49 citations · 194 across the 54 of their papers we have counts for
63 papers
Bayesian Continuum Robot Dynamics and State Estimation
James M. Ferguson, Tucker Hermans, Alan Kuntz
Recent factor graph approaches to continuum robot state estimation have been successful for quasi-static applications and spatiotemporal estimation using white-noise kinematic moti…
Differentiable Mesh State Estimation via Factor Graph Inference for Deformable Object Reconstruction
Lidia Al-Zogbi, Fangjie Li, Samuel Tobin +13
Estimating deformable object states remains a fundamental challenge in robotics and simulation. We propose a novel factor graph-based framework for probabilistic mesh state estimat…
Continuum Robot State Estimation with Actuation Uncertainty
James M. Ferguson, Alan Kuntz, Tucker Hermans
Continuum robots are flexible, slender manipulators well suited for confined surgical environments. In these settings, unknown interaction forces and model uncertainty significantl…
The Reality Gap in Robotics: Challenges, Solutions, and Best Practices
Elie Aljalbout, Jiaxu Xing, Angel Romero +9
Machine learning has facilitated significant advancements across various robotics domains, including navigation, locomotion, and manipulation. Many such achievements have been driv…
Do What You Say: Steering Vision-Language-Action Models via Runtime Reasoning-Action Alignment Verification
Yilin Wu, Anqi Li, Tucker Hermans +3
Reasoning Vision Language Action (VLA) models improve robotic instruction-following by generating step-by-step textual plans before low-level actions, an approach inspired by Chain…
First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines
Martin Matak, Mohanraj Devendran Shanthi, Karl Van Wyk +1
Autonomous multi-finger grasping is a fundamental capability in robotic manipulation. Optimization-based approaches show strong performance, but tend to be sensitive to initializat…
Fail2Progress: Learning from Real-World Robot Failures with Stein Variational Inference
Yixuan Huang, Novella Alvina, Mohanraj Devendran Shanthi +1
Skill effect models for long-horizon manipulation tasks are prone to failures in conditions not covered by training data distributions. Therefore, enabling robots to reason about a…
DiffDef: A Diffusion Model for Generating Multimodal Goal Shapes From Demonstrations for Deformable Object Manipulation
Bao Thach, Tanner Watts, Siyeon Kim +6
Deformable object manipulation is a key capability in many robotic applications. A promising paradigm for this problem is shape servoing, which aims to control deformable objects t…
Constrained Stein Variational Gradient Descent for Robot Perception, Planning, and Identification
Griffin Tabor, Tucker Hermans
Many core problems in robotics can be framed as constrained optimization problems. Often on these problems, the robotic system has uncertainty, or it would be advantageous to ident…
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…
DefGoalNet: Contextual Goal Learning from Demonstrations For Deformable Object Manipulation
Bao Thach, Tanner Watts, Shing-Hei Ho +2
Shape servoing, a robotic task dedicated to controlling objects to desired goal shapes, is a promising approach to deformable object manipulation. An issue arises, however, with th…
Out of Sight, Still in Mind: Reasoning and Planning about Unobserved Objects with Video Tracking Enabled Memory Models
Yixuan Huang, Jialin Yuan, Chanho Kim +4
Robots need to have a memory of previously observed, but currently occluded objects to work reliably in realistic environments. We investigate the problem of encoding object-orient…
Latent Space Planning for Multi-Object Manipulation with Environment-Aware Relational Classifiers
Yixuan Huang, Nichols Crawford Taylor, Adam Conkey +2
Objects rarely sit in isolation in everyday human environments. If we want robots to operate and perform tasks in our human environments, they must understand how the objects they…
DeformerNet: Learning Bimanual Manipulation of 3D Deformable Objects
Bao Thach, Brian Y. Cho, Shing-Hei Ho +2
Applications in fields ranging from home care to warehouse fulfillment to surgical assistance require robots to reliably manipulate the shape of 3D deformable objects. Analytic mod…
DefGraspNets: Grasp Planning on 3D Fields with Graph Neural Nets
Isabella Huang, Yashraj Narang, Ruzena Bajcsy +3
Robotic grasping of 3D deformable objects is critical for real-world applications such as food handling and robotic surgery. Unlike rigid and articulated objects, 3D deformable obj…
Planning Visual-Tactile Precision Grasps via Complementary Use of Vision and Touch
Martin Matak, Tucker Hermans
Reliably planning fingertip grasps for multi-fingered hands lies as a key challenge for many tasks including tool use, insertion, and dexterous in-hand manipulation. This task beco…