59 citations · 114 across the 6 of their papers we have counts for
6 papers
A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation
TRI LBM Team, Jose Barreiros, Andrew Beaulieu +79
Robot manipulation has seen tremendous progress in recent years, with imitation learning policies enabling successful performance of dexterous and hard-to-model tasks. Concurrently…
CodeDiffuser: Attention-Enhanced Diffusion Policy via VLM-Generated Code for Instruction Ambiguity
Guang Yin, Yitong Li, Yixuan Wang +6
Natural language instructions for robotic manipulation tasks often exhibit ambiguity and vagueness. For instance, the instruction "Hang a mug on the mug tree" may involve multiple…
Tracking Partially-Occluded Deformable Objects while Enforcing Geometric Constraints
Yixuan Wang, Dale McConachie, Dmitry Berenson
In order to manipulate a deformable object, such as rope or cloth, in unstructured environments, robots need a way to estimate its current shape. However, tracking the shape of a d…
Learning When to Trust a Dynamics Model for Planning in Reduced State Spaces
Dale McConachie, Thomas Power, Peter Mitrano +1
When the dynamics of a system are difficult to model and/or time-consuming to evaluate, such as in deformable object manipulation tasks, motion planning algorithms struggle to find…
Manipulating Deformable Objects by Interleaving Prediction, Planning, and Control
Dale McConachie, Andrew Dobson, Mengyao Ruan +1
We present a framework for deformable object manipulation that interleaves planning and control, enabling complex manipulation tasks without relying on high-fidelity modeling or si…
Bandit-Based Model Selection for Deformable Object Manipulation
Dale McConachie, Dmitry Berenson
We present a novel approach to deformable object manipulation that does not rely on highly-accurate modeling. The key contribution of this paper is to formulate the task as a Multi…