TacPAC: 接触リッチ操作のためのワールドアクションモデルにおける触覚予測とリアルタイム行動修正
TacPAC: Tactile Prediction and Real-Time Action Correction in World-Action Models for Contact-Rich Manipulation
視覚中心のワールドアクションモデルに触覚予測を組み込む際、予測と実行のタイミング差が問題となる。TacPACは、計画された行動チャンクに基づく触覚予測をキャッシュし、実行中に得られる実触覚画像と照合して未実行の行動をリアルタイムに修正する手法を提案し、5つの実ロボットタスクで成功率を大幅に向上させた。
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2. 先行研究と比べてどこがすごい？
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著者: Zipei Ma, Xiaofei Wei, Junzhe Jiang, Shunlin Lu, Li Zhang
分類: cs.RO
原文アブストラクト
World-action models guide action generation with predicted future observations, but vision-centric predictions miss the local contact cues that decide contact-rich manipulation. However, naively predicting future tactile observations as additional views recovers only a third of the achievable gain in our experiments. This gap reflects a timing mismatch: predictions precede execution, while tactile feedback arrives during it. We introduce TacPAC, which turns tactile prediction into real-time action correction. Once the base model has planned an action chunk, TacPAC caches the predicted contact that plan was conditioned on together with the plan's own representation, and a tactile expert reads each newly observed tactile image against that cache to correct the actions not yet executed. Feedback is thus interpreted against what the plan anticipated rather than in isolation, and one correction is a single pass over that cache, $20.7\times$ cheaper than regenerating the chunk. On five real-robot tasks spanning precision insertion, fragile-object handling, object reorientation, and long-horizon manipulation, TacPAC leads every task and raises the average from 22% for its vision-only base model to 64%. Code is available at https://github.com/LogosRoboticsGroup/TacPAC.