Robust Fall Recovery for Armless Bipedal-Wheeled Robots Via Force-Guided Learning
For researchers in legged robotics, this work addresses a challenging problem (fall recovery without arms) with a novel force-guided learning approach, showing strong experimental results on a real robot.
This paper tackles fall recovery for armless bipedal-wheeled robots, introducing FTSR (Force-guided Teacher-student framework with Stage-wise Rewards) that uses constrained reinforcement learning with an auxiliary force to enable recovery. Experiments on a physical robot demonstrate robust recovery under diverse conditions, with the framework also generalizing to a high-DOF humanoid.
Fall recovery is critical for autonomous legged locomotion. Existing methods have demonstrated that some legged robots, such as humanoids and quadrupeds, are capable of fall recovery from diverse postures by utilizing arms or coordinating multi-legs to generate support forces. Without arms or other legs to provide supportive assistance, a bipedal-wheeled robot must rely solely on the actuation of its legs, making recovery particularly difficult. To address this, we introduce FTSR (Force-guided Teacher-student framework with Stage-wise Rewards). The force-guided method constructs an external auxiliary force during simulation training that correlates directly with the robot's real-time height, explicitly formulating this force as an optimizable constraint. Through constrained reinforcement learning, the policy is guided toward reducing force dependency gradually and increasing the body height, developing internal recovery strategies despite having no arms for support. Height-progressive stage-Wise rewards progressively structure posture stabilization during recovery and transition to sustained locomotion, integrated with teacher-student architecture distilling privileged knowledge of force effects and recovery dynamics. After simulation training, the policy is deployed on a physical armless bipedal-wheeled robot and extensively evaluated. Experiments confirm robust and reliable fall recovery under diverse challenging conditions, demonstrating strong environmental adaptability and motion robustness, while maintaining full post-recovery motion capability. The framework also generalizes effectively to a high-DOF humanoid, confirming its practical generalizability. The project page is available at https://2350575870.github.io/force-guided.github.io/