Learning Real-time Closed Loop Robotic Reaching from Monocular Vision by Exploiting A Control Lyapunov Function Structure
Visual reaching and grasping is a fundamental problem in robotics research. This paper proposes a novel approach based on deep learning a control Lyapunov function and its derivatives by encouraging a differential constraint in addition to vanilla regression that directly regresses independent joint control inputs. A key advantage of the proposed approach is that an estimate of the value of the control Lyapunov function is available in real-time that can be used to monitor the system performance and provide a level of assurance concerning progress towards the goal. The results we obtain demonstrate that the proposed approach is more robust and more reliable than vanilla regression.