titleConditional Trajectory Peaks: Single-Pass Multimodal Policies over Action Chunks
authorsDi Wu, Rongtian Shen, Ping Liu, Xuhua Chen, He Zheng, Lingfeng Zhang, Tao Zhang
date2026-10-05
venuearXiv (preprint, not peer reviewed)
idarXiv:2610.06104
upvotes2 (Hugging Face Daily Papers, 2026-11-01)
linksabs pdf hf
abstractMultimodal 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.

cite

@misc{wu2026conditional,
  title={Conditional Trajectory Peaks: Single-Pass Multimodal Policies over Action Chunks},
  author={Di Wu and Rongtian Shen and Ping Liu and Xuhua Chen and He Zheng and Lingfeng Zhang and Tao Zhang},
  year={2026},
  eprint={2610.06104},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2610.06104}
}