence of safe deployment. Report clean and recovery scores together and disclose simulator, policy, and data limitations.",
+  "rai:hasSyntheticData": true,
+  "prov:wasDerivedFrom": [
+    {
+      "@id": "https://huggingface.co/datasets/amandlek/mimicgen_datasets"
+    }
+  ],
+  "prov:wasGeneratedBy": {
+    "@type": "prov:Activity",
+    "name": "RecoverBench simulation, teleoperation and augmentation pipeline",
+    "description": "Replay upstream clean demonstrations, inject parameterized error skills, snapshot simulator states, collect SpaceMouse recoveries, and augment trajectories with MimicGen. See DATASHEET.md for collection and validation procedures; archive members and hashes are recorded in metadata/release_inventory.json."
+  },
+  "citeAs": "Zijia Tang, Ganlong Zhao, Xingping Chen, Junye Chen, Zihao Mo, Zedi Wang, Guanbin Li. RecoverBench: A Systematic Benchmark for Error Recovery in Robotic Manipulation. NeurIPS 2026, Evaluations and Datasets Track (accepted).",
+  "funder": [
+    {
+      "@type": "Organization",
+      "name": "National Natural Science Foundation of China"
+    },
+    {
+      "@type": "Organization",
+      "name": "Shenzhen Loop Area Institute"
+    }
+  ],
+  "datePublished": "2026-10-21"
+}

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+pick_place	v5_drop_at_wrong_place_D0_010c091ffb8e
+pick_place	v5_drop_at_wrong_place_D0_017ea13ff9b8
+pick_place	v5_drop_at_wrong_place_D0_0208f336a4e4
+pick_place	v5_drop_at_wrong_place_D0_0237c4acd8ca
+pick_place	v5_drop_at_wrong_place_D0_02a17e8512c1
+pick_place	v5_drop_at_wrong_place_D0_02b0d88c7348
+pick_place	v5_drop_at_wrong_place_D0_033a1dc2e497
+pick_place	v5_drop_at_wrong_place_D0_03f8842ea071
+pick_place	v5_drop_at_wrong_place_D0_0406eb9621fe
+pick_place	v5_drop_at_wrong_place_D0_042391cca1d7
+pick_place	v5_drop_at_wrong_place_D1_00ac2e8fe591
+pick_place	v5_drop_at_wrong_place_D1_00c968fec33d
+pick_place	v5_drop_at_wrong_place_D1_00cdab924b8f
+pick_place	v5_drop_at_wrong_place_D1_00fb70de6374
+pick_place	v5_drop_at_wrong_place_D1_014059249a8f
+pick_place	v5_drop_at_wrong_place_D1_01af8a5e1787
+pick_place	v5_drop_at_wrong_place_D1_01f5604c5bdb
+pick_place	v5_drop_at_wrong_place_D1_0226b1581322
+pick_place	v5_drop_at_wrong_place_D1_02cba9ade53d
+pick_place	v5_drop_at_wrong_place_D1_02ef04309a4e
+pick_place	v5_drop_in_transit_D0_001a60613c2c
+pick_place	v5_drop_in_transit_D0_001cf8a54a34
+pick_place	v5_drop_in_transit_D0_00419479f921
+pick_place	v5_drop_in_transit_D0_007631d0b1e4
+pick_place	v5_drop_in_transit_D0_007dbdf5e7ea
+pick_place	v5_drop_in_transit_D0_00debfb36b9d
+pick_place	v5_drop_in_transit_D0_00f14e935146
+pick_place	v5_drop_in_transit_D0_015bd9bf7297
+pick_place	v5_drop_in_transit_D0_0194e8ac55ec
+pick_place	v5_drop_in_transit_D0_01b4d9c89179
+pick_place	v5_drop_in_transit_D1_000a07d15eac
+pick_place	v5_drop_in_transit_D1_0027ecd5161c
+pick_place	v5_drop_in_transit_D1_00a7c6fe042b
+pick_place	v5_drop_in_transit_D1_00adf79f5562
+pick_place	v5_drop_in_transit_D1_00d94ae8ca98
+pick_place	v5_drop_in_transit_D1_00ea61f5df6a
+pick_place	v5_drop_in_transit_D1_0138ccc25541
+pick_place	v5_drop_in_transit_D1_0142e2ccd948
+pick_place	v5_drop_in_transit_D1_01512f53e0b0
+pick_place	v5_drop_in_transit_D1_016ae40bb12d
+pick_place	v5_grasp_misalignment_D0_0046ff1d7bf6
+pick_place	v5_grasp_misalignment_D0_00a2db7d13ef
+pick_place	v5_grasp_misalignment_D0_00a4276dbccb
+pick_place	v5_grasp_misalignment_D0_00dce714f560
+pick_place	v5_grasp_misalignment_D0_01946f95ace5
+pick_place	v5_grasp_misalignment_D0_01da01b33a52
+pick_place	v5_grasp_misalignment_D0_02668a613d10
+pick_place	v5_grasp_misalignment_D0_02d3e0848a7c
+pick_place	v5_grasp_misalignment_D0_030603337b5a
+pick_place	v5_grasp_misalignment_D0_03089ec5c970
+pick_place	v5_grasp_misalignment_D1_0020815b757d
+pick_place	v5_grasp_misalignment_D1_0022f8ce75e5
+pick_place	v5_grasp_misalignment_D1_0030b1685536
+pick_place	v5_grasp_misalignment_D1_003b4422b773
+pick_place	v5_grasp_misalignment_D1_00449c9fad8c
+pick_place	v5_grasp_misalignment_D1_008fe4718ca3
+pick_place	v5_grasp_misalignment_D1_00b4d94d7168
+pick_place	v5_grasp_misalignment_D1_00c545c5713f
+pick_place	v5_grasp_misalignment_D1_01466fe776a1
+pick_place	v5_grasp_misalignment_D1_0160be05e126
+pick_place	v5_grasp_wrong_pose_D0_00280dcd9f50
+pick_place	v5_grasp_wrong_pose_D0_00635a82e4e1
+pick_place	v5_grasp_wrong_pose_D0_006485951b14
+pick_place	v5_grasp_wrong_pose_D0_00d0f1e26dc1
+pick_place	v5_grasp_wrong_pose_D0_0170aeff4620
+pick_place	v5_grasp_wrong_pose_D0_03304ebfda88
+pick_place	v5_grasp_wrong_pose_D0_0348e2097392
+pick_place	v5_grasp_wrong_pose_D0_039a4af9ff32
+pick_place	v5_grasp_wrong_pose_D0_03ae3ccd6e56
+pick_place	v5_grasp_wrong_pose_D0_04585ee15057
+pick_place	v5_grasp_wrong_pose_D1_00534328c760
+pick_place	v5_grasp_wrong_pose_D1_00d14cfd1ea8
+pick_place	v5_grasp_wrong_pose_D1_00f592c0ab4d
+pick_place	v5_grasp_wrong_pose_D1_0171165da58b
+pick_place	v5_grasp_wrong_pose_D1_01e32f5ad575
+pick_place	v5_grasp_wrong_pose_D1_01edd821566f
+pick_place	v5_grasp_wrong_pose_D1_025965317d1a
+pick_place	v5_grasp_wrong_pose_D1_025c8433f5b9
+pick_place	v5_grasp_wrong_pose_D1_02b861923e73
+pick_place	v5_grasp_wrong_pose_D1_037a393b03c0
+pick_place	v5_position_error_D0_00a216f25d3e
+pick_place	v5_position_error_D0_00eea8474e74
+pick_place	v5_position_error_D0_012dc0126426
+pick_place	v5_position_error_D0_0134e13df1c2
+pick_place	v5_position_error_D0_0145e48d6228
+pick_place	v5_position_error_D0_014c4096ff14
+pick_place	v5_position_error_D0_014d4c909a88
+pick_place	v5_position_error_D0_0168df4ee20f
+pick_place	v5_position_error_D0_01e9eebd16db
+pick_place	v5_position_error_D0_022df9c25ff9
+pick_place	v5_position_error_D1_00842c853fa8
+pick_place	v5_position_error_D1_00be1f7ed0f5
+pick_place	v5_position_error_D1_00e15c810971
+pick_place	v5_position_error_D1_012f5204912f
+pick_place	v5_position_error_D1_019a7a960772
+pick_place	v5_position_error_D1_01a01e35f120
+pick_place	v5_position_error_D1_020d607e846c
+pick_place	v5_position_error_D1_0256f5f6d85f
+pick_place	v5_position_error_D1_027dd7e3bcf5
+pick_place	v5_position_error_D1_028604cc01a6
+pick_place	v5_stuck_no_progress_D0_002214161ca9
+pick_place	v5_stuck_no_progress_D0_00ebd31466fc
+pick_place	v5_stuck_no_progress_D0_00f8113bb303
+pick_place	v5_stuck_no_progress_D0_011a4417abc3
+pick_place	v5_stuck_no_progress_D0_012b034fabdd
+pick_place	v5_stuck_no_progress_D0_0165f64db884
+pick_place	v5_stuck_no_progress_D0_01b47a0e3a7b
+pick_place	v5_stuck_no_progress_D0_01e4f42e6eee
+pick_place	v5_stuck_no_progress_D0_02023abe896b
+pick_place	v5_stuck_no_progress_D0_02c915ad2e45
+pick_place	v5_stuck_no_progress_D1_0043cbc842b8
+pick_place	v5_stuck_no_progress_D1_0056c33ea7b7
+pick_place	v5_stuck_no_progress_D1_0091a900634f
+pick_place	v5_stuck_no_progress_D1_0101851ea316
+pick_place	v5_stuck_no_progress_D1_010389758a50
+pick_place	v5_stuck_no_progress_D1_016dd98a4e01
+pick_place	v5_stuck_no_progress_D1_017c20577a2d
+pick_place	v5_stuck_no_progress_D1_01929ba59815
+pick_place	v5_stuck_no_progress_D1_01d3ae4d2aa1
+pick_place	v5_stuck_no_progress_D1_01ddd609f406
+pick_place	v5_trajectory_regression_D0_009148caca51
+pick_place	v5_trajectory_regression_D0_0110c1203bcf
+pick_place	v5_trajectory_regression_D0_0171060db4ac
+pick_place	v5_trajectory_regression_D0_01f927634499
+pick_place	v5_trajectory_regression_D0_026510d69072
+pick_place	v5_trajectory_regression_D0_0276af74f006
+pick_place	v5_trajectory_regression_D0_02d3654a80f6
+pick_place	v5_trajectory_regression_D0_02fed2671c52
