Jiaji Zhou
Carnegie Mellon University

Abstract
A high-fidelity and tractable mechanics model of the physical interaction is essential for autonomous robotic manipulation in complex and uncertain environments. Nonetheless, task mechanics are often ignored or nullified in most robotic manipulation systems. This thesis proposal addresses three aspects of harnessing task mechanics: mechanics model learning, uncertainty reduction and control synthesis.

We first study a large class of manipulation problems where surface-to-surface planar sliding motion occurs. An efficiently identifiable convex polynomial force-motion model is proposed. We derive the kinematic contact model that resolves the contact modes and instantaneous object motion given a position controlled manipulator action. This enables generic quasi-static planar contact simulation, which is validated with extensive robotic grasping and pushing experiments. We then generate tree-structured sequential grasping plans, both sensored and sensorless, that will succeed in localizing the post-action object pose to a singleton (subject to symmetry) despite the presence of bounded initial state uncertainty.

We show some preliminary work on the differential flatness property of the pusher-slider system that leads to trajectory planning with Dubins curves and stable tracking with dynamic feedback linearization. Future work focuses on 1) manipulation in the gravity plane with external contacts including the ground and walls; 2) extensions of developed models to contend with clutter.

Thesis Committee
Matthew T. Mason, Co-chair
J. Andrew Bagnell, Co-chair
Christopher G. Atkeson
Russ Tedrake, MIT CSAIL