Reducing Risk for Assistive Reinforcement Learning Policies with Diffusion Models
This work addresses safety concerns for assistive robots interacting with humans, but it appears incremental as it builds on conventional RL methods.
The study tackled the problem of safety in assistive robotics by applying reinforcement learning and imitation learning to improve policy design, demonstrating enhanced safety without requiring additional environmental interactions in simulated tasks.
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.