+pick_place	v5_trajectory_regression_D0_03032e5b952e
+pick_place	v5_trajectory_regression_D0_03399990de72
+pick_place	v5_trajectory_regression_D1_0006ce2868d0
+pick_place	v5_trajectory_regression_D1_00b45a0df4e5
+pick_place	v5_trajectory_regression_D1_00ba816c6137
+pick_place	v5_trajectory_regression_D1_00d9e6c17bca
+pick_place	v5_trajectory_regression_D1_00f5526350c2
+pick_place	v5_trajectory_regression_D1_01512fe35819
+pick_place	v5_trajectory_regression_D1_016c2a036630
+pick_place	v5_trajectory_regression_D1_018565ce5a7c
+pick_place	v5_trajectory_regression_D1_018eea4abd79
+pick_place	v5_trajectory_regression_D1_01a29fe3cfec
+pick_place	v5_wrong_object_D0_00400a249295
+pick_place	v5_wrong_object_D0_00675d00b0f2
+pick_place	v5_wrong_object_D0_00b5824391c7
+pick_place	v5_wrong_object_D0_00f782f32880
+pick_place	v5_wrong_object_D0_0111945b9661
+pick_place	v5_wrong_object_D0_017703b7ce96
+pick_place	v5_wrong_object_D0_0187e503b1b2
+pick_place	v5_wrong_object_D0_01ca159c5688
+pick_place	v5_wrong_object_D0_01d0f51c5d64
+pick_place	v5_wrong_object_D0_01d9e4416f4a
+pick_place	v5_wrong_object_D1_00029ddb5734
+pick_place	v5_wrong_object_D1_001ad9f0a357
+pick_place	v5_wrong_object_D1_005873005372
+pick_place	v5_wrong_object_D1_006418df05e1
+pick_place	v5_wrong_object_D1_00f7abb544de
+pick_place	v5_wrong_object_D1_013e53faf3c9
+pick_place	v5_wrong_object_D1_0191860e915d
+pick_place	v5_wrong_object_D1_01b79b58a6e8
+pick_place	v5_wrong_object_D1_02adae694ab6
+pick_place	v5_wrong_object_D1_030e770f226d
+stack	v5_collision_eef_object_D0_00731d8acf70
+stack	v5_collision_eef_object_D0_008d9efee56b
+stack	v5_collision_eef_object_D0_00e4ca5d8cc8
+stack	v5_collision_eef_object_D0_01149c80a42c
+stack	v5_collision_eef_object_D0_0167632a38c3
+stack	v5_collision_eef_object_D0_019a373af87f
+stack	v5_collision_eef_object_D0_01fac118c8cf
+stack	v5_collision_eef_object_D0_021fe1953c91
+stack	v5_collision_eef_object_D0_028c8709c5dc
+stack	v5_collision_eef_object_D0_02d03115ce14
+stack	v5_collision_eef_object_D1_001c30e4658e
+stack	v5_collision_eef_object_D1_007fec20c127
+stack	v5_collision_eef_object_D1_00bff2723749
+stack	v5_collision_eef_object_D1_00efba9f6ad3
+stack	v5_collision_eef_object_D1_024aa1d26995
+stack	v5_collision_eef_object_D1_029252d649d2
+stack	v5_collision_eef_object_D1_0293c7e49247
+stack	v5_collision_eef_object_D1_02e09482618c
+stack	v5_collision_eef_object_D1_02fb27ed457b
+stack	v5_collision_eef_object_D1_03246b3d7dab
+stack	v5_collision_empty_D0_0021835e377b
+stack	v5_collision_empty_D0_00230e781dd9
+stack	v5_collision_empty_D0_00449b0d319e
+stack	v5_collision_empty_D0_00817ba8d27f
+stack	v5_collision_empty_D0_00884db3d78b
+stack	v5_collision_empty_D0_00ce2e0279ea
+stack	v5_collision_empty_D0_01b2399bb178
+stack	v5_collision_empty_D0_01e85c7b628b
+stack	v5_collision_empty_D0_01f55bba8452
+stack	v5_collision_empty_D0_0210109c3143
+stack	v5_collision_empty_D1_0008ecdfd34b
+stack	v5_collision_empty_D1_00509c254f23
+stack	v5_collision_empty_D1_00dfee433cc3
+stack	v5_collision_empty_D1_0104f24b2324
+stack	v5_collision_empty_D1_01053d35f447
+stack	v5_collision_empty_D1_0110fd5f0a73
+stack	v5_collision_empty_D1_0116b1f16bf8
+stack	v5_collision_empty_D1_0119e796fab3
+stack	v5_collision_empty_D1_0131a091ace5
+stack	v5_collision_empty_D1_017beda4c86e
+stack	v5_collision_holding_D0_00322453d5dd
+stack	v5_collision_holding_D0_00329936487c
+stack	v5_collision_holding_D0_005181f1bb2d
+stack	v5_collision_holding_D0_006dd56a4679
+stack	v5_collision_holding_D0_00ca674f09db
+stack	v5_collision_holding_D0_00d23ab35e9e
+stack	v5_collision_holding_D0_01550bc902b8
+stack	v5_collision_holding_D0_018fc9bbc1e9
+stack	v5_collision_holding_D0_01f3606316d6
+stack	v5_collision_holding_D0_025e12dd16ca
+stack	v5_collision_holding_D1_006a748decf1
+stack	v5_collision_holding_D1_00871e22f017
+stack	v5_collision_holding_D1_01779dd7ba4b
+stack	v5_collision_holding_D1_019b8503b514
+stack	v5_collision_holding_D1_01cb7ec14a74
+stack	v5_collision_holding_D1_028d2ca80258
+stack	v5_collision_holding_D1_0297a06171d7
+stack	v5_collision_holding_D1_029a71a800b1
+stack	v5_collision_holding_D1_029afd40d0f6
+stack	v5_collision_holding_D1_02c9610cce91
+stack	v5_collision_self_D0_0030df31c58f
+stack	v5_collision_self_D0_004c5c2b88d5
+stack	v5_collision_self_D0_006a1249a9f2
+stack	v5_collision_self_D0_00777bbeceb8
+stack	v5_collision_self_D0_0081a9f96438
+stack	v5_collision_self_D0_00cba89dbe8a
+stack	v5_collision_self_D0_00d737b79cc0
+stack	v5_collision_self_D0_00dbd546a2b9
+stack	v5_collision_self_D0_00efd8749e1b
+stack	v5_collision_self_D0_015b5b10669f
+stack	v5_collision_self_D1_0054c6485644
+stack	v5_collision_self_D1_00647afa5fb3
+stack	v5_collision_self_D1_0081f5615952
+stack	v5_collision_self_D1_00c80ffa381e
+stack	v5_collision_self_D1_00e4c0027663
+stack	v5_collision_self_D1_016381d0cee5
+stack	v5_collision_self_D1_023603afe8b8
+stack	v5_collision_self_D1_0239687a662e
+stack	v5_collision_self_D1_0287a8ae4e15
+stack	v5_collision_self_D1_03357c3623bf
+stack	v5_drop_at_wrong_place_D0_00beb5227b1d
+stack	v5_drop_at_wrong_place_D0_00fc10662dda
+stack	v5_drop_at_wrong_place_D0_017b51394c99
+stack	v5_drop_at_wrong_place_D0_01d5846c7a7a
+stack	v5_drop_at_wrong_place_D0_01e5b18e9bc5
+stack	v5_drop_at_wrong_place_D0_01f57cb795da
+stack	v5_drop_at_wrong_place_D0_022988fa19e1
+stack	v5_drop_at_wrong_place_D0_0250ba5b3089
+stack	v5_drop_at_wrong_place_D0_0268829cfee4
+stack	v5_drop_at_wrong_place_D0_0285cddf9873
+stack	v5_drop_at_wrong_place_D1_006120da4d30
+stack	v5_drop_at_wrong_place_D1_0078082b8b78
+stack	v5_drop_at_wrong_place_D1_008b13f7c9e7
+stack	v5_drop_at_wrong_place_D1_00c04c09d01d
+stack	v5_drop_at_wrong_place_D1_01151889b5fa
+stack	v5_drop_at_wrong_place_D1_011ee992cc45
+stack	v5_drop_at_wrong_place_D1_01f2a4426ca2
+stack	v5_drop_at_wrong_place_D1_01fcf3b97d2c
+stack	v5_drop_at_wrong_place_D1_025e904a7a69
+stack	v5_drop_at_wrong_place_D1_0263b8cf4fd7
+stack	v5_drop_in_transit_D0_0003ae83c275
+stack	v5_drop_in_transit_D0_002cbcad4a9c
+stack	v5_drop_in_transit_D0_002de86954fc
+stack	v5_drop_in_transit_D0_0047701dca39
+stack	v5_drop_in_transit_D0_00a7666bfcb1
+stack	v5_drop_in_transit_D0_00b2c09bc7a2
+stack	v5_drop_in_transit_D0_0100dc54fbbb
+stack	v5_drop_in_transit_D0_0108c521c5f5
+stack	v5_drop_in_transit_D0_01f576af72a7
+stack	v5_drop_in_transit_D0_01f64f76148e
+stack	v5_drop_in_transit_D1_00126060ff8f
+stack	v5_drop_in_transit_D1_00b00621324e
+stack	v5_drop_in_transit_D1_00b6312482cd
+stack	v5_drop_in_transit_D1_00ce8fb67ea4
+stack	v5_drop_in_transit_D1_01ac8b77a652
+stack	v5_drop_in_transit_D1_01f27fd0e10e
+stack	v5_drop_in_transit_D1_024ec822e580
+stack	v5_drop_in_transit_D1_0256a2d31206
+stack	v5_drop_in_transit_D1_026533846d22
+stack	v5_drop_in_transit_D1_0269723ed5b3
+stack	v5_grasp_misalignment_D0_0000af7128f9
+stack	v5_grasp_misalignment_D0_007c60a08a42
+stack	v5_grasp_misalignment_D0_0131d049871f
+stack	v5_grasp_misalignment_D0_01359f5e7d4f
+stack	v5_grasp_misalignment_D0_01b7e2e83904
+stack	v5_grasp_misalignment_D0_01c57ea0c829
+stack	v5_grasp_misalignment_D0_01d2eb2de01c
+stack	v5_grasp_misalignment_D0_01fe2cd9be97
+stack	v5_grasp_misalignment_D0_02210bea7940
+stack	v5_grasp_misalignment_D0_0256d53750a0
+stack	v5_grasp_misalignment_D1_002af7ee2176
+stack	v5_grasp_misalignment_D1_00c81f8f2238
+stack	v5_grasp_misalignment_D1_0144574e221a
+stack	v5_grasp_misalignment_D1_0148c7c5f37e
+stack	v5_grasp_misalignment_D1_015156d84a84
+stack	v5_grasp_misalignment_D1_01c770278c95
+stack	v5_grasp_misalignment_D1_01e258abb4cb
