Search Results for author:
Found 10 papers, 5 papers with code
JUICER: Data-Efficient Imitation Learning for Robotic Assembly
While learning from demonstrations is powerful for acquiring visuomotor policies, high-performance imitation without large demonstration datasets remains challenging for tasks requiring precise, long-horizon manipulation.
Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation
To learn performant, robust policies without the burden of unsafe real-world data collection or extensive human supervision, we propose RialTo, a system for robustifying real-world imitation learning policies via reinforcement learning in "digital twin" simulation environments constructed on the fly from small amounts of real-world data.
Lifelong Robot Learning with Human Assisted Language Planners
Large Language Models (LLMs) have been shown to act like planners that can decompose high-level instructions into a sequence of executable instructions.
Shelving, Stacking, Hanging: Relational Pose Diffusion for Multi-modal Rearrangement
We propose a system for rearranging objects in a scene to achieve a desired object-scene placing relationship, such as a book inserted in an open slot of a bookshelf.
Local Neural Descriptor Fields: Locally Conditioned Object Representations for Manipulation
A robot operating in a household environment will see a wide range of unique and unfamiliar objects.
SE(3)-Equivariant Relational Rearrangement with Neural Descriptor Fields
This formalism is implemented in three steps: assigning a consistent local coordinate frame to the task-relevant object parts, determining the location and orientation of this coordinate frame on unseen object instances, and executing an action that brings these frames into the desired alignment.
Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation
Our performance generalizes across both object instances and 6-DoF object poses, and significantly outperforms a recent baseline that relies on 2D descriptors.
A Long Horizon Planning Framework for Manipulating Rigid Pointcloud Objects
We present a framework for solving long-horizon planning problems involving manipulation of rigid objects that operates directly from a point-cloud observation, i. e. without prior object models.
Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners
We validate MPNet against gold-standard and state-of-the-art planning methods in a variety of problems from 2D to 7D robot configuration spaces in challenging and cluttered environments, with results showing significant and consistently stronger performance metrics, and motivating neural planning in general as a modern strategy for solving motion planning problems efficiently.
Motion Planning Networks
Fast and efficient motion planning algorithms are crucial for many state-of-the-art robotics applications such as self-driving cars.
