mlsanityData to 14 Oct 2026

Towards Online Safety Corrections for Robotic Manipulation Policies

arXiv 2409.08233 ↗First posted 12 Sep 2024cs.ROcited by 0

On the map: Deep reinforcement learning methods → Safe and constrained RL

Abstract (arXiv)

Recent successes in applying reinforcement learning (RL) for robotics has shown it is a viable approach for constructing robotic controllers. However, RL controllers can produce many collisions in environments where new obstacles appear during execution. This poses a problem in safety-critical settings. We present a hybrid approach, called iKinQP-RL, that uses an Inverse Kinematics Quadratic Programming (iKinQP) controller to correct actions proposed by an RL policy at runtime. This ensures safe execution in the presence of new obstacles not present during training. Preliminary experiments illustrate our iKinQP-RL framework completely eliminates collisions with new obstacles while maintaining a high task success rate.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Safe and constrained RL, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 13 references that are in the map. First posted on arXiv 12 Sep 2024.

Builds on

8 of 13 references.

References · 8

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2017Safe Reinforcement Learning via Shieldingcited by 449
  3. 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learningcited by 371
  4. 2016Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updatescited by 353
  5. 2020Conservative Safety Critics for Explorationcited by 113
  6. 2020Verifiably Safe Exploration for End-to-End Reinforcement Learningcited by 34
  7. 2020Safe Reinforcement Learning with Natural Language Constraintscited by 23
  8. 2023Multi-Arm Robot Task Planning for Fruit Harvesting Using Multi-Agent Reinforcement Learningcited by 3

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  1. 2026Safe Execution of RL Policies Via Acceleration-Based CBF-QP Constraint Enforcement for Real-World Robotic Deployments
  2. 2024SRL-VIC: A Variable Stiffness-Based Safe Reinforcement Learning for Contact-Rich Robotic Tasks
  3. 2026PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control
  4. 2025Skill-based Safe Reinforcement Learning with Risk Planning
  5. 2022Safe Inverse Reinforcement Learning via Control Barrier Function
  6. 2023Safe Reinforcement Learning in a Simulated Robotic Arm
  7. 2024A Dynamic Safety Shield for Safe and Efficient Reinforcement Learning of Navigation Tasks
  8. 2024Safety Optimized Reinforcement Learning via Multi-Objective Policy Optimization