Bi-MoDe: Bilateral Control-based Imitation Learning via Modifier-Conditioned Decoding for Modulation of Execution Speed and Contact Intensity

Abstract:Bilateral control-based imitation learning captures both position and force information, making it well suited to contact-rich manipulation. However, existing approaches provide limited means for an operator to specify how a learned task should be executed at inference time, such as slowly or quickly, gently or firmly. We propose Bi-MoDe, a modifier-conditioned decoding framework that injects a constrained latent into every layer of the Transformer action decoder via adaLN-Zero, allowing behavioral directives to directly influence action-chunk generation. We evaluate the method on a real-world whiteboard wiping task with combinations of temporal and physical modifiers. Bi-MoDe improves physical directive following over the action-chunking baseline while maintaining comparable temporal control. An ablation further shows that decoder conditioning and latent-space composition interact, and that their combination is important for accurate physical directive following. Additional material is available at the this https URL

Robotics (cs.RO)

arXiv:2609.16040 [cs.RO]

(or arXiv:2609.16040v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.16040 Focus to learn more arXiv-issued DOI via DataCite

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