arXiv Machine Learning
Jun 11

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies

arXiv:2605. 03065v2 Announce Type: replace Abstract: Generative control policies (GCPs), such as diffusion- and flow-based control policies, have emerged as effective parameterizations for robot learning.

By Sarvesh Patil, Mitsuhiko Nakamoto, Manan Agarwal, Shashwat Saxena, Jesse Zhang, Giri Anantharaman, Cleah Winston, Chaoyi Pan, Douglas Chen, Nai-Chieh Huang, Zeynep Temel, Oliver Kroemer, Sergey Levine, Abhishek Gupta, Hongkai Dai, Paarth Shah, Max Simchowitz
arXiv AI
3d ago

PrefPI: Preference-Guided Steering into Out-of-Distribution Behaviors

PrefPI (Preference-Guided Policy Iteration) is an iterative framework that steers pretrained generative robot policies using only relative preferences over self-generated trajectories. It treats preference learning as preference-conditioned generative modeling, where preferred trajectories define a conditional distribution whose density ratio with the broader behavior prior yields an implicit preference signal amplified by classifier-free guidance (CFG). By repeatedly applying this preference-conditioned modeling and guidance, PrefPI iteratively improves policies, enabling access to behaviors that were rarely or never observed under the initial policy, and achieves significant behavioral shifts such as increasing object transport height from 10.7 cm to 19.8 cm on real hardware with only 150 preference-labeled trajectories.

By Seungeun Rho, Wontaek Kim, Danfei Xu, Sehoon Ha
Hugging Face Trending Papers
Aug 20

RoMAN-Flow: Taming Autoregressive Normalizing Flows for Offline Reinforcement Learning in Robotic Manipulation

Offline reinforcement learning improves robotic policies using previously collected data without further environment interaction. Yet prevalent diffusion- and flow-matching robot policies lack tractable likelihoods, limiting their use in likelihood-based offline RL post-training.