Nancy S. Pollard — accepted AI-conference papers

2026

High Fidelity Capture, Reconstruction, and Transfer of Human Demonstrations for Robot-Assisted Bathing

Arjun Lakshmipathy, Jonathan P. King, Ethan Zuo, Rohit Satishkumar, Hongyi Chen, Jeffrey Ichnowski +3

RSS 2026poster

Despite the demand for robots in high-value clinical tasks like bathing, contemporary systems still lack the safety and reliability required for complex, sustained physical interaction with humans. A key challenge hindering the development of such systems is that collecting, understanding, and effec…

Cited by 0 Source ↗Scholar ↗

2022

Contact Transfer: A Direct, User-Driven Method for Human to Robot Transfer of Grasps and Manipulations

Arjun Lakshmipathy, Dominik Bauer, Cornelia Bauer, Nancy S. Pollard

ICRA 2022poster

We present a novel method for the direct transfer of grasps and manipulations between objects and hands through utilization of contact areas. Our method fully preserves contact shapes, and in contrast to existing techniques, is not dependent on grasp families, requires no model training or grasp sam…

Cited by 13 Source ↗Scholar ↗

2021

Contact Tracing: A Low Cost Reconstruction Framework for Surface Contact Interpolation

Arjun Lakshmipathy, Dominik Bauer, Nancy S. Pollard

IROS 2021poster

We present a novel, low cost framework for reconstructing surface contact movements during in-hand manipulations. Unlike many existing methods focused on hand pose tracking, ours models the behavior of contact patches, and by doing so is the first to obtain detailed contact tracking estimates for mu…

Cited by 5 Source ↗Scholar ↗

2017

A Framework for Optimal Grasp Contact Planning

Kaiyu Hang, Johannes A. Stork, Nancy S. Pollard, Danica Kragic

RA-L 2017

We consider the problem of finding grasp contacts that are optimal under a given grasp quality function on arbitrary objects. Our approach formulates a framework for contact-level grasping as a path finding problem in the space of supercontact grasps. The initial supercontact grasp contains all gras

Cited by 36 Source ↗Scholar ↗

2017

The manifold particle filter for state estimation on high-dimensional implicit manifolds

Michael C. Koval, Matthew Klingensmith, Siddhartha S. Srinivasa, Nancy S. Pollard, Michael Kaess

ICRA 2017poster

We estimate the state of a noisy robot arm and underactuated hand using an implicit Manifold Particle Filter (MPF) informed by contact sensors. As the robot touches the world, its state space collapses to a contact manifold that we represent implicitly using a signed distance field. This allows us t…

Cited by 30 Source ↗Scholar ↗

2015

Robust trajectory selection for rearrangement planning as a multi-armed bandit problem

Michael C. Koval, Jennifer E. King, Nancy S. Pollard, Siddhartha S. Srinivasa

IROS 2015poster

We present an algorithm for generating open-loop trajectories that solve the problem of rearrangement planning under uncertainty. We frame this as a selection problem where the goal is to choose the most robust trajectory from a finite set of candidates. We generate each candidate using a kinodynami…

Cited by 51 Source ↗Scholar ↗