pith. sign in

arxiv: 2606.18632 · v1 · pith:VFGPVZ5Nnew · submitted 2026-06-17 · 💻 cs.RO

ROBOSHACKLES: A Safety Dataset for Human-Injury Prevention in Embodied Foundation Models

Zhuowen Yin , Chongyang Liu , Wenzhang Yang , Renjue Li , Yinxing Xue This is my paper

Pith reviewed 2026-06-26 21:04 UTC · model grok-4.3

classification 💻 cs.RO
keywords embodied foundation modelssafety datasethuman injury preventionrobotic video synthesisrefusal learninghazard anticipationDROID datasetunsafe actions
0
0 comments X

The pith

Embodied foundation models produce unsafe actions in every tested human-injury scenario according to the ROBOSHACKLES dataset.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper constructs ROBOSHACKLES, a 10,000-clip dataset of robotic videos showing hazardous situations that could lead to human injury. The construction starts from real DROID observations and uses editing and synthesis to create scenarios without collecting dangerous real data. Tests on six models show they all generate unsafe actions, for a 100 percent rate. This matters because it offers a way to train and evaluate safety in robots that interact with people. The dataset covers direct and indirect harm types and is meant for refusal learning.

Core claim

The authors build a pipeline that edits real DROID scenes to introduce hazards, generates temporal prompts for scene evolution, and uses Wan2.7 to synthesize single-pass robotic rollouts. This produces ROBOSHACKLES spanning six harm categories. Evaluation under refusal criteria finds all models unsafe at 100 percent rate, positioning the dataset as a benchmark for safety alignment.

What carries the argument

The hazard-aware editing combined with Wan2.7 single-pass synthesis pipeline that generates realistic hazardous robotic videos from real observations.

If this is right

  • EFMs require improved refusal mechanisms to avoid unsafe actions.
  • The dataset enables training for hazard anticipation before action execution.
  • It provides a scalable alternative to real-world hazardous data collection.
  • Models can be benchmarked across direct and indirect harm scenarios.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Future work could test if fine-tuning on this data reduces the unsafe rate in real deployments.
  • Similar pipelines might apply to other safety domains like autonomous vehicles.
  • The 100 percent failure rate indicates a systemic issue in current EFM training approaches.

Load-bearing premise

The synthetic videos from the editing and synthesis pipeline match the distribution of real-world hazards that models would face.

What would settle it

A study comparing model actions in the synthetic videos versus the same scenarios executed by real robots in controlled settings, checking if unsafe rates match.

Figures

Figures reproduced from arXiv: 2606.18632 by ChongYang Liu, Renjue Li, Wenzhang Yang, Yinxing Xue, Zhuowen Yin.

Figure 1
Figure 1. Figure 1: Overview of the proposed safety dataset, evaluation, and training workflow for EFM safety alignment. The dataset [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Generation and evaluation pipeline of ROBOSHACKLES. Starting from real DROID observations, Qwen3-VL pro￾duces editing instructions, Qwen-Image constructs safety-critical initial frames, Qwen3-VL generates video prompts, and Wan2.7 synthesizes future rollouts. The resulting videos cover two direct-harm categories and four indirect-harm categories, and are evaluated with task-completion and visual-quality me… view at source ↗
read the original abstract

Embodied Foundation Models (EFMs) integrate multimodal understanding, future-state reasoning, and executable robot actions. Yet their safety alignment for human-injury prevention remains underexplored, primarily because real-world data of robots harming humans or creating hazardous household situations cannot be safely or ethically collected. To address this challenge, we propose a safety-critical data construction pipeline for human-injury prevention in EFMs.Starting from real DROID observations, our construction pipeline proceeds through scene understanding, hazard-aware image editing, temporal prompt generation, and single-pass rollout synthesis. The temporal prompts specify the expected scene evolution, while Wan2.7 synthesizes realistic robotic rollouts from the edited hazardous states in a single pass. Using this pipeline, we construct ROBOSHACKLES, a 10,000-clip robotic video dataset derived from real DROID observations, spanning two direct-harm and four indirect-harm categories. To ensure dataset quality, we assess task completion and visual quality with automatic metrics, and evaluate six representative EFMs under a refusal-based safety criterion. Results show that all evaluated models produce unsafe actions in the tested safety-critical scenarios, yielding a 100% unsafe action generation rate. ROBOSHACKLES serves as a scalable benchmark and training resource for refusal learning and hazard anticipation before robot action execution.The dataset is publicly available at https://huggingface.co/datasets/YZW00/RoboShackles.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper presents ROBOSHACKLES, a 10,000-clip synthetic robotic video dataset for safety evaluation of embodied foundation models (EFMs). Starting from real DROID observations, the authors apply a pipeline of scene understanding, hazard-aware image editing, temporal prompt generation, and single-pass synthesis via Wan2.7 to create clips in two direct-harm and four indirect-harm categories. They evaluate six representative EFMs under a refusal-based safety criterion, reporting a 100% unsafe action generation rate, and release the dataset publicly as a benchmark for refusal learning and hazard anticipation.

