arXiv：cs.AI· Brent Yi, Hongsuk Choi, Himanshu Gaurav Singh, Xiaoyu Huang, Takara E. Truong, Carmelo Sferrazza, Yi Ma, Rocky Duan, Pieter Abbeel, Guanya Shi, Karen Liu, Angjoo Kanazawa·· 5 小时前
Flow Policy Gradients 用于机器人控制
Flow Policy Gradients for Robot Control
AI 导读
研究提出 flow matching policy gradients 的改进目标，绕过可微动作似然计算，在足式运动、人形动作跟踪和操作任务上取得成效，并在两台人形机器人上实现稳健的 sim-to-real 迁移。消融实验显示，策略可从零训练时利用 flow 表示进行探索，相比基线微调鲁棒性更好。
正文
Authors:Brent Yi, Hongsuk Choi, Himanshu Gaurav Singh, Xiaoyu Huang, Takara E. Truong, Carmelo Sferrazza, Yi Ma, Rocky Duan, Pieter Abbeel, Guanya Shi, Karen Liu, Angjoo Kanazawa
Abstract:Likelihood-based policy gradient methods are the dominant approach for training robot control policies from rewards. These methods rely on differentiable action likelihoods, which constrain policy outputs to simple distributions like Gaussians. In this work, we show how flow matching policy gradients, a recent framework that bypasses likelihood computation, can be made effective for training and finetuning more expressive policies in challenging robot control settings. We introduce an improved objective that enables success in legged locomotion, humanoid motion tracking, and manipulation tasks, as well as robust sim-to-real transfer on two humanoid robots. We then present ablations and analysis on training dynamics. Results show how policies can exploit the flow representation for exploration when training from scratch, as well as improved finetuning robustness over baselines.
| Comments: | Project webpage: this https URL |
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2602.02481 [cs.RO] |
| (or arXiv:2602.02481v2 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2602.02481 arXiv-issued DOI via DataCite |
Submission history
From: Hongsuk Choi [view email]
[v1]
Mon, 2 Feb 2026 18:56:49 UTC (10,944 KB)
[v2]
Tue, 13 Oct 2026 08:31:31 UTC (16,799 KB)
来源：arXiv：cs.AI · arxiv.org