5 papers
Counterfactual Transport Flows for Offline Conservative Trajectory Refinement
Lena Krieger, Xuan Zhao, Zhuo Cao +3
Offline reinforcement learning (RL) offers a path to policy improvement from logged data alone, using historical returns or other measurable outcomes as world feedback. A key diffi…
How VLAs (Really) Work In Open-World Environments
Amir Rasouli, Yangzheng Wu, Zhiyuan Li +4
Vision-language-action models (VLAs) have been extensively used in robotics applications, achieving great success in various manipulation problems. More recently, VLAs have been us…
Distracted Robot: How Visual Clutter Undermine Robotic Manipulation
Amir Rasouli, Montgomery Alban, Sajjad Pakdamansavoji +4
In this work, we propose an evaluation protocol for examining the performance of robotic manipulation policies in cluttered scenes. Contrary to prior works, we approach evaluation…
CAPE: Context-Aware Diffusion Policy Via Proximal Mode Expansion for Collision Avoidance
Rui Heng Yang, Xuan Zhao, Leo Maxime Brunswic +5
In robotics, diffusion models can capture multi-modal trajectories from demonstrations, making them a transformative approach in imitation learning. However, achieving optimal perf…
Two-Steps Diffusion Policy for Robotic Manipulation via Genetic Denoising
Mateo Clemente, Leo Brunswic, Rui Heng Yang +5
Diffusion models, such as diffusion policy, have achieved state-of-the-art results in robotic manipulation by imitating expert demonstrations. While diffusion models were originall…