+stack	v5_grasp_misalignment_D1_01e32e81cd4c
+stack	v5_grasp_misalignment_D1_0215c01cb506
+stack	v5_grasp_misalignment_D1_0256517cd753
+stack	v5_grasp_wrong_pose_D0_004349b940ef
+stack	v5_grasp_wrong_pose_D0_010f56c34e22
+stack	v5_grasp_wrong_pose_D0_0115d6b13c97
+stack	v5_grasp_wrong_pose_D0_016abf8dd552
+stack	v5_grasp_wrong_pose_D0_019650670034
+stack	v5_grasp_wrong_pose_D0_01a9d188c54b
+stack	v5_grasp_wrong_pose_D0_01c17d3e1d78
+stack	v5_grasp_wrong_pose_D0_023c2cd6a180
+stack	v5_grasp_wrong_pose_D0_0297b73ddb68
+stack	v5_grasp_wrong_pose_D0_02a8fc25d8cb
+stack	v5_grasp_wrong_pose_D1_00065b1a624c
+stack	v5_grasp_wrong_pose_D1_00e65e56c4df
+stack	v5_grasp_wrong_pose_D1_00fb0e93a16d
+stack	v5_grasp_wrong_pose_D1_010c1bc3d731
+stack	v5_grasp_wrong_pose_D1_0150cace8840
+stack	v5_grasp_wrong_pose_D1_01cb9206f1e7
+stack	v5_grasp_wrong_pose_D1_025959a9c80d
+stack	v5_grasp_wrong_pose_D1_030565f96066
+stack	v5_grasp_wrong_pose_D1_0359e680b183
+stack	v5_grasp_wrong_pose_D1_0381d495a65f
+stack	v5_position_error_D0_001d263ec15e
+stack	v5_position_error_D0_007fd76d8807
+stack	v5_position_error_D0_00ce12c8c932
+stack	v5_position_error_D0_01d84ecde916
+stack	v5_position_error_D0_01e624853d35
+stack	v5_position_error_D0_0218a0e04d90
+stack	v5_position_error_D0_026f8eb3ba09
+stack	v5_position_error_D0_02c82b3a25d4
+stack	v5_position_error_D0_02c85ef4ee43
+stack	v5_position_error_D0_033aaec64aab
+stack	v5_position_error_D1_005697fbf292
+stack	v5_position_error_D1_008ceec022e9
+stack	v5_position_error_D1_01196d4aea33
+stack	v5_position_error_D1_012a7dd09e89
+stack	v5_position_error_D1_01ba4be88de8
+stack	v5_position_error_D1_01da3a33b697
+stack	v5_position_error_D1_025880455f9d
+stack	v5_position_error_D1_025e472d43c4
+stack	v5_position_error_D1_02972d340499
+stack	v5_position_error_D1_029a06f5b566
+stack	v5_stuck_no_progress_D0_001131905ab3
+stack	v5_stuck_no_progress_D0_0019b610e8d7
+stack	v5_stuck_no_progress_D0_002e24d76c0c
+stack	v5_stuck_no_progress_D0_00dac00ba2ab
+stack	v5_stuck_no_progress_D0_00dd4ac80c22
+stack	v5_stuck_no_progress_D0_012f67b11c68
+stack	v5_stuck_no_progress_D0_0197c1b1d3cf
+stack	v5_stuck_no_progress_D0_01a87b736018
+stack	v5_stuck_no_progress_D0_01abcdfbe5a6
+stack	v5_stuck_no_progress_D0_0212c0ac36ba
+stack	v5_stuck_no_progress_D1_00262af5b0ad
+stack	v5_stuck_no_progress_D1_0129e3632b87
+stack	v5_stuck_no_progress_D1_01339e75222f
+stack	v5_stuck_no_progress_D1_013cd9ffcfd1
+stack	v5_stuck_no_progress_D1_0140228e48fe
+stack	v5_stuck_no_progress_D1_0196496f3392
+stack	v5_stuck_no_progress_D1_02023c1d4ddd
+stack	v5_stuck_no_progress_D1_02bf5da823e4
+stack	v5_stuck_no_progress_D1_02ce672f76b8
+stack	v5_stuck_no_progress_D1_0308e7d7c96e
+stack	v5_trajectory_regression_D0_00679485f808
+stack	v5_trajectory_regression_D0_0076a2e1c4a5
+stack	v5_trajectory_regression_D0_00c7d514a199
+stack	v5_trajectory_regression_D0_00ff18ec84a5
+stack	v5_trajectory_regression_D0_00ff557c963a
+stack	v5_trajectory_regression_D0_017ea2ea32c0
+stack	v5_trajectory_regression_D0_0189011bb78d
+stack	v5_trajectory_regression_D0_021fac58b70f
+stack	v5_trajectory_regression_D0_0284bbb6e38d
+stack	v5_trajectory_regression_D0_02aa57defb85
+stack	v5_trajectory_regression_D1_0004b2cb5790
+stack	v5_trajectory_regression_D1_007591427c52
+stack	v5_trajectory_regression_D1_00babe1923fd
+stack	v5_trajectory_regression_D1_00c77bfc0388
+stack	v5_trajectory_regression_D1_00df5d5a3eed
+stack	v5_trajectory_regression_D1_00fd1da8e953
+stack	v5_trajectory_regression_D1_01e13d802d40
+stack	v5_trajectory_regression_D1_01e3719fc6b0
+stack	v5_trajectory_regression_D1_037d80ae3856
+stack	v5_trajectory_regression_D1_05be473ea021
+stack	v5_wrong_object_D0_00459d4dd4fe
+stack	v5_wrong_object_D0_0046c1c145e1
+stack	v5_wrong_object_D0_006798307e67
+stack	v5_wrong_object_D0_00b23860bf51
+stack	v5_wrong_object_D0_01238a10f337
+stack	v5_wrong_object_D0_0140411d0ff0
+stack	v5_wrong_object_D0_02b84a086c21
+stack	v5_wrong_object_D0_02bad2fb3c1a
+stack	v5_wrong_object_D0_02c6cce52ec7
+stack	v5_wrong_object_D0_02e4725577ea
+stack	v5_wrong_object_D1_0012b3083eba
+stack	v5_wrong_object_D1_00426518f553
+stack	v5_wrong_object_D1_004eaae6e488
+stack	v5_wrong_object_D1_00778265498c
+stack	v5_wrong_object_D1_00951761842b
+stack	v5_wrong_object_D1_00cd2c88c8dc
+stack	v5_wrong_object_D1_00d876da6221
+stack	v5_wrong_object_D1_00fd691f406f
+stack	v5_wrong_object_D1_020d91f78ebc
+stack	v5_wrong_object_D1_0212c773dd48
+stack_three	v5_collision_eef_object_D0_00117d6111d6
+stack_three	v5_collision_eef_object_D0_00b5275ca286
+stack_three	v5_collision_eef_object_D0_00f7a3ea4fbd
+stack_three	v5_collision_eef_object_D0_0135c84c89bf
+stack_three	v5_collision_eef_object_D0_016ff72118d1
+stack_three	v5_collision_eef_object_D0_01ba8839f29a
+stack_three	v5_collision_eef_object_D0_01be4c3d2e79
+stack_three	v5_collision_eef_object_D0_01d97d6882dd
+stack_three	v5_collision_eef_object_D0_032a28a5a41e
+stack_three	v5_collision_eef_object_D0_034c75e21818
+stack_three	v5_collision_eef_object_D1_010c6ea43b57
+stack_three	v5_collision_eef_object_D1_01a3cb71f071
+stack_three	v5_collision_eef_object_D1_024add3acc37
+stack_three	v5_collision_eef_object_D1_0269ab497afd
+stack_three	v5_collision_eef_object_D1_026a475df8a8
+stack_three	v5_collision_eef_object_D1_028b708956ae
+stack_three	v5_collision_eef_object_D1_02b789ad9aca
+stack_three	v5_collision_eef_object_D1_0302f4678a17
+stack_three	v5_collision_eef_object_D1_034579f93e0b
+stack_three	v5_collision_eef_object_D1_03faa77c0fdb
+stack_three	v5_collision_empty_D0_001705f8d10b
+stack_three	v5_collision_empty_D0_00840b4e71bd
+stack_three	v5_collision_empty_D0_00895ee34ef2
+stack_three	v5_collision_empty_D0_01357ab130f6
+stack_three	v5_collision_empty_D0_01978d3d80c1
+stack_three	v5_collision_empty_D0_01b498cad581
+stack_three	v5_collision_empty_D0_0225a18dfe0a
+stack_three	v5_collision_empty_D0_02562f8b7fa6
+stack_three	v5_collision_empty_D0_0273973dd7d2
+stack_three	v5_collision_empty_D0_0298c188311d
+stack_three	v5_collision_empty_D1_00ff276f50f2
+stack_three	v5_collision_empty_D1_01454c984941
+stack_three	v5_collision_empty_D1_01a45409c91b
+stack_three	v5_collision_empty_D1_01ce3b3a8fcf
+stack_three	v5_collision_empty_D1_03bc023181f2
+stack_three	v5_collision_empty_D1_041f56b5c2bf
+stack_three	v5_collision_empty_D1_0443d95cb006
+stack_three	v5_collision_empty_D1_04a606db120f
+stack_three	v5_collision_empty_D1_0559ff5c2dd3
+stack_three	v5_collision_empty_D1_05c9ffc617a8
+stack_three	v5_collision_holding_D0_00217739da0e
+stack_three	v5_collision_holding_D0_005155a7087d
+stack_three	v5_collision_holding_D0_00b5b3eadc8d
+stack_three	v5_collision_holding_D0_00c84dbc439e
+stack_three	v5_collision_holding_D0_00efb6720543
+stack_three	v5_collision_holding_D0_016eb2120be2
+stack_three	v5_collision_holding_D0_0193bc69ab68
+stack_three	v5_collision_holding_D0_01b7441d187f
+stack_three	v5_collision_holding_D0_01bb799de68d
+stack_three	v5_collision_holding_D0_01ef47c77471
+stack_three	v5_collision_holding_D1_0000100a8809
+stack_three	v5_collision_holding_D1_0006eaf5d483
+stack_three	v5_collision_holding_D1_00904d717801
+stack_three	v5_collision_holding_D1_00b95c749f29
+stack_three	v5_collision_holding_D1_00d12ba46317
+stack_three	v5_collision_holding_D1_00fd34949e7f
+stack_three	v5_collision_holding_D1_0206ff684f0a
+stack_three	v5_collision_holding_D1_021c19a30310
+stack_three	v5_collision_holding_D1_025a22b16d31
+stack_three	v5_collision_holding_D1_02f0223ec4c2
+stack_three	v5_collision_self_D0_00a9bf6e140d
