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AFFORD2ACT: How Far Can Sparse Keypoints Go for Robot Manipulation?
Anukriti Singh, Kasra Torshizi, Khuzema Habib +3
Vision-based manipulation policies are commonly learned from rich visual observations, typically using dense visual features extracted from full resolution images. It remains uncle…
VARP: Reinforcement Learning from Vision-Language Model Feedback with Agent Regularized Preferences
Anukriti Singh, Amisha Bhaskar, Peihong Yu +4
Designing reward functions for continuous-control robotics often leads to subtle misalignments or reward hacking, especially in complex tasks. Preference-based RL mitigates some of…
Sketch-to-Skill: Bootstrapping Robot Learning with Human Drawn Trajectory Sketches
Peihong Yu, Amisha Bhaskar, Anukriti Singh +2
Training robotic manipulation policies traditionally requires numerous demonstrations and/or environmental rollouts. While recent Imitation Learning (IL) and Reinforcement Learning…