Significance. If the synthetic data faithfully represents real deployment hazards, the reported 100% unsafe rate would establish a clear and actionable safety gap in current EFMs, motivating work on alignment techniques. The public dataset release and focus on human-injury prevention scenarios are constructive contributions to the robotics safety literature.

major comments (2)
  1. [EFM evaluation and results] The manuscript provides no details on the evaluation protocol for the six EFMs: the abstract and results description omit the number of test cases per model, the precise definition of 'refusal' or 'unsafe action,' the prompting format used, and any statistical validation of the 100% rate. This information is load-bearing for the central empirical claim.
  2. [Dataset construction pipeline and quality assessment] The claim that ROBOSHACKLES clips represent safety-critical scenarios rests on unverified fidelity of the synthetic pipeline (DROID base → hazard-aware editing → Wan2.7 synthesis). Only automatic task-completion and visual-quality metrics are mentioned; no human realism ratings, distributional comparison to real injury-incident data, or ablation of editing artifacts are reported. This directly affects whether model failures on these clips establish a general safety gap.
minor comments (1)
  1. [Abstract] The abstract states that the dataset 'spans two direct-harm and four indirect-harm categories' but does not list the specific categories or their definitions, which would aid reproducibility.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive feedback on the evaluation protocol and dataset quality assessment. We will revise the manuscript to provide the requested details and strengthen the validation where possible.

read point-by-point responses

  1. Referee: The manuscript provides no details on the evaluation protocol for the six EFMs: the abstract and results description omit the number of test cases per model, the precise definition of 'refusal' or 'unsafe action,' the prompting format used, and any statistical validation of the 100% rate. This information is load-bearing for the central empirical claim.

    Authors: We agree these protocol details are necessary for interpreting the 100% unsafe rate. The revised manuscript will add a new subsection in the Experiments section specifying: the number of test cases per model and per category, the precise definition of refusal/unsafe action (e.g., any action that would lead to direct or indirect harm), the exact prompting format and templates used for each of the six EFMs, and statistical validation such as per-model counts and confidence bounds around the observed rate. revision: yes

  2. Referee: The claim that ROBOSHACKLES clips represent safety-critical scenarios rests on unverified fidelity of the synthetic pipeline (DROID base → hazard-aware editing → Wan2.7 synthesis). Only automatic task-completion and visual-quality metrics are mentioned; no human realism ratings, distributional comparison to real injury-incident data, or ablation of editing artifacts are reported. This directly affects whether model failures on these clips establish a general safety gap.

    Authors: We will expand the quality assessment section to include human realism ratings collected from annotators and ablations isolating the effects of hazard-aware editing artifacts. A distributional comparison to real injury-incident data cannot be performed, as noted in the paper introduction, because such data cannot be ethically collected; we will explicitly state this limitation and instead highlight the pipeline's grounding in real DROID observations as the primary fidelity anchor. revision: partial

standing simulated objections not resolved
  • Distributional comparison to real injury-incident data, which cannot be ethically collected.

Circularity Check

0 steps flagged

No circularity: empirical dataset construction and model evaluation with no derivations or self-referential predictions.

full rationale

The paper presents a pipeline for generating synthetic safety-critical video clips from real DROID observations (scene understanding, hazard-aware editing, temporal prompts, Wan2.7 synthesis) followed by direct empirical evaluation of six EFMs under a refusal criterion. No equations, fitted parameters, uniqueness theorems, or predictions are claimed. The 100% unsafe rate is a direct count on the constructed test set, not a derived result that reduces to inputs by construction. No self-citations are load-bearing for any central claim. This is a standard data+eval paper with independent content.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central contribution rests on the assumption that the synthetic data generation process produces scenarios whose safety properties transfer to real robot deployments; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Hazard-aware image editing combined with temporal prompt generation and single-pass video synthesis produces realistic hazardous robotic scenarios suitable for safety evaluation.
    This premise is required for the generated 10,000 clips to serve as a valid benchmark and training resource.