+stack_three	v5_collision_self_D0_013d2d37b8ad
+stack_three	v5_collision_self_D0_0146852393b8
+stack_three	v5_collision_self_D0_01c539647e77
+stack_three	v5_collision_self_D0_01d06432ccd7
+stack_three	v5_collision_self_D0_02049269cb65
+stack_three	v5_collision_self_D0_024612393422
+stack_three	v5_collision_self_D0_02c5deb2fe23
+stack_three	v5_collision_self_D0_037b19a39779
+stack_three	v5_collision_self_D0_03836e289a54
+stack_three	v5_collision_self_D1_007151825d52
+stack_three	v5_collision_self_D1_00d80383f258
+stack_three	v5_collision_self_D1_00eb330c8691
+stack_three	v5_collision_self_D1_012d3af415b8
+stack_three	v5_collision_self_D1_0160adc690e7
+stack_three	v5_collision_self_D1_01c73e83bc67
+stack_three	v5_collision_self_D1_022f43964791
+stack_three	v5_collision_self_D1_023bde2b28c0
+stack_three	v5_collision_self_D1_02517bbb44bc
+stack_three	v5_collision_self_D1_025e4418a55e
+stack_three	v5_drop_at_wrong_place_D0_0046998b62d2
+stack_three	v5_drop_at_wrong_place_D0_005303be3502
+stack_three	v5_drop_at_wrong_place_D0_00d4fea1910d
+stack_three	v5_drop_at_wrong_place_D0_00d8a83db9e1
+stack_three	v5_drop_at_wrong_place_D0_0103f47de8af
+stack_three	v5_drop_at_wrong_place_D0_0107b028aa48
+stack_three	v5_drop_at_wrong_place_D0_0111d06f28bb
+stack_three	v5_drop_at_wrong_place_D0_01195ef1b39d
+stack_three	v5_drop_at_wrong_place_D0_0140b2bb3d3d
+stack_three	v5_drop_at_wrong_place_D0_0199b60038e8
+stack_three	v5_drop_at_wrong_place_D1_0066e512eb4c
+stack_three	v5_drop_at_wrong_place_D1_006d7f91e36e
+stack_three	v5_drop_at_wrong_place_D1_00a9bc049da3
+stack_three	v5_drop_at_wrong_place_D1_00bf1adbd160
+stack_three	v5_drop_at_wrong_place_D1_00d504c523e0
+stack_three	v5_drop_at_wrong_place_D1_01358e302abb
+stack_three	v5_drop_at_wrong_place_D1_01588e54fcde
+stack_three	v5_drop_at_wrong_place_D1_01767851313a
+stack_three	v5_drop_at_wrong_place_D1_017d65a7d48d
+stack_three	v5_drop_at_wrong_place_D1_01b32dd9840c
+stack_three	v5_drop_in_transit_D0_0046f759b229
+stack_three	v5_drop_in_transit_D0_004b90649d11
+stack_three	v5_drop_in_transit_D0_008ad9f22d32
+stack_three	v5_drop_in_transit_D0_00bda5357551
+stack_three	v5_drop_in_transit_D0_00c771d5d82e
+stack_three	v5_drop_in_transit_D0_011f04303fab
+stack_three	v5_drop_in_transit_D0_0130ff8394fd
+stack_three	v5_drop_in_transit_D0_0156197a2b80
+stack_three	v5_drop_in_transit_D0_01cccbc3a7b5
+stack_three	v5_drop_in_transit_D0_01d691f2c2b3
+stack_three	v5_drop_in_transit_D1_00a05d455384
+stack_three	v5_drop_in_transit_D1_00ae0c7fc235
+stack_three	v5_drop_in_transit_D1_013a72ee3e0a
+stack_three	v5_drop_in_transit_D1_0147c710d185
+stack_three	v5_drop_in_transit_D1_01af1b3884fd
+stack_three	v5_drop_in_transit_D1_01e5201ed248
+stack_three	v5_drop_in_transit_D1_025239b8cd3d
+stack_three	v5_drop_in_transit_D1_0295349dc5ee
+stack_three	v5_drop_in_transit_D1_033457780a2c
+stack_three	v5_drop_in_transit_D1_03b8705f0c37
+stack_three	v5_grasp_misalignment_D0_003e35ddd1da
+stack_three	v5_grasp_misalignment_D0_009dd70173d5
+stack_three	v5_grasp_misalignment_D0_014068512ca4
+stack_three	v5_grasp_misalignment_D0_016d729cd9be
+stack_three	v5_grasp_misalignment_D0_01969ec40612
+stack_three	v5_grasp_misalignment_D0_01a8270c2072
+stack_three	v5_grasp_misalignment_D0_01d19ba6f850
+stack_three	v5_grasp_misalignment_D0_01f189f96c9a
+stack_three	v5_grasp_misalignment_D0_029587c55c34
+stack_three	v5_grasp_misalignment_D0_0366391b9088
+stack_three	v5_grasp_misalignment_D1_0015253e8733
+stack_three	v5_grasp_misalignment_D1_00771ee02ec2
+stack_three	v5_grasp_misalignment_D1_00f68bb742a6
+stack_three	v5_grasp_misalignment_D1_012572f852b7
+stack_three	v5_grasp_misalignment_D1_013ac77c5c51
+stack_three	v5_grasp_misalignment_D1_01721a78187a
+stack_three	v5_grasp_misalignment_D1_01730fa48714
+stack_three	v5_grasp_misalignment_D1_017b023b82c9
+stack_three	v5_grasp_misalignment_D1_01a28e6ffb13
+stack_three	v5_grasp_misalignment_D1_01d57e60a7ce
+stack_three	v5_grasp_wrong_pose_D0_0007c7f6330e
+stack_three	v5_grasp_wrong_pose_D0_001ddce342c7
+stack_three	v5_grasp_wrong_pose_D0_0023b4be5784
+stack_three	v5_grasp_wrong_pose_D0_003cadbba84c
+stack_three	v5_grasp_wrong_pose_D0_0040c60c5331
+stack_three	v5_grasp_wrong_pose_D0_007c9d6c269c
+stack_three	v5_grasp_wrong_pose_D0_0081620f6e9f
+stack_three	v5_grasp_wrong_pose_D0_013cb29eac0f
+stack_three	v5_grasp_wrong_pose_D0_016238614756
+stack_three	v5_grasp_wrong_pose_D0_017942dc97ce
+stack_three	v5_grasp_wrong_pose_D1_00080eea625d
+stack_three	v5_grasp_wrong_pose_D1_00485887f754
+stack_three	v5_grasp_wrong_pose_D1_0096336e5c28
+stack_three	v5_grasp_wrong_pose_D1_01515bc25670
+stack_three	v5_grasp_wrong_pose_D1_01b3ca2a31cc
+stack_three	v5_grasp_wrong_pose_D1_025de73b5270
+stack_three	v5_grasp_wrong_pose_D1_029dfd57decb
+stack_three	v5_grasp_wrong_pose_D1_02e78f581b94
+stack_three	v5_grasp_wrong_pose_D1_03038819342a
+stack_three	v5_grasp_wrong_pose_D1_033339d8f156
+stack_three	v5_position_error_D0_001d20af7947
+stack_three	v5_position_error_D0_00848cd089b3
+stack_three	v5_position_error_D0_00a3ed98469e
+stack_three	v5_position_error_D0_00e705c1ce5d
+stack_three	v5_position_error_D0_01040328c904
+stack_three	v5_position_error_D0_0112a9d5e505
+stack_three	v5_position_error_D0_016b2cc1b37f
+stack_three	v5_position_error_D0_01786054bed7
+stack_three	v5_position_error_D0_01ae67e4d116
+stack_three	v5_position_error_D0_01d5b36edb82
+stack_three	v5_position_error_D1_0036b0137b1c
+stack_three	v5_position_error_D1_00481d8b2c3b
+stack_three	v5_position_error_D1_00e462605671
+stack_three	v5_position_error_D1_017cc0803155
+stack_three	v5_position_error_D1_019e523ab0a4
+stack_three	v5_position_error_D1_023e48d85839
+stack_three	v5_position_error_D1_026ede1daeb8
+stack_three	v5_position_error_D1_0282ecb48939
+stack_three	v5_position_error_D1_02e0cdd7e19a
+stack_three	v5_position_error_D1_03581bf695b2
+stack_three	v5_stuck_no_progress_D0_001d0b198165
+stack_three	v5_stuck_no_progress_D0_00681a235753
+stack_three	v5_stuck_no_progress_D0_00f2b0f24922
+stack_three	v5_stuck_no_progress_D0_00f9d4c0e1d2
+stack_three	v5_stuck_no_progress_D0_01194fb2406f
+stack_three	v5_stuck_no_progress_D0_0162f731ad12
+stack_three	v5_stuck_no_progress_D0_0193267af324
+stack_three	v5_stuck_no_progress_D0_01f8f6f4e158
+stack_three	v5_stuck_no_progress_D0_01ff4e63859d
+stack_three	v5_stuck_no_progress_D0_0227cdf348e5
+stack_three	v5_stuck_no_progress_D1_0026c02187e2
+stack_three	v5_stuck_no_progress_D1_006244a55b9f
+stack_three	v5_stuck_no_progress_D1_01bd87c0690e
+stack_three	v5_stuck_no_progress_D1_01edbd8c6138
+stack_three	v5_stuck_no_progress_D1_0279c00d88e0
+stack_three	v5_stuck_no_progress_D1_029f06dde4ee
+stack_three	v5_stuck_no_progress_D1_0336c257ca03
+stack_three	v5_stuck_no_progress_D1_040b17127a91
+stack_three	v5_stuck_no_progress_D1_0500fb586ec0
+stack_three	v5_stuck_no_progress_D1_055e43e2f0d8
+stack_three	v5_trajectory_regression_D0_0043688bb762
+stack_three	v5_trajectory_regression_D0_0066ddb90c6d
+stack_three	v5_trajectory_regression_D0_00bf16497bf4
+stack_three	v5_trajectory_regression_D0_00bf6e9234c8
+stack_three	v5_trajectory_regression_D0_011364a2686d
+stack_three	v5_trajectory_regression_D0_011c65f6f6ab
+stack_three	v5_trajectory_regression_D0_013a9fb7b2a1
+stack_three	v5_trajectory_regression_D0_0181158e49e7
+stack_three	v5_trajectory_regression_D0_01a005afb68b
+stack_three	v5_trajectory_regression_D0_01a11b037f3b
+stack_three	v5_trajectory_regression_D1_00246d27aee1
+stack_three	v5_trajectory_regression_D1_006d4521a718
+stack_three	v5_trajectory_regression_D1_00a86c2404b3
