# SRL-VIC: A Variable Stiffness-Based Safe Reinforcement Learning for Contact-Rich Robotic Tasks

arXiv: https://arxiv.org/abs/2406.13744 · HTML: https://mlsanity.com/paper/2406.13744/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 19 Jun 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 14 of 14)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning](https://mlsanity.com/paper/2108.06266/) (2021)
- [Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones](https://mlsanity.com/paper/2010.15920/) (2020)
- [Learning to be Safe: Deep RL with a Safety Critic](https://mlsanity.com/paper/2010.14603/) (2020)
- [Conservative Safety Critics for Exploration](https://mlsanity.com/paper/2010.14497/) (2020)
- [Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks](https://mlsanity.com/paper/1907.13098/) (2019)
- [Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks](https://mlsanity.com/paper/1906.08880/) (2019)
- [Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks](https://mlsanity.com/paper/2004.14404/) (2020)
- [Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments](https://mlsanity.com/paper/2010.11940/) (2020)
- [Variable impedance control and learning -- A review](https://mlsanity.com/paper/2010.06246/) (2020)
- [Learning Variable Impedance Control via Inverse Reinforcement Learning for Force-Related Tasks](https://mlsanity.com/paper/2102.06838/) (2021)
- [Impedance Adaptation by Reinforcement Learning with Contact Dynamic Movement Primitives](https://mlsanity.com/paper/2203.07191/) (2022)
- [A Self-Tuning Impedance-based Interaction Planner for Robotic Haptic Exploration](https://mlsanity.com/paper/2203.05413/) (2022)

## Built on by (most cited first, 15 of 15)

- [A Survey on Imitation Learning for Contact-Rich Tasks in Robotics](https://mlsanity.com/paper/2506.13498/) (2025)
- [Don't Let Your Robot be Harmful: Responsible Robotic Manipulation via Safety-as-Policy](https://mlsanity.com/paper/2411.18289/) (2024)
- [HYPERmotion: Learning Hybrid Behavior Planning for Autonomous Loco-manipulation](https://mlsanity.com/paper/2406.14655/) (2024)
- [Scaling Laws in Scientific Discovery with AI and Robot Scientists](https://mlsanity.com/paper/2503.22444/) (2025)
- [Bresa: Bio-inspired Reflexive Safe Reinforcement Learning for Contact-Rich Robotic Tasks](https://mlsanity.com/paper/2503.21989/) (2025)
- [Passivity-Centric Safe Reinforcement Learning for Contact-Rich Robotic Tasks](https://mlsanity.com/paper/2503.00287/) (2025)
- [HiBerNAC: Hierarchical Brain-inspired Robotic Neural Agent Collective for Disentangling Complex Manipulation](https://mlsanity.com/paper/2506.08296/) (2025)
- [INTENTION: Inferring Tendencies of Humanoid Robot Motion Through Interactive Intuition and Grounded VLM](https://mlsanity.com/paper/2508.04931/) (2025)
- [VIDP: Variable Impedance Diffusion Policy for Compliant Robot Manipulation from Diverse Demonstrations](https://mlsanity.com/paper/2608.06210/) (2026)
- [CR-VLA-Force: Learning Control-aware Compliance VLA Model for Robust Contact-rich Robotic Manipulation](https://mlsanity.com/paper/2609.05832/) (2026)
- [On the role of Artificial Intelligence methods in modern force-controlled manufacturing robotic tasks](https://mlsanity.com/paper/2409.16828/) (2024)
- [Reversible Simplex Supervision with Post-Action Debt Accounting for Goal-Reaching RL](https://mlsanity.com/paper/2601.19499/) (2026)
- [Reward-Zero: Language Embedding Driven Implicit Reward Mechanisms for Reinforcement Learning](https://mlsanity.com/paper/2603.09331/) (2026)
- [Efficient Real-World Online Reinforcement Learning for Robot Manipulation via Centralized Training and Critic Decomposition](https://mlsanity.com/paper/2608.09762/) (2026)
- [Potential-Field Action Representation for Reinforcement Learning in Contact-Rich Manipulation](https://mlsanity.com/paper/2609.21609/) (2026)

## Abstract (arXiv)

Reinforcement learning (RL) has emerged as a promising paradigm in complex and continuous robotic tasks, however, safe exploration has been one of the main challenges, especially in contact-rich manipulation tasks in unstructured environments. Focusing on this issue, we propose SRL-VIC: a model-free safe RL framework combined with a variable impedance controller (VIC). Specifically, safety critic and recovery policy networks are pre-trained where safety critic evaluates the safety of the next action using a risk value before it is executed and the recovery policy suggests a corrective action if the risk value is high. Furthermore, the policies are updated online where the task policy not only achieves the task but also modulates the stiffness parameters to keep a safe and compliant profile. A set of experiments in contact-rich maze tasks demonstrate that our framework outperforms the baselines (without the recovery mechanism and without the VIC), yielding a good trade-off between efficient task accomplishment and safety guarantee. We show our policy trained on simulation can be deployed on a physical robot without fine-tuning, achieving successful task completion with robustness and generalization. The video is available at https://youtu.be/ksWXR3vByoQ.