Self-rising Bipedal Robot for Embracing Fall Impact and Fall Detection with Multimodal Sensing

Humanoid robots are inherently unstable, making fall management critical, especially for hardware-based reinforcement learning (RL), where falls frequently occur. This paper introduces a lantern-shaped mechanical cover designed for a kid-sized humanoid robot to mitigate damage during falls and support autonomous recovery. A multimodal fall detection method integrating inertial, proprioceptive, and acoustic sensors was implemented alongside an improved stance phase detection algorithm that eliminates reliance on heuristic thresholds. Hardware experiments on the Hybrid Leg biped robot demonstrated improved walking robustness and revealed a 57.4% success rate for autonomous recovery after induced falls. Results indicated that perturbations in vertical (z) and positive forward (x) foot trajectories posed the greatest challenges to successful recovery.