New AI systems enable advanced humanoid robot control and reactivity · 2 sources tracked
ByPulseAugur Editorial·[7 sources]·
Researchers have developed two new systems for controlling humanoid robots. AnyBody allows for whole-body control using any subset of keypoints, overcoming limitations of previous methods that required full-body motion capture or separate upper/lower body control. ReactiveBFM, on the other hand, focuses on real-time, closed-loop motion planning to enable reactive whole-body coordination and error recovery in dynamic environments, demonstrating impressive agility and zero-shot target reaching on a Unitree G1 humanoid.
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These advancements in humanoid robot control could accelerate the development of more versatile and responsive robots for various applications.
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Two research papers published on arXiv detailing new methods for humanoid robot control.
arXiv:2506.12851v3 Announce Type: replace-cross Abstract: Humanoid robots are promising to acquire various skills by imitating human behaviors. However, existing algorithms are only capable of tracking smooth, low-speed human motions, even with delicate reward and curriculum desi…
arXiv:2509.15443v2 Announce Type: replace-cross Abstract: Human-to-humanoid imitation learning presents a promising pathway to address the severe data scarcity bottleneck in robotics by utilizing abundant, large-scale human motion collections. However, scaling this paradigm requi…
arXiv:2606.30362v1 Announce Type: cross Abstract: While current Behavior Foundation Models (BFMs) provide robust control priors for humanoids, they only execute pre-defined reference motions. As a result, they are vulnerable to environmental shifts and incapable of reactive whole…
arXiv:2606.29209v1 Announce Type: cross Abstract: We present AnyBody, a unified whole-body humanoid controller driven by an arbitrary subset of body keypoints chosen at deploy time. Prior physics-based trackers either rely on expensive full-body motion capture and error-prone tra…
While current Behavior Foundation Models (BFMs) provide robust control priors for humanoids, they only execute pre-defined reference motions. As a result, they are vulnerable to environmental shifts and incapable of reactive whole-body coordination. Naively cascading them with ge…
arXiv cs.CV
TIER_1English(EN)·Xiaofei Hui, Bo Yan, Haoxuan Qu, Hossein Rahmani, Jun Liu·
arXiv:2607.01990v1 Announce Type: new Abstract: Training-free controllable motion generation has attracted growing interest for enabling flexible constraint enforcement without constraint-specific training. However, existing training-free methods require constraints to be continu…
Training-free controllable motion generation has attracted growing interest for enabling flexible constraint enforcement without constraint-specific training. However, existing training-free methods require constraints to be continuous objective-based with differentiable losses, …
