Search Results for author:
Found 4 papers, 3 papers with code
GenCHiP: Generating Robot Policy Code for High-Precision and Contact-Rich Manipulation Tasks
Large Language Models (LLMs) have been successful at generating robot policy code, but so far these results have been limited to high-level tasks that do not require precise movement.
Distributional Depth-Based Estimation of Object Articulation Models
Our core contributions include a novel representation for distributions over rigid body transformations and articulation model parameters based on screw theory, von Mises-Fisher distributions, and Stiefel manifolds.
ScrewNet: Category-Independent Articulation Model Estimation From Depth Images Using Screw Theory
Robots in human environments will need to interact with a wide variety of articulated objects such as cabinets, drawers, and dishwashers while assisting humans in performing day-to-day tasks.
Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics
This hierarchical planning approach results in a decomposition of the POMDP planning problem into smaller sub-parts that can be solved with significantly lower computational costs.
