研究人员开发了两个新的人形机器人控制系统。AnyBody允许使用任意关键点子集进行全身控制，克服了先前需要全身动作捕捉或上下半身分开控制的限制。另一方面，ReactiveBFM专注于实时闭环运动规划，以实现动态环境中反应式全身协调和错误恢复，在Unitree G1人形机器人上展示了令人印象深刻的敏捷性和零样本目标达成能力。 AI
影响 人形机器人控制领域的这些进步可能会加速开发更通用、更灵敏的机器人，以应用于各种场景。
排序理由 arXiv上发表了两篇研究论文，详细介绍了人形机器人控制的新方法。
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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: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, …