49 citations · 194 across the 54 of their papers we have counts for
9 papers · 1 filter
23 DoF Grasping Policies from a Raw Point Cloud
Martin Matak, Karl Van Wyk, Tucker Hermans
Coordinating the motion of robots with high degrees of freedom (DoF) to grasp objects gives rise to many challenges. In this paper, we propose a novel imitation learning approach t…
Robust Bayesian Scene Reconstruction with Retrieval-Augmented Priors for Precise Grasping and Planning
Herbert Wright, Weiming Zhi, Martin Matak +2
Constructing 3D representations of object geometry is critical for many robotics tasks, particularly manipulation problems. These representations must be built from potentially noi…
Differentiable GPU-Parallelized Task and Motion Planning
William Shen, Caelan Garrett, Nishanth Kumar +5
Planning long-horizon robot manipulation requires making discrete decisions about which objects to interact with and continuous decisions about how to interact with them. A robot p…
Points2Plans: From Point Clouds to Long-Horizon Plans with Composable Relational Dynamics
Yixuan Huang, Christopher Agia, Jimmy Wu +2
We present Points2Plans, a framework for composable planning with a relational dynamics model that enables robots to solve long-horizon manipulation tasks from partial-view point c…
DextrAH-G: Pixels-to-Action Dexterous Arm-Hand Grasping with Geometric Fabrics
Tyler Ga Wei Lum, Martin Matak, Viktor Makoviychuk +5
A pivotal challenge in robotics is achieving fast, safe, and robust dexterous grasping across a diverse range of objects, an important goal within industrial applications. However,…
Composable Part-Based Manipulation
Weiyu Liu, Jiayuan Mao, Joy Hsu +3
In this paper, we propose composable part-based manipulation (CPM), a novel approach that leverages object-part decomposition and part-part correspondences to improve learning and…
Point Cloud Models Improve Visual Robustness in Robotic Learners
Skand Peri, Iain Lee, Chanho Kim +3
Visual control policies can encounter significant performance degradation when visual conditions like lighting or camera position differ from those seen during training -- often ex…
V-PRISM: Probabilistic Mapping of Unknown Tabletop Scenes
Herbert Wright, Weiming Zhi, Matthew Johnson-Roberson +1
The ability to construct concise scene representations from sensor input is central to the field of robotics. This paper addresses the problem of robustly creating a 3D representat…
Pick and Place Planning is Better than Pick Planning then Place Planning
Mohanraj Devendran Shanthi, Tucker Hermans
Robotic pick and place stands at the heart of autonomous manipulation. When conducted in cluttered or complex environments robots must jointly reason about the selected grasp and d…