Yeping Wang    王业平

I am a final-year PhD candidate in Computer Science at the University of Wisconsin-Madison, advised by Michael Gleicher. I am currently focused on developing planning and learning algorithms for both fixed-base and mobile manipulators. I am also interested in human-robot interaction and robot visualization. Previously, I obtained my Master's degree in Robotics from Johns Hopkins University.

In Fall 2025, I am a Research Scientist Intern at Meta Reality Labs. Before that, I worked as an Applied Scientist Intern at Amazon Robotics and as a Research Intern at Mitsubishi Electric Research Laboratories (MERL).

yeping@cs.wisc.edu

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Publications

A motion planner that enables a robotic arm to coverage a surface with its end-effector, ideal for applications such as sanding, polishing, and sensor scanning.

A fast motion planner for guiding a robotic arm’s end-effector along a trajectory, particularly useful for applications such as welding and drawing.

We design and build a web-based tool for roboticists to understand, compare, and share robot motions.

A method for tracking end effector trajectories while taking minimal breaks to reconfigure the arm position.

We directly utilize low-level information generated by optical time-of-flight sensors to recovery of planar geometry and albedo from a single sensor measurement.

A real-time motion generation method that accommodates various types of kinematic requirements within a single, unified framework.

We explore task tolerances, i.e., allowable position or rotation inaccuracy, as an important resource to facilitate smooth and effective telemanipulation.

We design, build, and evaluate Periscope, a robotic camera system that allows two people to collaborate remotely on physical tasks.

We investigate the effects of using multiple view-specific control frames in a multi-camera interface on task performance and user experience during robot telemanipulation.

We explore how first-person demonstrations can be utilized to enable user-centric robotic assistance in human-robot collaborative assembly tasks.