# Safe Learning for Contact-Rich Robot Tasks: A Survey from Classical Learning-Based Methods to Safe Foundation Models

arXiv: https://arxiv.org/abs/2512.11908 · HTML: https://mlsanity.com/paper/2512.11908/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2025.

## Built on by (most cited first, 7 of 7)

- [TacVLA: Contact-Aware Tactile Fusion for Robust Vision-Language-Action Manipulation](https://mlsanity.com/paper/2603.12665/) (2026)
- [CMP: Robust Whole-Body Tracking for Loco-Manipulation via Competence Manifold Projection](https://mlsanity.com/paper/2604.07457/) (2026)
- [DreamAvoid: Critical-Phase Test-Time Dreaming to Avoid Failures in VLA Policies](https://mlsanity.com/paper/2605.11750/) (2026)
- [Safe Embodied AI for Long-horizon Tasks: A Cross-layer Analysis of Robotic Manipulation](https://mlsanity.com/paper/2606.05660/) (2026)
- [Learning of Robot Safety Policies via Adversarial Synthetic Scenarios](https://mlsanity.com/paper/2606.05952/) (2026)
- [Rule-based High-Level Coaching for Goal-Conditioned Reinforcement Learning in Search-and-Rescue UAV Missions Under Limited-Simulation Training](https://mlsanity.com/paper/2604.26833/) (2026)
- [Potential-Field Action Representation for Reinforcement Learning in Contact-Rich Manipulation](https://mlsanity.com/paper/2609.21609/) (2026)

## Abstract (arXiv)

Contact-rich tasks pose significant challenges for robotic systems due to inherent uncertainty, complex dynamics, and the high risk of damage during interaction. Recent advances in learning-based control have shown great potential in enabling robots to acquire and generalize complex manipulation skills in such environments, but ensuring safety, both during exploration and execution, remains a critical bottleneck for reliable real-world deployment. This survey provides a comprehensive overview of safe learning-based methods for robot contact-rich tasks. We categorize existing approaches into two main domains: safe exploration and safe execution. We review key techniques, including constrained reinforcement learning, risk-sensitive optimization, uncertainty-aware modeling, control barrier functions, and model predictive safety shields, and highlight how these methods incorporate prior knowledge, task structure, and online adaptation to balance safety and efficiency. A particular emphasis of this survey is on how these safe learning principles extend to and interact with emerging robotic foundation models, especially vision-language models (VLMs) and vision-language-action models (VLAs), which unify perception, language, and control for contact-rich manipulation. We discuss both the new safety opportunities enabled by VLM/VLA-based methods, such as language-level specification of constraints and multimodal grounding of safety signals, and the amplified risks and evaluation challenges they introduce. Finally, we outline current limitations and promising future directions toward deploying reliable, safety-aligned, and foundation-model-enabled robots in complex contact-rich environments. More details and materials are available at our href https://github.com/jack-sherman01/Awesome-Learning4Safe-Contact-rich-tasksProject GitHub Repository.