+stack_three	v5_trajectory_regression_D1_00d128feca68
+stack_three	v5_trajectory_regression_D1_00d5449c1ae6
+stack_three	v5_trajectory_regression_D1_0154dd1ab638
+stack_three	v5_trajectory_regression_D1_019fd765d002
+stack_three	v5_trajectory_regression_D1_021180ee9e5b
+stack_three	v5_trajectory_regression_D1_02a666f54a9e
+stack_three	v5_trajectory_regression_D1_036ff6b4fa15
+stack_three	v5_wrong_object_D0_000f623143a5
+stack_three	v5_wrong_object_D0_0051122e7934
+stack_three	v5_wrong_object_D0_005c5b898aa9
+stack_three	v5_wrong_object_D0_005d95c0f3fb
+stack_three	v5_wrong_object_D0_00628fd19c7c
+stack_three	v5_wrong_object_D0_00eff5a467a4
+stack_three	v5_wrong_object_D0_014db42ea8e4
+stack_three	v5_wrong_object_D0_0155129fbe8d
+stack_three	v5_wrong_object_D0_0159b64b9c7f
+stack_three	v5_wrong_object_D0_01afd524e350
+stack_three	v5_wrong_object_D1_0040fbcab52e
+stack_three	v5_wrong_object_D1_0063ec3be857
+stack_three	v5_wrong_object_D1_00a8f9317953
+stack_three	v5_wrong_object_D1_00f051fdaed2
+stack_three	v5_wrong_object_D1_01ab5ea027ec
+stack_three	v5_wrong_object_D1_02014b622586
+stack_three	v5_wrong_object_D1_023e696f8a0f
+stack_three	v5_wrong_object_D1_026fe566e141
+stack_three	v5_wrong_object_D1_027c764ba466
+stack_three	v5_wrong_object_D1_02dcd7404559
+threading	v5_collision_eef_object_D0_00467da6bbf8
+threading	v5_collision_eef_object_D0_0046b48dcf05
+threading	v5_collision_eef_object_D0_0066d181607c
+threading	v5_collision_eef_object_D0_00af3464459a
+threading	v5_collision_eef_object_D0_0107ff728437
+threading	v5_collision_eef_object_D0_011dcdbc0b4c
+threading	v5_collision_eef_object_D0_01609f6350ef
+threading	v5_collision_eef_object_D0_01b719bcd08c
+threading	v5_collision_eef_object_D0_03698d3fc502
+threading	v5_collision_eef_object_D0_03f139ee0142
+threading	v5_collision_eef_object_D1_0045d3bc2cab
+threading	v5_collision_eef_object_D1_006e19a9e400
+threading	v5_collision_eef_object_D1_00799179f40c
+threading	v5_collision_eef_object_D1_00caa2f1f213
+threading	v5_collision_eef_object_D1_00e47703f24e
+threading	v5_collision_eef_object_D1_011645b0bdc5
+threading	v5_collision_eef_object_D1_01577d6ad09f
+threading	v5_collision_eef_object_D1_02273352d34d
+threading	v5_collision_eef_object_D1_033526083a83
+threading	v5_collision_eef_object_D1_03564ebe8a8a
+threading	v5_collision_empty_D0_0082fdb0d9fe
+threading	v5_collision_empty_D0_013ec1038fce
+threading	v5_collision_empty_D0_020807a9e14e
+threading	v5_collision_empty_D0_036fa52d37f3
+threading	v5_collision_empty_D0_062b04ec64f9
+threading	v5_collision_empty_D0_0692777d2190
+threading	v5_collision_empty_D0_078783b31a14
+threading	v5_collision_empty_D0_07c23a904f02
+threading	v5_collision_empty_D0_088f3588500f
+threading	v5_collision_empty_D0_0895658989a0
+threading	v5_collision_empty_D1_01678df2affc
+threading	v5_collision_empty_D1_01d72bb067b2
+threading	v5_collision_empty_D1_020d45b9f377
+threading	v5_collision_empty_D1_026884958c9a
+threading	v5_collision_empty_D1_028f5f8eba4c
+threading	v5_collision_empty_D1_02bf79cc25a2
+threading	v5_collision_empty_D1_02e9ee24700d
+threading	v5_collision_empty_D1_02f38d2e066c
+threading	v5_collision_empty_D1_02fd9b49f615
+threading	v5_collision_empty_D1_0325ed30ce87
+threading	v5_collision_holding_D0_0057bbd4bcf5
+threading	v5_collision_holding_D0_006c8d676fc5
+threading	v5_collision_holding_D0_013cfe9dd4c2
+threading	v5_collision_holding_D0_01e215028c9e
+threading	v5_collision_holding_D0_02329e38d100
+threading	v5_collision_holding_D0_0263a3d6a1d0
+threading	v5_collision_holding_D0_0365eb66367b
+threading	v5_collision_holding_D0_03d888588aa6
+threading	v5_collision_holding_D0_03e7f15694c4
+threading	v5_collision_holding_D0_04690ebefbf9
+threading	v5_collision_holding_D1_001f7282a51e
+threading	v5_collision_holding_D1_002604e8d4cf
+threading	v5_collision_holding_D1_004e90b6d993
+threading	v5_collision_holding_D1_0052eaa8ede5
+threading	v5_collision_holding_D1_006432b6fbba
+threading	v5_collision_holding_D1_008081c891be
+threading	v5_collision_holding_D1_02009bb6db04
+threading	v5_collision_holding_D1_020cded47a9f
+threading	v5_collision_holding_D1_026818f9a64a
+threading	v5_collision_holding_D1_02ad543b7a32
+threading	v5_collision_self_D0_0022b7505490
+threading	v5_collision_self_D0_007321c8e5be
+threading	v5_collision_self_D0_009c259a277c
+threading	v5_collision_self_D0_010448f92786
+threading	v5_collision_self_D0_013b3412420b
+threading	v5_collision_self_D0_014cab69f6cf
+threading	v5_collision_self_D0_016c8a720ac8
+threading	v5_collision_self_D0_0179e267f5b4
+threading	v5_collision_self_D0_01f473768ad1
+threading	v5_collision_self_D0_020fe3550218
+threading	v5_collision_self_D1_0023d58ce354
+threading	v5_collision_self_D1_007293a69345
+threading	v5_collision_self_D1_00782ba055e9
+threading	v5_collision_self_D1_007e829c076f
+threading	v5_collision_self_D1_00d0c535f021
+threading	v5_collision_self_D1_00d86974d51d
+threading	v5_collision_self_D1_01134db59743
+threading	v5_collision_self_D1_012a2e414ba2
+threading	v5_collision_self_D1_01346a844ba8
+threading	v5_collision_self_D1_01ac94237244
+threading	v5_drop_in_transit_D0_001ed3338397
+threading	v5_drop_in_transit_D0_003727409acf
+threading	v5_drop_in_transit_D0_004660400883
+threading	v5_drop_in_transit_D0_005dca138a8f
+threading	v5_drop_in_transit_D0_0079d0fc5a57
+threading	v5_drop_in_transit_D0_00899e258715
+threading	v5_drop_in_transit_D0_00a7b09ebe3e
+threading	v5_drop_in_transit_D0_00d3972446f8
+threading	v5_drop_in_transit_D0_016253ac2446
+threading	v5_drop_in_transit_D0_01a60d9de0a1
+threading	v5_drop_in_transit_D1_0014ce70deba
+threading	v5_drop_in_transit_D1_003500a88878
+threading	v5_drop_in_transit_D1_004c39d2a17f
+threading	v5_drop_in_transit_D1_00c13bd2ca54
+threading	v5_drop_in_transit_D1_013ba9dd06df
+threading	v5_drop_in_transit_D1_01c737388f4a
+threading	v5_drop_in_transit_D1_01d751d4b7ea
+threading	v5_drop_in_transit_D1_01f5252c5a58
+threading	v5_drop_in_transit_D1_020a65c9c990
+threading	v5_drop_in_transit_D1_0229879c05b9
+threading	v5_grasp_misalignment_D0_000c0df200fc
+threading	v5_grasp_misalignment_D0_003bcef602b1
+threading	v5_grasp_misalignment_D0_012644bca198
+threading	v5_grasp_misalignment_D0_0152c668ce23
+threading	v5_grasp_misalignment_D0_02126b69c4a9
+threading	v5_grasp_misalignment_D0_0216aadd0d90
+threading	v5_grasp_misalignment_D0_0250821c6b75
+threading	v5_grasp_misalignment_D0_026a18241f55
+threading	v5_grasp_misalignment_D0_027f2415f277
+threading	v5_grasp_misalignment_D0_02a53bfcd055
+threading	v5_grasp_misalignment_D1_004dc02ebffb
+threading	v5_grasp_misalignment_D1_006fec27d882
+threading	v5_grasp_misalignment_D1_00ea53b5d0c6
+threading	v5_grasp_misalignment_D1_00f77d05f887
+threading	v5_grasp_misalignment_D1_00ffe4010064
+threading	v5_grasp_misalignment_D1_01c2bc1040bb
+threading	v5_grasp_misalignment_D1_0258646a9a4b
+threading	v5_grasp_misalignment_D1_025bf21cc5b4
+threading	v5_grasp_misalignment_D1_02d2b30ff3cf
+threading	v5_grasp_misalignment_D1_0310850f34b3
+threading	v5_grasp_wrong_pose_D0_008c48e6556f
+threading	v5_grasp_wrong_pose_D0_009dd6323f82
+threading	v5_grasp_wrong_pose_D0_00de93d9ec02
+threading	v5_grasp_wrong_pose_D0_01f9836789d8
+threading	v5_grasp_wrong_pose_D0_02258371d5f4
+threading	v5_grasp_wrong_pose_D0_025bb7162e46
+threading	v5_grasp_wrong_pose_D0_028a8ab2fcc0
+threading	v5_grasp_wrong_pose_D0_02a937518eec
+threading	v5_grasp_wrong_pose_D0_03b6b553df05
+threading	v5_grasp_wrong_pose_D0_03ba8f18cb8f
+threading	v5_grasp_wrong_pose_D1_003287c3d476
+threading	v5_grasp_wrong_pose_D1_003879b8899b
