条件付き軌道ピーク：アクションチャンク上のシングルパス多峰性ポリシー
Conditional Trajectory Peaks: Single-Pass Multimodal Policies over Action Chunks
同じ観測下で多様な行動を生成しつつ再計画時の一貫性も保つ、シングルパスで動作する模倣学習ポリシーを提案。実機双腕タスクで成功率を維持しつつ推論遅延を約3分の1に短縮した。
著者: Di Wu, Rongtian Shen, Ping Liu, Xuhua Chen, He Zheng, Lingfeng Zhang, Tao Zhang
分類: cs.RO
原文アブストラクト
Multimodal imitation learning requires diverse executable futures under the same observation and consistent behavior across replanning cycles. We present Conditional Trajectory Peaks (CTP), a single-pass policy framework that jointly predicts complete action-chunk candidates, probability masses, and trajectory scales. Distribution-Aware Peak Specialization (DAPS) specializes trajectory peaks using trajectory-level posterior responsibilities and mass- and scale-modulated overlap constraints. Evidence-Gated Trajectory Belief Transport (ETBT) maintains cross-chunk consistency through geometric correspondence between exchangeable candidate sets, while allowing current policy evidence to override historical constraints. CTP achieves a coverage score of 91.40% on Push-T; success rates of 100.0%, 79.72%, and 84.44% on D3IL Avoiding, Aligning, and Sorting-2, respectively. On LIBERO, CTP achieves an average success rate of 97.25%. In real-world dual-arm experiments, CTP preserves both placement modes in a two-plate task, succeeding in all 50 trials. On bottle uprighting and pen placement into a holder, it maintains success rates comparable to $π_{0.5}$ while reducing policy inference latency from 218.24 ms to 75.80 ms. These results demonstrate that single-pass trajectory modeling can combine multimodal behavior, closed-loop consistency, and efficient inference.