arXiv AI By He Zhang, Ying Sun, Pengteng Li, Ziyang Chen, Yiren Zhao, Ziyang Rao, Weiyu Guo, Yandong Guo, Hui Xiong

Source-Lifted Flow Matching for Intervenable Multimodal Imitation

Read the original on arXiv AI →

arXiv:2607. 10206v1 Announce Type: cross Abstract: Flow-matching policies are promising for imitation learning because they model complex multimodal action distributions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning
Jun 8

SERNF: Sample-Efficient Real-World Dexterous Policy Fine-Tuning via Action-Chunked Critics and Normalizing Flows

arXiv:2602. 09580v4 Announce Type: replace-cross Abstract: Real-world fine-tuning of dexterous manipulation policies remains challenging due to limited real-world interaction budgets and highly multimodal action distributions.

By Chenyu Yang, Denis Tarasov, Davide Liconti, Romain Guntz, Hehui Zheng, Robert K. Katzschmann
arXiv AI
3d ago

Understanding Multimodality in Generative Behavioral Cloning

The paper investigates how generative behavioral-cloning policies handle multimodal expert behavior, identifying bottlenecks in both latent-variable and action-space policy designs. For latent-variable policies, preserving demonstrated modes depends on action-conditioned latent representations, and excessive posterior-prior regularization can suppress this information. In action-space generative policies, multimodality is limited by the smoothness of the base-to-action transport, requiring either sharp transitions or off-support bridge regions to capture many well-separated modes. Experiments on synthetic navigation and a physical-robot manipulation task confirm these findings, while standard robotic simulation benchmarks show limited conditional multimodality, making deterministic regression competitive.

By Lorenzo Mazza, Massimiliano Datres, Ariel Rodriguez, Sebastian Bodenstedt, Gitta Kutyniok, Stefanie Speidel