Adaptive Control for Pivoting with Visual and Tactile Feedback

Francisco E. Viña B., Yiannis Karayiannidis, Christian Smith and Danica Kragic

2016 IEEE International Conference on Robotics and Automation (ICRA'16). Stockholm, Sweden.


In this work we present an adaptive control approach for pivoting, which is an in-hand manipulation maneuver that consists of rotating a grasped object to a desired orientation relative to the robot's hand. We perform pivoting by means of gravity, allowing the object to rotate between the fingers of a one degree of freedom gripper and controlling the gripping force to ensure that the object follows a reference trajectory and arrives at the desired angular position. We use a visual pose estimation system to track the pose of the object and force measurements from tactile sensors to control the gripping force. The adaptive controller employs an update law that accommodates for errors in the friction coefficient, which is one of the most common sources of uncertainty in manipulation. Our experiments confirm that the proposed adaptive controller successfully pivots a grasped object in the presence of uncertainty in the object's friction parameters.


Adaptive Control for Pivoting with Visual and Tactile Feedback