Event Location: Newell Simon Hall 1507

Abstract: Every planning problem in robotics involves constraints. Whether the robot must avoid collision or joint limits, there are always states that are not permissible. Some constraints are straightforward to satisfy while others can be so stringent that the allowed states are very difficult to identify. What makes constrained planning challenging is that, for many constraints, the planning algorithm does not know what the allowed states are; it must discover these states as it plans. The goal of this thesis is to develop a framework for representing and exploring allowable states in the context of manipulation planning.

Planning for manipulation gives rise to a rich variety of tasks that include constraints on collision-avoidance, torque, balance, closed-chain kinematics, and end-effector pose. While many researchers have developed representations and strategies to plan with a specific constraint, the goal of this thesis is to develop a broad representation of constraints on a robot’s configuration and identify general strategies to manage these constraints during the planning process. Some of the most important constraints in manipulation planning are functions of the pose of the manipulator’s end-effector, so we devote a large part of this thesis to end-effector placement for grasping and transport tasks. We present an efficient approach to generating paths that uses Task Space Regions (TSRs) to specify manipulation tasks which involve end-effector pose goals and/or path constraints. We show how to use TSRs for path planning using the Constrained BiDirectional RRT (CBiRRT) algorithm and describe several extensions of the TSR representation. Among them are methods to plan with object pose uncertainty, find optimal base placements, and handle more complex pose constraints by chaining TSRs together. We also present two automatic algorithms to generate end-effector poses for grasping given hand and object models.

We propose to unify these two algorithms with the TSR representation by converting discrete lists of grasps into TSRs. We also propose to expand our framework to address planning under various forms of uncertainty, combining constraints using logical operators, and exploring methods to incorporate nonholonomic constraints.

Committee:James Kuffner, Co-chair

Siddhartha Srinivasa, Co-chair

Matthew Mason

Thierry Simeon, LAAS-CNRS