+threading	v5_grasp_wrong_pose_D1_00bc6a23eb6c
+threading	v5_grasp_wrong_pose_D1_00f2aa87b73f
+threading	v5_grasp_wrong_pose_D1_011af83bd08e
+threading	v5_grasp_wrong_pose_D1_014d68f1e81a
+threading	v5_grasp_wrong_pose_D1_01c698ea5db4
+threading	v5_grasp_wrong_pose_D1_0202d91a5c21
+threading	v5_grasp_wrong_pose_D1_022b3c200f9f
+threading	v5_grasp_wrong_pose_D1_022fd0f74039
+threading	v5_position_error_D0_001fb8dd0e2a
+threading	v5_position_error_D0_0071529c5c2e
+threading	v5_position_error_D0_0077a35a11ba
+threading	v5_position_error_D0_00db626dffa4
+threading	v5_position_error_D0_00e6cf931749
+threading	v5_position_error_D0_015493d51d3c
+threading	v5_position_error_D0_01870a6e4b38
+threading	v5_position_error_D0_019307bab095
+threading	v5_position_error_D0_01a00a670152
+threading	v5_position_error_D0_01aa4455acd7
+threading	v5_position_error_D1_000a8b00d647
+threading	v5_position_error_D1_004df1be4264
+threading	v5_position_error_D1_0095f820fb96
+threading	v5_position_error_D1_00cb713193bb
+threading	v5_position_error_D1_0105b0e28592
+threading	v5_position_error_D1_0114b4a362c4
+threading	v5_position_error_D1_016422e56bcf
+threading	v5_position_error_D1_01b9eeb8101d
+threading	v5_position_error_D1_026469196c21
+threading	v5_position_error_D1_026cc8774e73
+threading	v5_stuck_no_progress_D0_000fee88c59c
+threading	v5_stuck_no_progress_D0_0016ad211491
+threading	v5_stuck_no_progress_D0_0038cf653867
+threading	v5_stuck_no_progress_D0_0062f9e90a5c
+threading	v5_stuck_no_progress_D0_00693cffcbe6
+threading	v5_stuck_no_progress_D0_007ea9dff4d5
+threading	v5_stuck_no_progress_D0_009e8340d055
+threading	v5_stuck_no_progress_D0_00dc79c64161
+threading	v5_stuck_no_progress_D0_01bcf1f62d26
+threading	v5_stuck_no_progress_D0_01e67272b9f6
+threading	v5_stuck_no_progress_D1_001bd70540b0
+threading	v5_stuck_no_progress_D1_0031feac5daf
+threading	v5_stuck_no_progress_D1_0032bcc581df
+threading	v5_stuck_no_progress_D1_004504bcdac3
+threading	v5_stuck_no_progress_D1_008f5e08c6db
+threading	v5_stuck_no_progress_D1_00b54d8112de
+threading	v5_stuck_no_progress_D1_00cb85ec0187
+threading	v5_stuck_no_progress_D1_00d447844346
+threading	v5_stuck_no_progress_D1_00f2ef4e0b8b
+threading	v5_stuck_no_progress_D1_011f50e4b4a4
+threading	v5_trajectory_regression_D0_0092118ddf84
+threading	v5_trajectory_regression_D0_009622b681d5
+threading	v5_trajectory_regression_D0_01275d388581
+threading	v5_trajectory_regression_D0_0129254c7beb
+threading	v5_trajectory_regression_D0_017a718d7066
+threading	v5_trajectory_regression_D0_01a28049136b
+threading	v5_trajectory_regression_D0_01af7077b559
+threading	v5_trajectory_regression_D0_023000ac7beb
+threading	v5_trajectory_regression_D0_02c754e9fa22
+threading	v5_trajectory_regression_D0_02cf76743e05
+threading	v5_trajectory_regression_D1_0034d2b5db59
+threading	v5_trajectory_regression_D1_0053f5482d6b
+threading	v5_trajectory_regression_D1_006ec614381a
+threading	v5_trajectory_regression_D1_00827ca50e5f
+threading	v5_trajectory_regression_D1_00a34f6447f0
+threading	v5_trajectory_regression_D1_00b90d99017e
+threading	v5_trajectory_regression_D1_00e4628a2717
+threading	v5_trajectory_regression_D1_00ff9a8ad01e
+threading	v5_trajectory_regression_D1_0174646323c6
+threading	v5_trajectory_regression_D1_019da279a256
+three_piece_assembly	v5_collision_eef_object_D0_0091ec66cd80
+three_piece_assembly	v5_collision_eef_object_D0_00ee1323f0b8
+three_piece_assembly	v5_collision_eef_object_D0_0124fbb288b5
+three_piece_assembly	v5_collision_eef_object_D0_01651ad27719
+three_piece_assembly	v5_collision_eef_object_D0_016628e5fb5c
+three_piece_assembly	v5_collision_eef_object_D0_01e550b4c308
+three_piece_assembly	v5_collision_eef_object_D0_01ead119d8e0
+three_piece_assembly	v5_collision_eef_object_D0_0210db73eb10
+three_piece_assembly	v5_collision_eef_object_D0_02142fda33c5
+three_piece_assembly	v5_collision_eef_object_D0_0221b5b3fe90
+three_piece_assembly	v5_collision_eef_object_D1_0098f469ad26
+three_piece_assembly	v5_collision_eef_object_D1_00c196cb5e9a
+three_piece_assembly	v5_collision_eef_object_D1_00e55a432ef4
+three_piece_assembly	v5_collision_eef_object_D1_00f470d0d862
+three_piece_assembly	v5_collision_eef_object_D1_013208410ff8
+three_piece_assembly	v5_collision_eef_object_D1_01361c42f706
+three_piece_assembly	v5_collision_eef_object_D1_01770cf6f803
+three_piece_assembly	v5_collision_eef_object_D1_017fc57e9ae1
+three_piece_assembly	v5_collision_eef_object_D1_019fde9d6c7e
+three_piece_assembly	v5_collision_eef_object_D1_020d1af245be
+three_piece_assembly	v5_collision_empty_D0_003e7ffb4ecd
+three_piece_assembly	v5_collision_empty_D0_00a55531ac78
+three_piece_assembly	v5_collision_empty_D0_012c7d7418b2
+three_piece_assembly	v5_collision_empty_D0_017c1ea3cd8b
+three_piece_assembly	v5_collision_empty_D0_020b10fd6216
+three_piece_assembly	v5_collision_empty_D0_026575024c91
+three_piece_assembly	v5_collision_empty_D0_027236a2d3cf
+three_piece_assembly	v5_collision_empty_D0_02a4bec0f8b9
+three_piece_assembly	v5_collision_empty_D0_0348763a74e4
+three_piece_assembly	v5_collision_empty_D0_04673491f329
+three_piece_assembly	v5_collision_empty_D1_0032529dc7e8
+three_piece_assembly	v5_collision_empty_D1_0033f74a34d6
+three_piece_assembly	v5_collision_empty_D1_0046cf5af2d4
+three_piece_assembly	v5_collision_empty_D1_00d36ebd6643
+three_piece_assembly	v5_collision_empty_D1_00d64dd67928
+three_piece_assembly	v5_collision_empty_D1_00d8cf18627f
+three_piece_assembly	v5_collision_empty_D1_0169c30d014b
+three_piece_assembly	v5_collision_empty_D1_01dca4af2cf6
+three_piece_assembly	v5_collision_empty_D1_0217b5394ef2
+three_piece_assembly	v5_collision_empty_D1_021f52596dca
+three_piece_assembly	v5_collision_holding_D0_002a940e55e7
+three_piece_assembly	v5_collision_holding_D0_004a41a96d70
+three_piece_assembly	v5_collision_holding_D0_007682f60799
+three_piece_assembly	v5_collision_holding_D0_00d4e5a305be
+three_piece_assembly	v5_collision_holding_D0_0139adaa29fb
+three_piece_assembly	v5_collision_holding_D0_01a49e84d649
+three_piece_assembly	v5_collision_holding_D0_01ba062adf11
+three_piece_assembly	v5_collision_holding_D0_01f1b896736d
+three_piece_assembly	v5_collision_holding_D0_020cfa82de6a
+three_piece_assembly	v5_collision_holding_D0_032538b84788
+three_piece_assembly	v5_collision_holding_D1_0036677b156e
+three_piece_assembly	v5_collision_holding_D1_0117cd68f285
+three_piece_assembly	v5_collision_holding_D1_01412ce679dd
+three_piece_assembly	v5_collision_holding_D1_01a1eb517d43
+three_piece_assembly	v5_collision_holding_D1_01b579017b91
+three_piece_assembly	v5_collision_holding_D1_020c77b81685
+three_piece_assembly	v5_collision_holding_D1_0269e8effea8
+three_piece_assembly	v5_collision_holding_D1_028c3f0d8c60
+three_piece_assembly	v5_collision_holding_D1_02a2d3bdefc9
+three_piece_assembly	v5_collision_holding_D1_02c73e42dda3
+three_piece_assembly	v5_collision_self_D0_0151c38b3615
+three_piece_assembly	v5_collision_self_D0_01629dd3c919
+three_piece_assembly	v5_collision_self_D0_01ba5f9f585b
+three_piece_assembly	v5_collision_self_D0_01d40734ddc0
+three_piece_assembly	v5_collision_self_D0_01f5ce049249
+three_piece_assembly	v5_collision_self_D0_0244f7c6c062
+three_piece_assembly	v5_collision_self_D0_02a6502b74ff
+three_piece_assembly	v5_collision_self_D0_02b551eece07
+three_piece_assembly	v5_collision_self_D0_02c0abcc2b1f
+three_piece_assembly	v5_collision_self_D0_02c1c55c997b
+three_piece_assembly	v5_collision_self_D1_00166f92069d
+three_piece_assembly	v5_collision_self_D1_0083a4d715e7
