4 accepted papers
Inverse kinematics—finding joint poses that reach a given Cartesian-space end-effector pose—is a fundamental operation in robotics, since goals and waypoints are typically defined in Cartesian space, but robots must be controlled in joint space. However, existing inverse kinematics solvers return a
We present a feedback motion planning algorithm, Bounded-Error LQR-Trees, that leverages reinforcement learning theory to find a policy with a bounded amount of error. The algorithm composes locally valid linear-quadratic regulators (LQR) into a nonlinear controller, similar to how LQR-Trees constru…
A critical capability required for generally intelligent robot behavior is the ability to sequence motor skills to reach a goal. This requires a (typically abstract) representation that supports goal-directed planning, which raises the question of how to construct such a representation. Previous wor…
Despite increasing popularity of compliant and underactuated hands, few tools are available for modeling them. Thus, we propose a simulation technique to predict the success of a compliant gripper grasping irregular objects, which could be used in mechanism design as well as grasp planning. The simu