Robot learning combines the challenges of understanding, modeling and applying dynamical systems with task learning from rewards, through human robot interaction or from intrinsic motivation signals. While outstanding results using machine and deep learning have been generated in robot learning in the last years, current challenges in industrial applications are underrepresented. The goal of this workshop is to go beyond discussing potential industrial applications like in related past workshops.

These topics were discussed with Pieter Abbeel, Dileep George, Sergey Levine, Jan Peters, Freek Stulp, Marc Toussaint, Patrick van der Smagt, and Georg von Wichert.

Open-ended learning aims to build learning machines and robots that are able to acquire skills and knowledge in an incremental fashion in a certain environment. This competition addresses autonomous open-ended learning with a focus on simulated robot systems that: (a) acquire a sensorimotor competence that allows them to interact with objects and physical environments; (b) learn in a fully autonomous way, i.e. with no human intervention (e.g., no tasks or reward functions) on the basis of mechanisms such as curiosity, intrinsic motivations, task-free reinforcement learning, self-generated goals, and any other mechanism that might support autonomous learning. The competition challenge will feature two phases: during an initial “”intrinsic phase”” the system will have a certain time to freely explore and learn in an environment containing multiple objects, and then during an “”extrinsic phase”” the quality of the autonomously acquired knowledge will be measured with tasks unknown at design time and during the intrinsic phase.

Modern robots are complex machines with many compliant actuators and various types of sensors including depth and vision cameras, tactile electrodes and dozens of proprioceptive sensors. The obvious challenges are to process these high dimensional input patterns, memorize low dimensional representations of them and to generate the desired motor commands to interact in dynamically changing environments. Similar challenges exist in brain machine interfaces (BMIs) where complex prostheses with perceptional feedback are controlled, or in motor neuroscience where in addition cognitive features need to be considered. Despite this broad research overlap the developments happened mainly in parallel and were not ported or exploited in the related domains. The main bottleneck for collaborative studies has been a lack of interaction between the core robotics, the machine learning and the neuroscience communities.