Event Location: NSH 1305
Bio: Jiaji Zhou is a PhD student in the Robotics Institute of Carnegie Mellon University advised by Professor Matt Mason and Professor Drew Bagnell. His research interests lie in the intersection of manipulation, machine learning and control, with a focus on system identification, stability condition and planning for contact-rich manipulation. Jiaji has won the 2016 ICRA best conference paper award. He has interned in GoogleX self-driving car team and Turi Inc.

Abstract: Understanding the mechanics of manipulation is essential for robots to autonomously interact with the physical world. One of the common manipulation scenarios involves pushing objects in a plane subject to dry friction. We propose a planar friction (force-motion) model that relates an applied robot action to the resultant object motion. The set of generalized friction loads is the 1-sublevel set of a polynomial whose gradient directions correspond to generalized velocities. Additionally, the polynomial is confined to be convex even-degree homogeneous in order to obey the maximum work inequality, symmetry, shape invariance in scale, and fast invertibility. We present a simple and statistically-efficient model identification procedure using a sum-of-squares convex relaxation. Simulation and robotic experiments validate the accuracy and efficiency of our approach. We also show practical applications of our model including stable pushing of objects and free sliding dynamic simulations.