19 citations · 19 across the 7 of their papers we have counts for
7 papers
Action Chunk Scheduling for Batched Robot Policy Serving
Rohan Bansal, David He, Nadun Ranawaka Arachchige +4
Deploying robot foundation models at scale is the next step towards realizing the potential of general-purpose robots. However, Vision-Language-Action (VLA) and other foundation mo…
Static In, Dynamic Out: Counterfactual Action Augmentation for Moving Object Manipulation
Woo Chul Shin, Zhenyang Chen, Alfred Cueva +5
Visuomotor policies have advanced on manipulation tasks where the target object stays static during execution, but real deployments break this assumption: parts drift on conveyors…
Joint Model-based Model-free Diffusion for Planning with Constraints
Wonsuhk Jung, Utkarsh A. Mishra, Nadun Ranawaka Arachchige +3
Model-free diffusion planners have shown great promise for robot motion planning, but practical robotic systems often require combining them with model-based optimization modules t…
What Matters in Learning from Large-Scale Datasets for Robot Manipulation
Vaibhav Saxena, Matthew Bronars, Nadun Ranawaka Arachchige +5
Imitation learning from large multi-task demonstration datasets has emerged as a promising path for building generally-capable robots. As a result, 1000s of hours have been spent o…
SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies
Nadun Ranawaka Arachchige, Zhenyang Chen, Wonsuhk Jung +8
Offline Imitation Learning (IL) methods such as Behavior Cloning are effective at acquiring complex robotic manipulation skills. However, existing IL-trained policies are confined…
RAIL: Reachability-Aided Imitation Learning for Safe Policy Execution
Wonsuhk Jung, Dennis Anthony, Utkarsh A. Mishra +4
Imitation learning (IL) has shown great success in learning complex robot manipulation tasks. However, there remains a need for practical safety methods to justify widespread deplo…
The Utility of Explainable AI in Ad Hoc Human-Machine Teaming
Rohan Paleja, Muyleng Ghuy, Nadun Ranawaka Arachchige +2
Recent advances in machine learning have led to growing interest in Explainable AI (xAI) to enable humans to gain insight into the decision-making of machine learning models. Despi…