mlsanityData to 14 Oct 2026

Reducing Risk for Assistive Reinforcement Learning Policies with Diffusion Models

arXiv 2405.07603 ↗First posted 13 May 2024cs.RO cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

Abstract (arXiv)

Care-giving and assistive robotics, driven by advancements in AI, offer promising solutions to meet the growing demand for care, particularly in the context of increasing numbers of individuals requiring assistance. This creates a pressing need for efficient and safe assistive devices, particularly in light of heightened demand due to war-related injuries. While cost has been a barrier to accessibility, technological progress is able to democratize these solutions. Safety remains a paramount concern, especially given the intricate interactions between assistive robots and humans. This study explores the application of reinforcement learning (RL) and imitation learning, in improving policy design for assistive robots. The proposed approach makes the risky policies safer without additional environmental interactions. Through experimentation using simulated environments, the enhancement of the conventional RL approaches in tasks related to assistive robotics is demonstrated.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 9 of its 13 references that are in the map. First posted on arXiv 13 May 2024.

Builds on

9 of 13 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning29 Jan

Other references · 8

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2023Diffusion Policy: Visuomotor Policy Learning via Action Diffusioncited by 2,838
  3. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  4. 2018Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Reviewcited by 482
  5. 2024Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperationcited by 391
  6. 2019Assistive Gym: A Physics Simulation Framework for Assistive Roboticscited by 44
  7. 2022RCareWorld: A Human-centric Simulation World for Caregiving Robotscited by 14
  8. 2022Learning Representations that Enable Generalization in Assistive Taskscited by 13

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Similar, not cited

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  1. 2024Detecting Unsafe Behavior in Neural Network Imitation Policies for Caregiving Robotics
  2. 2024Robustifying a Policy in Multi-Agent RL with Diverse Cooperative Behaviors and Adversarial Style Sampling for Assistive Tasks
  3. 2026Learning Robot Policies for Whole-Body Mobility Assistance via Dynamic Human Motion Generation ↗
  4. 2025Assistax: A Multi-Agent Hardware-Accelerated Reinforcement Learning Benchmark for Assistive Robotics
  5. 2025Integrating Reinforcement Learning and AI Agents for Adaptive Robotic Interaction and Assistance in Dementia Care
  6. 2026Learning to Assist: Physics-Grounded Human-Human Control via Multi-Agent Reinforcement Learning
  7. 2023Safe Reinforcement Learning in a Simulated Robotic Arm
  8. 2021Credit Assignment Safety Learning from Human Demonstrations