Fall Prediction for New Sequences of Motions

Abstract. Motions reinforce meanings in human-robot communication, when they are relevant and initiated at the right times. Given a task of using motions for an autonomous humanoid robot to communicate, different sequences of relevant motions are generated from the motion library. Each motion in the motion library is stable, but a sequence may cause the robot to be unstable and fall. We are interested in predicting if a sequence of motions will result in a fall, without executing the sequence on the robot. We contribute a novel algorithm, ProFeaSM, that uses only body angles collected during the execution of single motions and interpolations between pairs of motions, to predict whether a sequence will cause the robot to fall. We demonstrate the efficacy of ProFeaSM on the NAO humanoid robot in a real-time simulator, Webots, and on a real NAO and explore the trade-off between precision and recall

Towards Rapid Multi-robot Learning from Demonstration at the RoboCup Competition

Abstract. We describe our previous and current efforts towards achiev-ing an unusual personal RoboCup goal: to train a full team of robots directly through demonstration, on the field of play at the RoboCup venue, how to collaboratively play soccer, and then use this trained team in the competition itself. Using our method, HiTAB, we can train teams of collaborative agents via demonstration to perform nontrivial joint behaviors in the form of hierarchical finite-state automata. We discuss HiTAB, our previous efforts in using it in RoboCup 2011 and 2012, recent experimental work, and our current efforts for 2014, then suggest a new RoboCup Technical Challenge problem in learning from demonstration. Imagine that you are at an unfamiliar disaster site with a team of robots, and are faced with a previously unseen task for them to do. The robots have only rudimentary but useful utility behaviors implemented. You are not a programmer. Without coding them, you have only a few hours to get your robots doing useful collaborative work in this new environment. How would you do this

PAC models in stochastic multi-objective multi-armed bandits

Many real-world applications, such as stock markets, energy consumption time series, and scheduling in noisy environments, are characterised by stochastic feedback. In this paper, the evolutionary multi-objective (EMO) techniques, like elitist selection strategies, and the probably approximatively correct (PAC) model are used to analyse the multi-armed bandits (MAB) paradigm that identifies the Pareto front from a finite set of arms with stochastic reward vectors. Each arm is associated with a confidence ball centred in the sampling's mean vector that decreases towards its true vector when the number of samples increases. The Pareto lower upper confidence bound algorithm samples the alternatives for which their confidence ball overlaps with the confidence regions of the Pareto optimal arms. Pareto racing deletes the arms classified with certainty as either suboptimal or Pareto optimal arms. The sample complexity estimates the number of samples required for an accurate approximation of the Pareto front using two different statistics, i.e. empirically determined means or quantiles. The analysed PAC models are empirically compared on realistic datasets with two and three objectives