The Physical Exploration Challenge: Robots Learning to Discover, Actuate, and Explore Degrees of Freedom in the World

• Applicants: Professor Dr. Oliver Brock; Professor Dr. Marc Toussaint
• Subject Area: Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
• Term: from 2014 to 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 260200664

This project will equip real-world robotic systems with one of themost interesting aspects of intelligence: an internal drive to learn,i.e., the ability to exhibit behavior that maximizes learning progresstowards an objective. Concretely, we will design robotic systems thatphysically explore their environment by pushing, pulling, and twistingseemingly interesting parts, with the goal of learning how todiscover, actuate, and explore degrees of freedom (DoF) in the world.Our approach is informed by state-of-the-art Machine Learning researchon exploration and active learning, especially the concept ofoptimizing policies for expected information gain. Our real-worldexploration scenario, however, raises fundamental issues that have notpreviously been addressed in existing research. Three open questions define the major research areas of this proposal:(1)~How can we formulate a tractable belief over kinematic structuresthat represents the relevant uncertainties over relevant aspects, suchas the existence of DoF, their properties, and their relationships?What methods can derive information-gain driven exploration strategiesfor these representations, given that perception and actions of theagent are subject to uncertainty and are continuously improved fromexperience? (2)~What perception methods are useful for discoveringand identifying DoF, for forming hypotheses about promisinginteraction points, and for segmentating the scene into rigid bodyhypotheses? (3)~How can we parameterize and optimize motion primitivesfor the objective of successful interaction and manipulation ofdegrees of freedom in the context of exploration?The proposed research addresses a fundamental challenge in theintersection of machine learning and robotics. If successful, webelieve that this will make a transitional change in the way roboticsystems behave, in their autonomy of learning, in the way they groundacquired knowledge, and eventually also in the way they will interactwith humans and play their part in a wide range of applications inindustry and the private sector.

• DFG Programme: Priority Programmes
• Subproject of: SPP 1527: Autonomous Learning