Generative Action-Chunk Sampling for Adaptive Stiffness Control in Physical Human-Robot Collaboration
Physical human-robot collaboration requires a robot to provide assistance when human intention is clear while remaining compliant when several future motions are plausible. We present an adaptive stiffness framework based on generative action-chunk sampling. Conditioned on an RGB image and external joint-torque estimates, the policy samples multiple latent variables from an observation-conditioned prior and decodes them into future action chunks. Variation among the sampled action chunks is used to continuously adapt joint stiffness and damping. Greater variation makes the robot more compliant to facilitate human guidance, whereas lower variation provides firmer assistance. In a real-worl...
| Authors | Aoi Otake, Ferdinand Hartmann, Ko Igari et al. |
|---|---|
| Primary category | cs.RO |
| Published | 26 August 2026 |
| Last updated at source | 7 September 2026 |
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