49 citations · 194 across the 54 of their papers we have counts for
11 papers · 1 filter
StructFormer: Learning Spatial Structure for Language-Guided Semantic Rearrangement of Novel Objects
Weiyu Liu, Chris Paxton, Tucker Hermans +1
Geometric organization of objects into semantically meaningful arrangements pervades the built world. As such, assistive robots operating in warehouses, offices, and homes would gr…
Toward Learning Context-Dependent Tasks from Demonstration for Tendon-Driven Surgical Robots
Yixuan Huang, Michael Bentley, Tucker Hermans +1
Tendon-driven robots, a type of continuum robot, have the potential to reduce the invasiveness of surgery by enabling access to difficult-to-reach anatomical targets. In the future…
Planning Sensing Sequences for Subsurface 3D Tumor Mapping
Brian Y. Cho, Tucker Hermans, Alan Kuntz
Surgical automation has the potential to enable increased precision and reduce the per-patient workload of overburdened human surgeons. An effective automation system must be able…
Learning Visual Shape Control of Novel 3D Deformable Objects from Partial-View Point Clouds
Bao Thach, Brian Y. Cho, Alan Kuntz +1
If robots could reliably manipulate the shape of 3D deformable objects, they could find applications in fields ranging from home care to warehouse fulfillment to surgical assistanc…
Parallelised Diffeomorphic Sampling-based Motion Planning
Tin Lai, Weiming Zhi, Tucker Hermans +1
We propose Parallelised Diffeomorphic Sampling-based Motion Planning (PDMP). PDMP is a novel parallelised framework that uses bijective and differentiable mappings, or diffeomorphi…
Predicting Stable Configurations for Semantic Placement of Novel Objects
Chris Paxton, Chris Xie, Tucker Hermans +1
Human environments contain numerous objects configured in a variety of arrangements. Our goal is to enable robots to repose previously unseen objects according to learned semantic…
Ergonomically Intelligent Physical Human-Robot Interaction: Postural Estimation, Assessment, and Optimization
Amir Yazdani, Roya Sabbagh Novin, Andrew Merryweather +1
Ergonomics and human comfort are essential concerns in physical human-robot interaction. Common practical methods in the area either fail in estimating the correct posture due to o…
DeformerNet: A Deep Learning Approach to 3D Deformable Object Manipulation
Bao Thach, Alan Kuntz, Tucker Hermans
In this paper, we propose a novel approach to 3D deformable object manipulation leveraging a deep neural network called DeformerNet. Controlling the shape of a 3D object requires a…
DULA: A Differentiable Ergonomics Model for Postural Optimization in Physical HRI
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…
DefGraspSim: Simulation-based grasping of 3D deformable objects
Isabella Huang, Yashraj Narang, Clemens Eppner +4
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…
Optimizing Hospital Room Layout to Reduce the Risk of Patient Falls
Sarvenaz Chaeibakhsh, Roya Sabbagh Novin, Tucker Hermans +2
Despite years of research into patient falls in hospital rooms, falls and related injuries remain a serious concern to patient safety. In this work, we formulate a gradient-free co…