Fred Shentu — accepted AI-conference papers

2026

SARM: Stage-Aware Reward Modeling for Long Horizon Robot Manipulation

Qianzhong Chen, Justin Yu, Mac Schwager, Pieter Abbeel, Fred Shentu, Philipp Wu

ICLR 2026poster

Large-scale robot learning has made progress on complex manipulation tasks, yet long-horizon, contact-rich problems—especially those involving deformable objects—remain challenging due to inconsistent demonstration quality. We propose a stage-aware, video-based reward modeling framework that jointly…

Cited by 0 Source ↗code ↗Scholar ↗

2017

Probabilistically safe policy transfer

David Held, Zoe McCarthy, Michael Zhang, Fred Shentu, Pieter Abbeel

ICRA 2017poster

Although learning-based methods have great potential for robotics, one concern is that a robot that updates its parameters might cause large amounts of damage before it learns the optimal policy. We formalize the idea of safe learning in a probabilistic sense by defining an optimization problem: we…

Cited by 28 Source ↗Scholar ↗