# Learning Failure Prevention Skills for Safe Robot Manipulation

arXiv: https://arxiv.org/abs/2305.02807 · HTML: https://mlsanity.com/paper/2305.02807/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2023.

## Built on by (most cited first, 5 of 5)

- [RoboDexVLM: Visual Language Model-Enabled Task Planning and Motion Control for Dexterous Robot Manipulation](https://mlsanity.com/paper/2503.01616/) (2025)
- [I-FailSense: Towards General Robotic Failure Detection with Vision-Language Models](https://mlsanity.com/paper/2509.16072/) (2025)
- [What Breaks Embodied AI Security:LLM Vulnerabilities, CPS Flaws,or Something Else?](https://mlsanity.com/paper/2602.17345/) (2026)
- [UniManip: General-Purpose Zero-Shot Robotic Manipulation with Agentic Operational Graph](https://mlsanity.com/paper/2602.13086/) (2026)
- [Enhancing Goal Inference via Correction Timing](https://mlsanity.com/paper/2602.18603/) (2026)

## Abstract (arXiv)

Robots are more capable of achieving manipulation tasks for everyday activities than before. But the safety of manipulation skills that robots employ is still an open problem. Considering all possible failures during skill learning increases the complexity of the process and restrains learning an optimal policy. Beyond that, in unstructured environments, it is not easy to enumerate all possible failures beforehand. In the context of safe skill manipulation, we reformulate skills as base and failure prevention skills where base skills aim at completing tasks and failure prevention skills focus on reducing the risk of failures to occur. Then, we propose a modular and hierarchical method for safe robot manipulation by augmenting base skills by learning failure prevention skills with reinforcement learning, forming a skill library to address different safety risks. Furthermore, a skill selection policy that considers estimated risks is used for the robot to select the best control policy for safe manipulation. Our experiments show that the proposed method achieves the given goal while ensuring safety by preventing failures. We also show that with the proposed method, skill learning is feasible, novel failures are easily adaptable, and our safe manipulation tools can be transferred to the real environment.