A system may include a controller configured to determine a user interface status, wherein the user interface status includes user interaction of a user interface. The controller may also be configured to determine a drive mechanism location relative to a first limit and a second limit and select a clutching location based on the user interface status and drive mechanism location.

A system may include a controller configured to determine a user interface status, wherein the user interface status includes user interaction of a user interface. The controller may also be configured to determine a drive mechanism location relative to a first limit and a second limit and select a clutching location based on the user interface status and drive mechanism location.

A system may include a controller configured to determine a user interface status, wherein the user interface status includes user interaction of a user interface. The controller may also be configured to determine a drive mechanism location relative to a first limit and a second limit and select a clutching location based on the user interface status and drive mechanism location.

A system may include a controller configured to determine a user interface status, wherein the user interface status includes user interaction of a user interface. The controller may also be configured to determine a drive mechanism location relative to a first limit and a second limit and select a clutching location based on the user interface status and drive mechanism location.

A system may include a controller configured to determine a user interface status, wherein the user interface status includes user interaction of a user interface. The controller may also be configured to determine a drive mechanism location relative to a first limit and a second limit and select a clutching location based on the user interface status and drive mechanism location.

A system for generating a model-free reinforcement learning policy for a robotic hand for grasping an object is provided, including a processor; a memory; and a simulator implemented via the processor and the memory, performing: sampling a plurality of stable grasps relevant to reorienting the grasped object about a desired axis of rotation and using stable grasps as initial states for collecting training trajectories; learning finger-gaiting and finger-grasping policies for each axis of rotation in the hand coordinate frame based on proprioceptive sensing in the robotic hand, wherein the finger-gaiting and finger-pivoting policy is implemented on the robotic hand.