Shared Control of Holonomic Wheelchairs through Reinforcement Learning
This work addresses the challenge of intuitive and efficient shared control for holonomic wheelchairs, offering a novel solution that could enhance mobility for users, though it is incremental as it builds on existing shared control concepts with a new method.
The paper tackles the problem of shared control for holonomic wheelchairs, which often leads to unintuitive behavior, by proposing a reinforcement learning-based method that outputs 3D motion from 2D user input to improve user comfort and reduce cognitive load. The result shows collision-free navigation with better or competitive smoothness compared to a previous non-learning-based method, and it includes the first real-world implementation of RL-based shared control for an omnidirectional platform.
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.