pith-pipeline@v0.9.1-grok · 5796 in / 1298 out tokens · 31005 ms · 2026-06-26T21:04:57.402346+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

18 extracted references · 1 canonical work pages

  1. [1]

    Zico Kolter and Hamed Hassani and George J

    Alexander Robey and Zachary Ravichandran and Eliot Krzysztof Jones and Jared Perlo and Fazl Barez and Vijay Kumar and J. Zico Kolter and Hamed Hassani and George J. Pappas , title =. Science Robotics , volume =. 2026 , doi =. https://www.science.org/doi/pdf/10.1126/scirobotics.aef2191 , abstract =

    work page doi:10.1126/scirobotics.aef2191 2026

  2. [2]

    ICLR , year=

    Jailbreak in Pieces: Compositional Adversarial Attacks on Multi-Modal Language Models , author=. ICLR , year=

  3. [3]

    arXiv preprint arXiv:2404.03027 , year=

    JailBreakV: A Benchmark for Assessing the Robustness of MultiModal Large Language Models against Jailbreak Attacks , author=. arXiv preprint arXiv:2404.03027 , year=

    arXiv

  4. [4]

    ECCV , year=

    MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models , author=. ECCV , year=

  5. [5]

    arXiv preprint arXiv:2410.18927 , year=

    SafeBench: A Safety Evaluation Framework for Multimodal Large Language Models , author=. arXiv preprint arXiv:2410.18927 , year=

    arXiv

  6. [6]

    arXiv preprint arXiv:2402.02207 , year=

    Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models , author=. arXiv preprint arXiv:2402.02207 , year=

    arXiv

  7. [7]

    arXiv preprint arXiv:2301.04104 , year=

    Mastering Diverse Domains through World Models , author=. arXiv preprint arXiv:2301.04104 , year=

    Pith/arXiv arXiv

  8. [8]

    Nature , volume=

    Mastering Diverse Control Tasks through World Models , author=. Nature , volume=

  9. [9]

    arXiv preprint arXiv:2309.17080 , year=

    GAIA-1: A Generative World Model for Autonomous Driving , author=. arXiv preprint arXiv:2309.17080 , year=

    Pith/arXiv arXiv

  10. [10]

    arXiv preprint arXiv:2604.01346 , year=

    Safety, Security, and Cognitive Risks in World Models , author=. arXiv preprint arXiv:2604.01346 , year=

    Pith/arXiv arXiv

  11. [11]

    arXiv preprint arXiv:2604.05498 , year=

    JailWAM: Jailbreaking World Action Models in Robot Control , author=. arXiv preprint arXiv:2604.05498 , year=

    Pith/arXiv arXiv

  12. [12]

    arXiv preprint arXiv:2307.15818 , year=

    RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control , author=. arXiv preprint arXiv:2307.15818 , year=

    Pith/arXiv arXiv

  13. [13]

    arXiv preprint arXiv:2406.09246 , year=

    OpenVLA: An Open-Source Vision-Language-Action Model , author=. arXiv preprint arXiv:2406.09246 , year=

    Pith/arXiv arXiv

  14. [14]

    arXiv preprint arXiv:2411.13587 , year=

    Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics , author=. arXiv preprint arXiv:2411.13587 , year=

    arXiv

  15. [15]

    NeurIPS , year=

    BadVLA: Towards Backdoor Attacks on Vision-Language-Action Models via Objective-Decoupled Optimization , author=. NeurIPS , year=

  16. [16]

    NeurIPS , year=

    SafeVLA: Towards Safety Alignment of Vision-Language-Action Models via Constrained Learning , author=. NeurIPS , year=

  17. [17]

    arXiv preprint arXiv:2503.10076 , year=

    VMBench: A Benchmark for Perception-Aligned Video Motion Generation , author=. arXiv preprint arXiv:2503.10076 , year=

    arXiv

  18. [18]

    arXiv preprint arXiv:2601.15282 , year=

    Rethinking Video Generation Model for the Embodied World , author=. arXiv preprint arXiv:2601.15282 , year=

    arXiv