Deploying robots for service tasks requires learning algorithms that scale to the combinatorial complexity of our daily environment. Inspired by the way humans decompose complex tasks, hierarchical methods for robot learning have attracted significant interest. In this paper, we apply the MAXQ method for hierarchical reinforcement learning to continuous state spaces. By using Gaussian Process Regression for MAXQ value function decomposition, we obtain probabilistic estimates of primitive and completion values for every subtask within the MAXQ hierarchy. From these, we recursively compute probabilistic estimates of state-action values. Based on the expected deviation of these estimates, we devise a Bayesian exploration strategy that balances optimization of expected values and risk from exploring unknown actions. To further reduce risk and to accelerate learning, we complement MAXQ with learning from demonstrations in an interactive way. In every situation and subtask, the system may ask for a demonstration if there is not enough knowledge available to determine a safe action for exploration. We demonstrate the ability of the proposed system to efficiently learn solutions to complex tasks on a box stacking scenario.

Deploying robots to our day-to-day life requires them to have the ability to learn from their environment in order to acquire new task knowledge and to flexibly adapt existing skills to various situations. For typical real-world tasks, it is not sufficient to endow robots with a set of primitive actions. Rather, they need to learn how to sequence these in order to achieve a desired effect on their environment. In this paper, we propose an intuitive learning method for a robot to acquire sequences of motions by combining learning from human demonstrations and reinforcement learning. In every situation, our approach treats both ways of learning as alternative control flows to optimally exploit their strengths without inheriting their shortcomings. Using a Gaussian Process approximation of the state-action sequence value function, our approach generalizes values observed from demonstrated and autonomously generated action sequences to unknown inputs. This approximation is based on a kernel we designed to account for different representations of tasks and action sequences as well as inputs of variable length. From the expected deviation of value estimates, we devise a greedy exploration policy following a Bayesian optimization criterion that quickly converges learning to promising action sequences while protecting the robot from sequences with unpredictable outcome. We demonstrate the ability of our approach to efficiently learn appropriate action sequences in various situations on a manipulation task involving stacked boxes.

The ability to recognize human actions is a fundamental problem in many areas of robotics research concerned with human-robot interaction or learning from human demonstration. In this paper, we present a new integrated approach to identifying and recognizing actions in human movement sequences and their reproduction in unknown situations. We propose a set of task-space features to construct probabilistic models of action classes. Based on this representation, we suggest a combined segmentation and classification algorithm which processes data non-greedily using an incremental lookahead to reliably locate transitions between actions. In a programming by demonstration scenario, our action models afford the generalization and reproduction of learned movements to previously unseen situations. To evaluate the performance of our approach, we consider typical manipulation tasks in a table top setting. In a sequence of human demonstrations, our approach successfully extracts and recognizes actions from different classes and subsequently generalizes them to unknown situations.