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
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
arXiv:2511. 08583v2 Announce Type: replace-cross Abstract: Developing efficient and accurate visuomotor policies poses a central challenge in robotic imitation learning.
By Rong Xue, Jiageng Mao, Mingtong Zhang, Yue Wang
arXiv:2607. 27138v1 Announce Type: cross Abstract: Vision-language-action (VLA) models remain constrained by scarce action-labeled robot data, whereas action-free videos offer abundant observations of physical change.
By Zuojin Tang, Feifan Luo, Haoyun Liu, Botai Yuan, Dekang Qi, Ronghan Chen, Yandan Yang, Tong Lin, Xinyuan Chang, Mu Xu, Bin Liu, De Ma, Zhiheng Ma
arXiv:2604.16683v2 Announce Type: replace-cross
Abstract: Imitation learning has enabled robots to acquire complex visuomotor manipulation skills from demonstrations, but deployment failures remain a...
By Gehan Zheng, Sanjay Seenivasan, Matthew Johnson-Roberson, Weiming Zhi
arXiv:2609.15883v1 Announce Type: cross
Abstract: Offline reinforcement learning aims to learn a policy solely from fixed datasets, which often contain multimodal action distributions. Flow policies...
By Jaehun Shon, Jinha Choi, Jongwook Jeon, Jongmin Lee