HumanoidTTT: Test-Time Capability Reuse for Efficient Humanoid Control

Summary

Abstract Summary

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.

Key Points

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.

METHOD: HUMANOIDTTT

HumanoidTTT instantiates Test-Time Capability Learning by maintaining a persistent Full-Motion Store during continual deployment. Fig. 2 summarizes its read, acquisition, and consolidation paths. Entry Applicability selects validated complete motions for direct execution from changed robot states. Reuse misses invoke a Frozen Motion Generator followed by fixed execution-based qualification.

Experiments

We evaluate HumanoidTTT along four dimensions: selective full-motion reuse reliability, accepted-reuse preparation efficiency, end-to-end inference efficiency, and finite-capacity capability consolidation. We use a frozen motion generator and a fixed execution stack.

A stored capability consists of a validated complete G1 motion together with the entry information used to determine whether that motion remains executable from the current state.

Experiments — HumanoidTTT: Test-Time Capability Reuse for Efficient Humanoid Control — Fig. 4: Capacity and request dynamics. (a) Calls avoided versus Store capacity. (b) Online-minus-frozen gains across three request families. Error bars show stream-level 95% bootstrap confidence intervals.

Sources and Demonstrations

Figures are reproduced from Yang et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.