mlsanityData to 14 Oct 2026

Shared Control of Holonomic Wheelchairs through Reinforcement Learning

arXiv 2507.17055 ↗First posted 22 Jul 2025cs.RO cs.LGcited by 0

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

Abstract (arXiv)

Smart electric wheelchairs can improve user experience by supporting the driver with shared control. State-of-the-art work showed the potential of shared control in improving safety in navigation for non-holonomic robots. However, for holonomic systems, current approaches often lead to unintuitive behavior for the user and fail to utilize the full potential of omnidirectional driving. Therefore, we propose a reinforcement learning-based method, which takes a 2D user input and outputs a 3D motion while ensuring user comfort and reducing cognitive load on the driver. Our approach is trained in Isaac Gym and tested in simulation in Gazebo. We compare different RL agent architectures and reward functions based on metrics considering cognitive load and user comfort. We show that our method ensures collision-free navigation while smartly orienting the wheelchair and showing better or competitive smoothness compared to a previous non-learning-based method. We further perform a sim-to-real transfer and demonstrate, to the best of our knowledge, the first real-world implementation of RL-based shared control for an omnidirectional mobility platform.

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 19 of its 31 references that are in the map. First posted on arXiv 22 Jul 2025.

Builds on

19 of 31 references.

References · 19

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2016Generative Adversarial Imitation Learningcited by 1,597
  3. 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 998
  4. 2016Benchmarking Deep Reinforcement Learning for Continuous Controlcited by 371
  5. 2023Real-World Humanoid Locomotion with Reinforcement Learningcited by 187
  6. 2020Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPOcited by 186
  7. 2018Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learningcited by 175
  8. 2022Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behaviorcited by 138
  9. 2023Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learningcited by 111
  10. 2018Shared Autonomy via Deep Reinforcement Learningcited by 66
  11. 2020RLOC: Terrain-Aware Legged Locomotion using Reinforcement Learning and Optimal Controlcited by 59
  12. 2021Partially Observable Markov Decision Processes (POMDPs) and Roboticscited by 49
7 more
  1. 2024Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robotscited by 48
  2. 2023Emergence of Maps in the Memories of Blind Navigation Agentscited by 33
  3. 2020Residual Policy Learning for Shared Autonomycited by 26
  4. 2021Crowd-Aware Robot Navigation for Pedestrians with Multiple Collision Avoidance Strategies via Map-based Deep Reinforcement Learningcited by 12
  5. 2018CrowdMove: Autonomous Mapless Navigation in Crowded Scenarioscited by 8
  6. 2022Pedestrian-Robot Interactions on Autonomous Crowd Navigation: Reactive Control Methods and Evaluation Metricscited by 5
  7. 2023Understanding Shared Control for Assistive Robotic Armscited by 1

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  1. 2025Follow-Me in Micro-Mobility with End-to-End Imitation Learning
  2. 2025A Shared Control Framework for Mobile Robots with Planning-Level Intention Prediction ↗
  3. 2026A Reinforcement Learning Framework for Safety-Aware Haptic Shared Control in Surgical Robotics
  4. 2023To the Noise and Back: Diffusion for Shared Autonomy
  5. 2025COMPASS: Cross-embodiment Mobility Policy via Residual RL and Skill Synthesis
  6. 2026Adaptive Reinforcement and Model Predictive Control Switching for Safe Human-Robot Cooperative Navigation
  7. 2024Reinforcement Learning for Motor Control: A Comprehensive Review
  8. 2025Assistax: A Multi-Agent Hardware-Accelerated Reinforcement Learning Benchmark for Assistive Robotics