Being able to manipulate degrees of freedom of the environment, such as doors or drawers, is a requirement for most tasks a robot is supposed to perform. Often these external degrees of freedom depend on other ones, e.g., a drawer can only be opened if the lock is not locking the joint. We propose an approach to autonomously and efficiently explore and uncover joint dependency structures. We develop a probabilistic model for joint dependency structures which is the basis for active learning. Discontinuities in the dynamics of the joint, which often indicate key points of the joint, are used to segment the joint space into meaningful segments which then allows efficient exploration with the developed maximum cross-entropy (MaxCE) exploration strategy. Experiments in a simulated environment and on a real PR2 suggest that the proposed approach yields efficient exploration of joint dependency structures.

Physical exploration refers to the challenge of autonomously discovering and learning how to manipulate the environment's degrees of freedom (DOF)-by identifying promising points of interaction and pushing or pulling object parts to reveal DOF and their properties. Recent existing work focused on sub-problems like estimating DOF parameters from given data. Here, we address the integrated problem, focusing on the higher-level strategy to iteratively decide on the next exploration point before applying motion generation methods to execute the explorative action and data analysis methods to interpret the feedback. We propose to decide on exploration points based on the expected information gain, or change in entropy in the robot's current belief (uncertain knowledge) about the DOF. To this end, we first define how we represent such a belief. This requires dealing with the fact that the robot initially does not know which random variables (which DOF, and depending on their type, which DOF properties) actually exist. We then propose methods to estimate the expected information gain for an exploratory action. We analyze these strategies in simple environments and evaluate them in combination with full motion planning and data analysis in a physical simulation environment.