Bi-MoDe: 双方向制御に基づく模倣学習における修飾子条件付きデコーディングによる実行速度と接触強度の調整
Bi-MoDe: Bilateral Control-based Imitation Learning via Modifier-Conditioned Decoding for Modulation of Execution Speed and Contact Intensity
Transformer行動デコーダの各層に制約付き潜在変数を注入し、実行速度や接触強度などの指示に従って模倣学習した動作を調整できるようにした。実機のホワイトボード拭きタスクで有効性を検証した。
著者: Takumi Kobayashi, Masato Kobayashi, Yuki Uranishi
分類: cs.RO
原文アブストラクト
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 https://mertcookimg.github.io/bi-mode/