+three_piece_assembly	v5_collision_self_D1_012421a4ba9b
+three_piece_assembly	v5_collision_self_D1_01b007d3bbe6
+three_piece_assembly	v5_collision_self_D1_021e31ce7223
+three_piece_assembly	v5_collision_self_D1_0285e693965a
+three_piece_assembly	v5_collision_self_D1_02a7227317d1
+three_piece_assembly	v5_collision_self_D1_02d6af01bf18
+three_piece_assembly	v5_collision_self_D1_031ed3c1ce16
+three_piece_assembly	v5_collision_self_D1_03531590f43f
+three_piece_assembly	v5_drop_at_wrong_place_D0_0003af35db7d
+three_piece_assembly	v5_drop_at_wrong_place_D0_0037583130fd
+three_piece_assembly	v5_drop_at_wrong_place_D0_007a44067454
+three_piece_assembly	v5_drop_at_wrong_place_D0_00983424e965
+three_piece_assembly	v5_drop_at_wrong_place_D0_00a36c0ea8e1
+three_piece_assembly	v5_drop_at_wrong_place_D0_00af39052a62
+three_piece_assembly	v5_drop_at_wrong_place_D0_0140f4d1ecaa
+three_piece_assembly	v5_drop_at_wrong_place_D0_0173b8914485
+three_piece_assembly	v5_drop_at_wrong_place_D0_0184b5c3bb70
+three_piece_assembly	v5_drop_at_wrong_place_D0_021c9ad3d29d
+three_piece_assembly	v5_drop_at_wrong_place_D1_000f24a6cd8a
+three_piece_assembly	v5_drop_at_wrong_place_D1_00363ba6b4e1
+three_piece_assembly	v5_drop_at_wrong_place_D1_008af9fde70c
+three_piece_assembly	v5_drop_at_wrong_place_D1_00b922261401
+three_piece_assembly	v5_drop_at_wrong_place_D1_00ed79676f49
+three_piece_assembly	v5_drop_at_wrong_place_D1_00fcaa040aed
+three_piece_assembly	v5_drop_at_wrong_place_D1_012162661740
+three_piece_assembly	v5_drop_at_wrong_place_D1_01823fc2d1ba
+three_piece_assembly	v5_drop_at_wrong_place_D1_019437b44e2d
+three_piece_assembly	v5_drop_at_wrong_place_D1_01b4835594af
+three_piece_assembly	v5_drop_in_transit_D0_004b7d965317
+three_piece_assembly	v5_drop_in_transit_D0_00f655c24105
+three_piece_assembly	v5_drop_in_transit_D0_010b35fb6d67
+three_piece_assembly	v5_drop_in_transit_D0_01339509a989
+three_piece_assembly	v5_drop_in_transit_D0_01b9ec2cc8a1
+three_piece_assembly	v5_drop_in_transit_D0_01e3eeca6a59
+three_piece_assembly	v5_drop_in_transit_D0_01f3d991693e
+three_piece_assembly	v5_drop_in_transit_D0_025fd5c4ba2b
+three_piece_assembly	v5_drop_in_transit_D0_0270e8125a39
+three_piece_assembly	v5_drop_in_transit_D0_02ba476526c4
+three_piece_assembly	v5_drop_in_transit_D1_004fd9c17020
+three_piece_assembly	v5_drop_in_transit_D1_005bc2c6c2d4
+three_piece_assembly	v5_drop_in_transit_D1_00a97f7685f7
+three_piece_assembly	v5_drop_in_transit_D1_00aacb61deb7
+three_piece_assembly	v5_drop_in_transit_D1_016f189996c8
+three_piece_assembly	v5_drop_in_transit_D1_021a34704100
+three_piece_assembly	v5_drop_in_transit_D1_0249362c5f04
+three_piece_assembly	v5_drop_in_transit_D1_028bf271a8be
+three_piece_assembly	v5_drop_in_transit_D1_03b0af8ee8e0
+three_piece_assembly	v5_drop_in_transit_D1_03b4416b1870
+three_piece_assembly	v5_grasp_misalignment_D0_0037c0165ba1
+three_piece_assembly	v5_grasp_misalignment_D0_00384681470b
+three_piece_assembly	v5_grasp_misalignment_D0_00b53574dc07
+three_piece_assembly	v5_grasp_misalignment_D0_00c2e504dc51
+three_piece_assembly	v5_grasp_misalignment_D0_00cc03dcfc09
+three_piece_assembly	v5_grasp_misalignment_D0_01113bdd176e
+three_piece_assembly	v5_grasp_misalignment_D0_026f3c75ba1e
+three_piece_assembly	v5_grasp_misalignment_D0_02a7238a8055
+three_piece_assembly	v5_grasp_misalignment_D0_02b09a005f3d
+three_piece_assembly	v5_grasp_misalignment_D0_02bc9704c53e
+three_piece_assembly	v5_grasp_misalignment_D1_0011993cb8b3
+three_piece_assembly	v5_grasp_misalignment_D1_00120576b4c0
+three_piece_assembly	v5_grasp_misalignment_D1_00159a74d8e7
+three_piece_assembly	v5_grasp_misalignment_D1_00df8f2c32eb
+three_piece_assembly	v5_grasp_misalignment_D1_01ae1ffafab8
+three_piece_assembly	v5_grasp_misalignment_D1_01c2971607d6
+three_piece_assembly	v5_grasp_misalignment_D1_01f39444ca88
+three_piece_assembly	v5_grasp_misalignment_D1_02893caeb96f
+three_piece_assembly	v5_grasp_misalignment_D1_0295423d3ba7
+three_piece_assembly	v5_grasp_misalignment_D1_02d3763689ae
+three_piece_assembly	v5_grasp_wrong_pose_D0_002ab220ee34
+three_piece_assembly	v5_grasp_wrong_pose_D0_002f4b1bfeb0
+three_piece_assembly	v5_grasp_wrong_pose_D0_00a06fa0ff92
+three_piece_assembly	v5_grasp_wrong_pose_D0_00ea6e34b34d
+three_piece_assembly	v5_grasp_wrong_pose_D0_01a404ba9929
+three_piece_assembly	v5_grasp_wrong_pose_D0_01b36c90875e
+three_piece_assembly	v5_grasp_wrong_pose_D0_01c090dfe764
+three_piece_assembly	v5_grasp_wrong_pose_D0_023e200496c5
+three_piece_assembly	v5_grasp_wrong_pose_D0_02839fd0454f
+three_piece_assembly	v5_grasp_wrong_pose_D0_02b4f2c0bd74
+three_piece_assembly	v5_grasp_wrong_pose_D1_00baafce9185
+three_piece_assembly	v5_grasp_wrong_pose_D1_00c57aaf5c3b
+three_piece_assembly	v5_grasp_wrong_pose_D1_010a6e42eb40
+three_piece_assembly	v5_grasp_wrong_pose_D1_011b384d3070
+three_piece_assembly	v5_grasp_wrong_pose_D1_011cf95d9cbe
+three_piece_assembly	v5_grasp_wrong_pose_D1_011d318ff04d
+three_piece_assembly	v5_grasp_wrong_pose_D1_019056f4b7f7
+three_piece_assembly	v5_grasp_wrong_pose_D1_01a4783fc629
+three_piece_assembly	v5_grasp_wrong_pose_D1_01c7c63745fa
+three_piece_assembly	v5_grasp_wrong_pose_D1_01fd540f21a2
+three_piece_assembly	v5_position_error_D0_009fb284f360
+three_piece_assembly	v5_position_error_D0_00be29fb0b1f
+three_piece_assembly	v5_position_error_D0_011a3e1446dd
+three_piece_assembly	v5_position_error_D0_0136f43ad45f
+three_piece_assembly	v5_position_error_D0_013cd6c3d8a3
+three_piece_assembly	v5_position_error_D0_01a0ffadfce8
+three_piece_assembly	v5_position_error_D0_01b0304b8362
+three_piece_assembly	v5_position_error_D0_01ca9a71bf1b
+three_piece_assembly	v5_position_error_D0_01ea410c257f
+three_piece_assembly	v5_position_error_D0_023320b87550
+three_piece_assembly	v5_position_error_D1_001c4e8893a8
+three_piece_assembly	v5_position_error_D1_0024cd1dcf95
+three_piece_assembly	v5_position_error_D1_0090b2620f58
+three_piece_assembly	v5_position_error_D1_00e46689893c
+three_piece_assembly	v5_position_error_D1_011d3d185bb9
+three_piece_assembly	v5_position_error_D1_0263c6b841dd
+three_piece_assembly	v5_position_error_D1_026e355c7bb6
+three_piece_assembly	v5_position_error_D1_02f9b1914ef6
+three_piece_assembly	v5_position_error_D1_033749d1fb26
+three_piece_assembly	v5_position_error_D1_034e29a9b983
+three_piece_assembly	v5_stuck_no_progress_D0_000bc1a96fd7
+three_piece_assembly	v5_stuck_no_progress_D0_009a3339a748
+three_piece_assembly	v5_stuck_no_progress_D0_0115e0b518c4
+three_piece_assembly	v5_stuck_no_progress_D0_018709923a1b
+three_piece_assembly	v5_stuck_no_progress_D0_018a66d18458
+three_piece_assembly	v5_stuck_no_progress_D0_0193875f4107
+three_piece_assembly	v5_stuck_no_progress_D0_01a736167dec
+three_piece_assembly	v5_stuck_no_progress_D0_01b427ac9965
+three_piece_assembly	v5_stuck_no_progress_D0_01c326f00770
+three_piece_assembly	v5_stuck_no_progress_D0_021449b466cf
+three_piece_assembly	v5_stuck_no_progress_D1_0032c378f7f5
+three_piece_assembly	v5_stuck_no_progress_D1_008a14ffd897
+three_piece_assembly	v5_stuck_no_progress_D1_00bf8346181e
+three_piece_assembly	v5_stuck_no_progress_D1_00c9d8c2e028
+three_piece_assembly	v5_stuck_no_progress_D1_012bbeb42688
+three_piece_assembly	v5_stuck_no_progress_D1_012dd2776a53
+three_piece_assembly	v5_stuck_no_progress_D1_0161a79942e2
+three_piece_assembly	v5_stuck_no_progress_D1_01a9a4c97c19
+three_piece_assembly	v5_stuck_no_progress_D1_01f1de7bd25c
+three_piece_assembly	v5_stuck_no_progress_D1_0237677dfaa5
+three_piece_assembly	v5_trajectory_regression_D0_0097572eb721
