Exploring Data Driven approaches for Robot Control : Real Time Visual Servoing and Value Function based Inverse Kinematics

Abstract

Data-Driven approaches are becoming increasingly popular for robot control problems. In this thesis, we present two approaches that make use of deep learning frameworks to solve classic robot control problems. In the first part(chapter 3), we propose a lightweight visual servoing MPC framework that generates optimal control near real-time at a frequency of 10.52 Hz. This work utilizes the differential crossentropy sampling method for quick and effective control generation along with a lightweight neural network, significantly improving the servoing frequency. We also propose a flow depth normalization layer that ameliorates the issue of inferior predictions of two view depths from the flow network. We conduct extensive experimentation on the Habitat simulator and show a notable decrease in servoing time compared to other approaches that optimize over a time horizon. We achieve the right balance between time and performance for visual servoing in six degrees of freedom (6DoF), while retaining the advantageous MPC formulation. In the second part(chapter 4), we present a real-time algorithm for computing the optimal sequence and motion trajectories for a fixed-base manipulator to pick and place a set of given objects. The optimality is defined in terms of the total execution time of the sequence or its proxy, the arc length in the joint space. The fundamental complexity stems from the fact that the optimality metric depends on the joint motion, but the task specification is in the end-effector space. Moreover, mapping between a pair of end-effector positions to the shortest arc-length joint trajectory is not analytic; instead, it entails solving a complex trajectory optimization problem. Existing works ignore this complex mapping and use the Euclidean distance in the end-effector space to compute the sequence. In this chapter, we overcome the reliance on the Euclidean distance heuristic by introducing a novel data-driven technique to estimate the optimal arc-length cost in joint space (a.k.a the value function) between two given endeffector positions. We parametrize the value function as a Neural Network and motivate a niche choice for its architecture, inspired by the works on metric learning. The learned value function is then used as an edge cost in a capacitated vehicle routing problem (CVRP) set-up to compute the optimal visitation sequence. Finally, we optimize over the input space of the learned value function network to propose a novel Inverse Kinematics (IK) algorithm that produces substantially shorter joint arc-length trajectories than existing approaches while executing the computed optimal sequence. We show that our sequence planner, in combination with our proposed IK contoller, offers a substantial improvement in joint arc length over existing state-of-the-art while maintaining scalability to a large number of objects