A Multi-policy Approach Based on Clustering for Minimizing the Damage on a Falling Ballbot

Fall resilience remains a significant challenge for robots capable of locomotion. In the case of humanoid robots, arms can be utilized to mitigate impact damage, thereby improving resilience to falls. Although previous work has primarily focused on damage reduction strategies for legged robots, the question of minimizing fall damage in humanoid ballbots remains largely unexplored. In this paper, we introduce a novel multi-policy approach, combining clustering on the initial pose and Reinforcement Learning, to reduce damage resulting from a fall in a commercial humanoid ballbot. We conduct simulations to compare our method against three baselines: (1) a curriculum learning policy, (2) a single-policy approach enhanced by clustering information, and (3) a standard single-policy baseline. Experimental results demonstrate that our approach achieves higher success rates and greater damage reduction compared to existing alternatives.