49 citations · 194 across the 54 of their papers we have counts for
8 papers · 1 filter
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…
StructDiffusion: Language-Guided Creation of Physically-Valid Structures using Unseen Objects
Weiyu Liu, Yilun Du, Tucker Hermans +2
Robots operating in human environments must be able to rearrange objects into semantically-meaningful configurations, even if these objects are previously unseen. In this work, we…
Planning for Multi-Object Manipulation with Graph Neural Network Relational Classifiers
Yixuan Huang, Adam Conkey, Tucker Hermans
Objects rarely sit in isolation in human environments. As such, we'd like our robots to reason about how multiple objects relate to one another and how those relations may change a…
Occlusion-Robust Multi-Sensory Posture Estimation in Physical Human-Robot Interaction
Amir Yazdani, Roya Sabbagh Novin, Andrew Merryweather +1
3D posture estimation is important in analyzing and improving ergonomics in physical human-robot interaction and reducing the risk of musculoskeletal disorders. Vision-based postur…
DULA and DEBA: Differentiable Ergonomic Risk Models for Postural Assessment and Optimization in Ergonomically Intelligent pHRI
Amir Yazdani, Roya Sabbagh Novin, Andrew Merryweather +1
Ergonomics and human comfort are essential concerns in physical human-robot interaction applications. Defining an accurate and easy-to-use ergonomic assessment model stands as an i…
Correcting Robot Plans with Natural Language Feedback
Pratyusha Sharma, Balakumar Sundaralingam, Valts Blukis +5
When humans design cost or goal specifications for robots, they often produce specifications that are ambiguous, underspecified, or beyond planners' ability to solve. In these case…
DefGraspSim: Physics-based simulation of grasp outcomes for 3D deformable objects
Isabella Huang, Yashraj Narang, Clemens Eppner +5
Robotic grasping of 3D deformable objects (e.g., fruits/vegetables, internal organs, bottles/boxes) is critical for real-world applications such as food processing, robotic surgery…
L4KDE: Learning for KinoDynamic Tree Expansion
Tin Lai, Weiming Zhi, Tucker Hermans +1
We present the Learning for KinoDynamic Tree Expansion (L4KDE) method for kinodynamic planning. Tree-based planning approaches, such as rapidly exploring random tree (RRT), are the…