发表机构
Peking University（北京大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对人形机器人持续部署中运动能力重复请求的挑战，提出HumanoidTTT框架，通过选择性全身运动复用和测试时能力整合，实现零不安全接受和16.4倍加速，并自适应保留有用能力。
AI 中文摘要
近期在运动生成和全身跟踪方面的进展使类人机器人能够执行日益多样化的运动，然而在持续部署过程中，相同的运动能力可能会被重复请求。可靠的复用具有挑战性，因为干预运动可能改变机器人的进入状态，使得先前成功的运动在盲目重放时不再安全。同时，经过验证的能力在部署过程中不断积累，而有限的存储空间要求决定哪些能力值得保留。为解决这些挑战，我们提出了HumanoidTTT，一个用于持续人形控制中测试时能力复用的框架。具体而言，我们引入了选择性全身运动复用，该机制仅授权从经过认证的适用进入状态直接复用已验证的完整运动，从而允许接受的复用绕过新生成。我们进一步引入了测试时能力整合，该机制利用后续部署复用作为反馈，调整哪些合格能力在有限的全身运动存储中持续存在。实验表明，零次不安全接受，并且端到端速度比新生成快16.4倍，而在线整合相比冻结版本，每200个请求避免了13.2次生成器调用。总体而言，HumanoidTTT实现了对已验证运动能力的可靠高效复用，同时在持续部署过程中自适应地保留有用能力。代码：此https URL。网站：此https URL。
英文摘要
Recent advances in motion generation and whole-body tracking have enabled humanoid robots to execute increasingly diverse motions, yet the same motion capabilities may be requested repeatedly during continual deployment. Reliable reuse is challenging because intervening motions can change the robot's entry state, making previously successful motions unsafe to replay blindly. Meanwhile, validated capabilities accumulate during deployment, while bounded storage requires deciding which ones are worth retaining. To address these challenges, we present HumanoidTTT, a framework for test-time capability reuse in continual humanoid control. Specifically, we introduce Selective Full-Motion Reuse, which authorizes direct reuse of validated complete motions only from certified applicable entry states, allowing accepted reuse to bypass fresh generation. We further introduce Test-Time Capability Consolidation, which adapts which qualified capabilities persist in a bounded Full-Motion Store using subsequent deployment reuse as feedback. Experiments demonstrate zero unsafe accepts and a 16.4$\times$ end-to-end speedup over fresh generation, while online consolidation improves avoided generator calls by 13.2 per 200 requests over its frozen counterpart. Overall, HumanoidTTT enables reliable and efficient reuse of validated motion capabilities while adaptively retaining useful capabilities throughout continual deployment. Code: https://github.com/AIGeeksGroup/HumanoidTTT. Website: https://aigeeksgroup.github.io/HumanoidTTT.
