Self-Supervised Learning for Precise Pick-and-Place Without Object Model

Flexible pick-and-place is a fundamental yet challenging task within robotics, in particular due to the need of an object model for a simple target pose definition. In this work, the robot instead learns to pick-and-place objects using planar manipulation according to a single, demonstrated goal state. Our primary contribution lies within combining robot learning of primitives, commonly estimated by fully-convolutional neural networks, with one-shot imitation learning. Therefore, we define the place reward as a contrastive loss between real-world measurements and a task-specific noise distribution. Furthermore, we design our system to learn in a self-supervised manner, enabling real-world experiments with up to 25 000 pick-and-place actions. Then, our robot is able to place trained objects with an average placement error of (2.7 +/- 0.2) mm and (2.6 +/- 0.8)degrees. As our approach does not require an object model, the robot is able to generalize to unknown objects while keeping a precision of (5.9 +/- 1.1) mm and (4.1 +/- 1.2)degrees. We further show a range of emerging behaviors: The robot naturally learns to select the correct object in the presence of multiple object types, precisely inserts objects within a peg game, picks screws out of dense clutter, and infers multiple pick-and-place actions from a single goal state.