+three_piece_assembly	v5_trajectory_regression_D0_00a251df5429
+three_piece_assembly	v5_trajectory_regression_D0_00fb545aa591
+three_piece_assembly	v5_trajectory_regression_D0_01508a58ab87
+three_piece_assembly	v5_trajectory_regression_D0_0171b383cf04
+three_piece_assembly	v5_trajectory_regression_D0_02103e620ba1
+three_piece_assembly	v5_trajectory_regression_D0_0264e2728286
+three_piece_assembly	v5_trajectory_regression_D0_02e759692651
+three_piece_assembly	v5_trajectory_regression_D0_0359461a1765
+three_piece_assembly	v5_trajectory_regression_D0_036ead946158
+three_piece_assembly	v5_trajectory_regression_D1_00138b8c08bf
+three_piece_assembly	v5_trajectory_regression_D1_001a67b75c02
+three_piece_assembly	v5_trajectory_regression_D1_010ac67f8393
+three_piece_assembly	v5_trajectory_regression_D1_0153c5138ec3
+three_piece_assembly	v5_trajectory_regression_D1_017bf7c3065b
+three_piece_assembly	v5_trajectory_regression_D1_019dc55ca47f
+three_piece_assembly	v5_trajectory_regression_D1_01a4b9e63ce4
+three_piece_assembly	v5_trajectory_regression_D1_01aa6329ceef
+three_piece_assembly	v5_trajectory_regression_D1_01b77c30c1d4
+three_piece_assembly	v5_trajectory_regression_D1_020791e2c7ba
+three_piece_assembly	v5_wrong_object_D0_004bdf4e5b92
+three_piece_assembly	v5_wrong_object_D0_00f0b1bbb447
+three_piece_assembly	v5_wrong_object_D0_014317ad4281
+three_piece_assembly	v5_wrong_object_D0_0154ee88d1b5
+three_piece_assembly	v5_wrong_object_D0_0171937b8746
+three_piece_assembly	v5_wrong_object_D0_01c350af7249
+three_piece_assembly	v5_wrong_object_D0_01ded3790688
+three_piece_assembly	v5_wrong_object_D0_01eb28451e04
+three_piece_assembly	v5_wrong_object_D0_022346ac47bc
+three_piece_assembly	v5_wrong_object_D0_0241c5281837
+three_piece_assembly	v5_wrong_object_D1_00066e129f6e
+three_piece_assembly	v5_wrong_object_D1_00580e0a76c6
+three_piece_assembly	v5_wrong_object_D1_00d9f4999911
+three_piece_assembly	v5_wrong_object_D1_00fd5de771df
+three_piece_assembly	v5_wrong_object_D1_0110ef38e323
+three_piece_assembly	v5_wrong_object_D1_0111c108d977
+three_piece_assembly	v5_wrong_object_D1_0143bc7d385e
+three_piece_assembly	v5_wrong_object_D1_0149f1c5c455
+three_piece_assembly	v5_wrong_object_D1_0292ed5c7f98
+three_piece_assembly	v5_wrong_object_D1_029c2dfc9b04

excluded_backfill_scene_ids.txt

@@ -0,0 +1,461 @@
+v5_train_backfill_collision_eef_object_D0_0010_0002d5b31c7e
+v5_train_backfill_collision_eef_object_D0_0010_00731d8acf70
+v5_train_backfill_collision_eef_object_D0_0011_006854cc5240
+v5_train_backfill_collision_eef_object_D0_0011_008d9efee56b
+v5_train_backfill_collision_eef_object_D0_0012_008aab5113eb
+v5_train_backfill_collision_eef_object_D0_0012_00e4ca5d8cc8
+v5_train_backfill_collision_eef_object_D0_0013_00ca1f92f1dc
+v5_train_backfill_collision_eef_object_D0_0013_01149c80a42c
+v5_train_backfill_collision_eef_object_D0_0014_013fcf3920af
+v5_train_backfill_collision_eef_object_D0_0014_0167632a38c3
+v5_train_backfill_collision_eef_object_D0_0015_019a373af87f
+v5_train_backfill_collision_eef_object_D0_0015_026d62a4146f
+v5_train_backfill_collision_eef_object_D0_0016_01fac118c8cf
+v5_train_backfill_collision_eef_object_D0_0016_026db4e618d7
+v5_train_backfill_collision_eef_object_D0_0017_021fe1953c91
+v5_train_backfill_collision_eef_object_D0_0017_028a8b42a0cc
+v5_train_backfill_collision_eef_object_D0_0018_028c8709c5dc
+v5_train_backfill_collision_eef_object_D0_0018_02e513d859ef
+v5_train_backfill_collision_eef_object_D0_0019_02d03115ce14
+v5_train_backfill_collision_eef_object_D0_0019_031b904c5e6c
+v5_train_backfill_collision_eef_object_D1_0010_00197f0c35e7
+v5_train_backfill_collision_eef_object_D1_0010_001c30e4658e
+v5_train_backfill_collision_eef_object_D1_0011_003f714cf7e9
+v5_train_backfill_collision_eef_object_D1_0011_007fec20c127
+v5_train_backfill_collision_eef_object_D1_0012_005fb2ae98ad
+v5_train_backfill_collision_eef_object_D1_0012_00bff2723749
+v5_train_backfill_collision_eef_object_D1_0013_0062a060fb44
+v5_train_backfill_collision_eef_object_D1_0013_00efba9f6ad3
+v5_train_backfill_collision_eef_object_D1_0014_008cb89f3d9e
+v5_train_backfill_collision_eef_object_D1_0014_024aa1d26995
+v5_train_backfill_collision_eef_object_D1_0015_008e8fc90016
+v5_train_backfill_collision_eef_object_D1_0015_029252d649d2
+v5_train_backfill_collision_eef_object_D1_0016_008f4b7d2470
+v5_train_backfill_collision_eef_object_D1_0016_0293c7e49247
+v5_train_backfill_collision_eef_object_D1_0017_01334dd7c8c7
+v5_train_backfill_collision_eef_object_D1_0017_02e09482618c
+v5_train_backfill_collision_eef_object_D1_0018_02602d2f843b
+v5_train_backfill_collision_eef_object_D1_0018_02fb27ed457b
+v5_train_backfill_collision_eef_object_D1_0019_030ed4648cb4
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+v5_train_backfill_collision_empty_D0_0011_00230e781dd9
+v5_train_backfill_collision_empty_D0_0011_004b618f5d96
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+v5_train_backfill_collision_empty_D0_0012_009f807dad1a
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+v5_train_backfill_collision_empty_D0_0013_00a49352a0e3
+v5_train_backfill_collision_empty_D0_0014_00884db3d78b
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+v5_train_backfill_collision_empty_D0_0019_0210109c3143
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+v5_train_backfill_collision_empty_D1_0010_0008ecdfd34b
+v5_train_backfill_collision_empty_D1_0010_0020cf1e67e3
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+v5_train_backfill_collision_empty_D1_0012_00dfee433cc3
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+v5_train_backfill_collision_empty_D1_0016_0116b1f16bf8
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+v5_train_backfill_collision_holding_D0_0019_025e12dd16ca
+v5_train_backfill_collision_holding_D0_0020_01e7c18bcf6a
+v5_train_backfill_collision_holding_D0_0021_0224772d7a75
+v5_train_backfill_collision_holding_D1_0010_0008c58e1225
+v5_train_backfill_collision_holding_D1_0010_006a748decf1
+v5_train_backfill_collision_holding_D1_0011_001939ede787
+v5_train_backfill_collision_holding_D1_0011_00871e22f017
+v5_train_backfill_collision_holding_D1_0012_0038bd8972d7
+v5_train_backfill_collision_holding_D1_0012_01779dd7ba4b
+v5_train_backfill_collision_holding_D1_0013_003ae95fde83
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+v5_train_backfill_collision_self_D0_0020_012f0b5ea46d
+v5_train_backfill_collision_self_D1_0010_00026f121706
+v5_train_backfill_collision_self_D1_0010_0054c6485644
+v5_train_backfill_collision_self_D1_0011_0056e4897a0a
+v5_train_backfill_collision_self_D1_0011_00647afa5fb3
+v5_train_backfill_collision_self_D1_0012_0078c5abbb3c
+v5_train_backfill_collision_self_D1_0012_0081f5615952
+v5_train_backfill_collision_self_D1_0013_007ae693966b
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+v5_train_backfill_collision_self_D1_0014_00a72c1e3ac8
+v5_train_backfill_collision_self_D1_0014_00e4c0027663
+v5_train_backfill_collision_self_D1_0015_00b90ffc5088
+v5_train_backfill_collision_self_D1_0015_016381d0cee5
+v5_train_backfill_collision_self_D1_0016_00e6e1955305
+v5_train_backfill_collision_self_D1_0016_023603afe8b8
+v5_train_backfill_collision_self_D1_0017_00eb83231f9a
+v5_train_backfill_collision_self_D1_0017_0239687a662e
+v5_train_backfill_collision_self_D1_0018_00eecf9bd475
+v5_train_backfill_collision_self_D1_0018_0287a8ae4e15
+v5_train_backfill_collision_self_D1_0019_00ef5512cc32
+v5_train_backfill_collision_self_D1_0019_03357c3623bf
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