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 rein- forcement 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 detec- tion method integrating inertial, proprioceptive, and acoustic sensors was implemented alongside an improved stance phase detection algorithm that eliminates reliance on heuristic thresh- olds. 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.