Fall Mitigation and Fall Recovery

Two related abilities for a humanoid: minimizing damage while it falls, and getting itself back up afterward.

A humanoid robot has a high center of mass and a small support base, so being pushed, stepping into a gap, or slipping can all cause a fall, and a single hard fall can damage joints, cameras, and the outer shell. Fall safety is usually split into three stages. Where possible, balance control and stepping (push recovery) are used to avoid falling in the first place. Once a fall becomes unavoidable, fall mitigation kicks in: bending the knees to lower the center of mass, reaching out an arm or stepping to make contact with the ground early, and adjusting posture to spread the impact onto sturdier parts of the body. After landing, fall recovery means getting back up from whatever position the robot ended up in. Earlier work mostly relied on hand-designed motion sequences and trajectory optimization — for example, Wang and Hauser in 2018 used contact-sequence tree search to plan multi-contact protective motions like stepping and bracing with the hands — while the mainstream approach in the last couple of years has been training a policy in simulation with reinforcement learning and transferring it to the real robot, with recent work starting to fold all three stages into a single policy.

ExampleHoST (RSS 2025) used reinforcement learning in simulation to train Unitree's G1 to get up from a wide variety of fallen postures, without relying on any preset motion trajectory, and deployed it directly to indoor and outdoor scenes on the real robot; another piece of work from November 2025 combined a small number of human demonstrations with reinforcement learning on the G1 to unify fall prevention, impact mitigation, and getting up into a single policy.

Also called

Humanoid Fall Safety, Fall Protection

Related

Fall Recovery · HoST (Humanoid Standing-up) · Push Recovery · Balance Control · Damping Mode · Humanoid Robot

Sources

Unified Humanoid Fall-Safety Policy from a Few Demonstrations (arXiv 2511.07407)
Learning Humanoid Standing-up Control across Diverse Postures (HoST, arXiv 2502.08378)
Unified Multi-Contact Fall Mitigation Planning for Humanoids via Contact Transition Tree Optimization (arXiv 1807.08667)

As of

2025-11