LHM-Humanoid: Long-Horizon Human Motion Control for Continuous Object Transport in Cluttered Scenes
LHM-Humanoid achieves long-horizon human motion control for continuous object transport in cluttered scenes, outperforming baselines significantly.
Key Findings
Methodology
LHM-Humanoid uses goal-conditioned controllers, adversarial motion priors, and DAgger distillation to form a unified policy. It includes two controllers: one completes the fetch-carry-place cycle, and another takes over from the resulting state distribution. By learning release-and-retreat behavior, it ensures each cycle ends in a recoverable region.
Key Results
- LHM-Humanoid achieves a 72.4% success rate across 350 cluttered layouts, significantly outperforming end-to-end RL and hierarchical RL.
- On 66 unseen tasks, it maintains a 63.2% success rate, demonstrating robust generalization.
- In zero-shot scenarios, LHM-Humanoid extends from two-object to five-object sequences, where baselines nearly fail.
Significance
This study is significant in the field of physics-based human motion control, addressing the challenge of long-horizon, reset-free motion control. It enhances the stability and success rate of simulated characters in complex scenes, offering new insights for future robotics and VR applications.
Technical Contribution
LHM-Humanoid introduces the recoverability principle and adversarial motion priors, overcoming existing methods' limitations in long-horizon motion control. Its dual-teacher mechanism and DAgger distillation provide new technical pathways for continuous motion control in complex scenes.
Novelty
This method is the first to treat recoverability as a core principle for long-horizon motion control, achieved through learned termination behaviors. This innovation enables continuous, reset-free motion in complex scenes.
Limitations
- In extremely complex scenarios, the method may still face stability issues, especially as the number of objects increases.
- Requires substantial training data and computational resources, limiting immediate practical application.
Future Work
Future research could explore applying this method in more complex scenarios and integrating additional sensory inputs, such as tactile and visual, to enhance system robustness and adaptability.
AI Executive Summary
LHM-Humanoid is an innovative physics-based human motion control method designed to address the challenge of long-horizon motion for continuous object transport in cluttered scenes. Existing methods typically reset over short durations, while LHM-Humanoid achieves reset-free continuous motion by introducing the principle of recoverability.
The method employs dual goal-conditioned controllers, combined with adversarial motion priors and DAgger distillation, to form a unified policy. One controller completes the fetch-carry-place cycle, while another takes over from the resulting state distribution. By learning release-and-retreat behavior, it ensures each cycle ends in a recoverable region.
Experimental results show that LHM-Humanoid achieves a 72.4% success rate across 350 cluttered layouts and maintains a 63.2% success rate on 66 unseen tasks. In zero-shot scenarios, it extends from two-object to five-object sequences, where baselines nearly fail. This research provides new perspectives and technical pathways for the field of physics-based human motion control.
Deep Dive
Abstract
Physics-based human motion control can make a simulated character walk, sit, and manipulate objects with high physical realism. Almost always, though, this happens in short, isolated clips that are re-initialized between interactions. We instead aim for continuous, reset-free long-horizon motion: a physically simulated humanoid that repeatedly walks to a displaced object, lifts it with a balanced whole-body posture, carries it past obstacles, and places it at a goal, over and over within a single uninterrupted take. The hard part is not any individual motion but the transitions between them. Without a reset, each cycle must end in a state that both leaves the object just placed undisturbed and lets the next cycle begin, yet every placement leaves the character off-balance in a non-canonical pose where naive end-to-end reinforcement learning fails. Our key idea is to treat this handoff as a two-sided problem of recoverability: the character must disengage from the object it just placed so the prior success is preserved, and settle into a state from which a balanced continuation exists. Instead of engineering a transition by hand, we learn to shape where each cycle ends so that it lands in this recoverable region. We introduce LHM-Humanoid. One goal-conditioned controller completes a fetch--carry--place cycle and, through a learned release-and-retreat behavior, steers its terminal state into this region; a second controller then takes over from the resulting state distribution. Both are regularized by an adversarial motion prior and distilled into a single goal-conditioned policy that runs the whole sequence as one reset-free rollout. Across 350 cluttered layouts spanning four room types, LHM-Humanoid produces far more successful and stable long-horizon motion than end-to-end RL, hierarchical RL, and prior physics-based human-scene-interaction methods, on both seen and unseen scenes.