LHM-Humanoid: Long-Horizon Human Motion Control for Continuous Object Transport in Cluttered Scenes
arXiv:2508.16943v3 Announce Type: replace-cross 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
Advances in physics-based simulation and AI control algorithms are enabling more complex, continuous, and robust robotic behaviors, moving beyond isolated task demonstrations.
This development pushes the frontier of general-purpose humanoid robot capabilities, addressing a key challenge in unstructured environments and continuous operation.
The ability of humanoid robots to perform long-horizon, continuous tasks involving object manipulation and navigation in complex settings improves dramatically, decreasing reliance on frequent re-initialization.
- · Humanoid robotics developers
- · Logistics and manufacturing sectors
- · AI researchers in motion control
- · Companies reliant on highly structured, repetitive manual labor
- · Developers of less robust, short-horizon robotic control systems
Humanoid robots become capable of performing more complex, multi-stage sequences of tasks without human intervention.
Increased commercial viability and deployment of humanoid robots in new sectors due to enhanced autonomy and dexterity.
Accelerated development of general-purpose AI for physical world interaction, blurring the lines between simulation and reality for robot training.
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Read at arXiv cs.AI