Yeping Wang — accepted AI-conference papers

2026

Contact-Grounded Policy: Dexterous Visuotactile Policy with Generative Contact Grounding

Zhengtong Xu, Yeping Wang, Ben Abbatematteo, Jom Preechayasomboon, Sonny Chan, Nicholas Colonnese +1

RSS 2026poster

Contact-rich dexterous manipulation with multi-finger hands remains an open challenge in robotics because task success depends on multi-point contacts that continuously evolve and are highly sensitive to object geometry, frictional transitions, and slip. Recently, tactile-informed manipulation polic…

Cited by 0 Source ↗Scholar ↗

2026

Safe Whole-Body Loco-Manipulation Via Combined Model and Learning-Based Control

Alexander Schperberg, Yeping Wang, Stefano Di Cairano

ICRA 2026poster

Simultaneous locomotion and manipulation enables robots to interact with their environment beyond the constraints of a fixed base. However, coordinating legged locomotion with arm manipulation, while considering safety and compliance during contact interaction remains challenging. To this end, we pr…

Cited by 0 Source ↗PDF ↗Scholar ↗

2025

Anytime Planning for End-Effector Trajectory Tracking

Yeping Wang, Michael Gleicher

RA-L 2025

End-effector trajectory tracking algorithms find joint motions that drive robot manipulators to track reference trajectories. In practical scenarios, anytime algorithms are preferred for their ability to quickly generate initial motions and continuously refine them over time. In this letter, we pres

Cited by 2 Source ↗code ↗Scholar ↗

2025

Hierarchically Accelerated Coverage Path Planning for Redundant Manipulators

Yeping Wang, Michael Gleicher

ICRA 2025

Many robotic applications, such as sanding, polishing, wiping and sensor scanning, require a manipulator to dexterously cover a surface using its end-effector. In this paper, we provide an efficient and effective coverage path planning approach that leverages a manipulator's redundancy and task tole

Cited by 0 Source ↗code ↗Scholar ↗

2024

IKLink: End-Effector Trajectory Tracking with Minimal Reconfigurations

Yeping Wang, Carter Sifferman, Michael Gleicher

ICRA 2024poster

Many applications require a robot to accurately track reference end-effector trajectories. Certain trajectories may not be tracked as single, continuous paths due to the robot’s kinematic constraints or obstacles elsewhere in the environment. In this situation, it becomes necessary to divide the tra…

Cited by 2 Source ↗code ↗Scholar ↗

2024

Motion Comparator: Visual Comparison of Robot Motions

Yeping Wang, Alexander Peseckis, Zelong Jiang, Michael Gleicher

RA-L 2024

Roboticists compare robot motions for tasks such as parameter tuning, troubleshooting, and deciding between possible motions. However, most existing visualization tools are designed for individual motions and lack the features necessary to facilitate robot motion comparison. In this letter, we utili

Cited by 3 Source ↗code ↗Scholar ↗

2023

Exploiting Task Tolerances in Mimicry-Based Telemanipulation

Yeping Wang, Carter Sifferman, Michael Gleicher

IROS 2023poster

We explore task tolerances, i.e., allowable position or rotation inaccuracy, as an important resource to facilitate smooth and effective telemanipulation. Task tolerances provide a robot flexibility to generate smooth and feasible motions; however, in teleoperation, this flexibility may make the use…

Cited by 3 Source ↗Scholar ↗

2023

RangedIK: An Optimization-based Robot Motion Generation Method for Ranged-Goal Tasks

Yeping Wang, Pragathi Praveena, Daniel Rakita, Michael Gleicher

ICRA 2023poster

Generating feasible robot motions in real-time requires achieving multiple tasks (i.e., kinematic requirements) simultaneously. These tasks can have a specific goal, a range of equally valid goals, or a range of acceptable goals with a preference toward a specific goal. To satisfy multiple and poten…

Cited by 18 Source ↗code ↗Scholar ↗

2023

Unlocking the Performance of Proximity Sensors by Utilizing Transient Histograms

Carter Sifferman, Yeping Wang, Mohit Gupta, Michael Gleicher

RA-L 2023

We provide methods which recover planar scene geometry by utilizing the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">transient histograms</i> captured by a class of close-range time-of-flight (ToF) distance sensor. A transient histogram is a one d

Cited by 9 Source ↗Scholar ↗