← 研踪 PaperTrailarXiv:2610.39327 · v3
SONIC-K1: Porting a Universal Humanoid Motion Tracker to a Small Humanoid on a Single GPU
首次提交 2026-10-29 12:16
版本更新 2026-11-05 11:03
雷达观测 2026-11-06 11:06
摘要
We present SONIC-K1, to our knowledge the first open-weights whole-body motion tracker for the Booster K1 humanoid, obtained by adapting the SONIC framework to this smaller robot. The policy is trained from scratch on a single shared GPU with a small fraction of the compute reported for SONIC. In MuJoCo simulation, it tracks nearly all held-out motion clips and performs well on external benchmarks under SONIC's success criterion. The first release ran on the real K1 but shook its legs on steps and turns, and simulation showed a sensitivity to actuation delay. We therefore post-trained it with the robot's deployment torque limits, randomized actuation delay and gain randomization. The post-trained v1.0 stays robust under real-robot torque limits and large actuation delays, where the first release often failed, and in our runs on the real K1 it walks and turns without the earlier leg shaking. These results show broad motion-tracking capability on a modest training budget. Code, policy weights and evaluation tools are publicly available.
作者备注：4 pages, 3 figures. Project page: https://kyry.me/sonic-k1
观测到的版本
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v3 · 2026-11-05 11:03 · 最新观测版本
SONIC-K1: Porting a Universal Humanoid Motion Tracker to a Small Humanoid on a Single GPU
We present SONIC-K1, to our knowledge the first open-weights whole-body motion tracker for the Booster K1 humanoid, obtained by adapting the SONIC framework to this smaller robot. The policy is trained from scratch on a single shared GPU with a small fraction of the compute reported for SONIC. In MuJoCo simulation, it tracks nearly all held-out motion clips and performs well on external benchmarks under SONIC's success criterion. The first release ran on the real K1 but shook its legs on steps and turns, and simulation showed a sensitivity to actuation delay. We therefore post-trained it with the robot's deployment torque limits, randomized actuation delay and gain randomization. The post-trained v1.0 stays robust under real-robot torque limits and large actuation delays, where the first release often failed, and in our runs on the real K1 it walks and turns without the earlier leg shaking. These results show broad motion-tracking capability on a modest training budget. Code, policy weights and evaluation tools are publicly available.
查看此版本解读 →查看此版本原文 ↗v2 · 2026-11-04 01:41
SONIC-K1: Porting a Universal Humanoid Motion Tracker to a Small Humanoid on a Single GPU
We present SONIC-K1, to our knowledge the first open-weights whole-body motion tracker for the Booster K1 humanoid, obtained by adapting the SONIC framework to this smaller robot. The policy is trained from scratch on a single shared GPU with a small fraction of the compute reported for SONIC. In MuJoCo simulation, it tracks nearly all held-out motion clips and performs well on external benchmarks under SONIC's success criterion. On the real K1, it tracks upper-body and slow motions, while stepping and turning remain less reliable, and simulation experiments show a sensitivity to action latency. These results show broad motion-tracking capability on a modest training budget and point to quantitative hardware evaluation as the main next step. Code, policy weights and evaluation tools are publicly available.
查看此版本解读 →查看此版本原文 ↗v1 · 2026-10-29 12:16
SONIC-K1: Porting a Universal Humanoid Motion Tracker to a Small Humanoid on a Single GPU
We present SONIC-K1, to our knowledge the first open-weights whole-body motion tracker for the Booster K1 humanoid, obtained by adapting the SONIC framework to this smaller robot. The policy is trained from scratch on a single shared GPU with a small fraction of the compute reported for SONIC. In MuJoCo simulation, it tracks nearly all held-out motion clips and performs well on external benchmarks under SONIC's success criterion. On the real K1, it tracks upper-body and slow motions, while stepping and turning remain less reliable, and simulation experiments show a sensitivity to action latency. These results show broad motion-tracking capability on a modest training budget and point to quantitative hardware evaluation as the main next step. Code, policy weights and evaluation tools are publicly available.
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