Mukunda Bharatheesha

TL;DR: The robot’s software uses ROS to integrate off-the-shelf components and modules developed specifically for the competition, implementing Deep Learning and other AI techniques for object recognition and pose estimation, grasp planning and motion planning.

TL;DR: This paper proposes automation levels based on the usage of information at design or runtime to drive the robot's behavior, and uses them to discuss Team Delft's design solution and the lessons learned from this robot development experience.

TL;DR: The winning team of Team Delft as mentioned in this paper used an industrial robot arm, 3D cameras and a customized gripper for picking and placing items in an Amazon warehouse, which won both the picking and the stowing tasks.

TL;DR: This work uses the Iterative Linear Quadratic Regulator approach for estimating an approximation to the optimal cost and learns this cost using Locally Weighted Projection Regression and shows that the learnt function approximates the original cost with a reasonable tolerance and gives a tremendous speed up of a factor of 1000 over the actual computation time.

TL;DR: In this article, the authors proposed to use indirect optimal control instead of direct optimal control to reduce the computational effort to generate the data and provide a low-dimensional parametrization of the action space.