Interactive Robot Learning of Gestures, Language and Affordances

A growing field in robotics and Artificial Intelligence (AI) research is human-robot collaboration, whose target is to enable effective teamwork between humans and robots. However, in many situations human teams are still superior to human-robot teams, primarily because human teams can easily agree on a common goal with language, and the individual members observe each other effectively, leveraging their shared motor repertoire and sensorimotor resources. This paper shows that for cognitive robots it is possible, and indeed fruitful, to combine knowledge acquired from interacting with elements of the environment (affordance exploration) with the probabilistic observation of another agent's actions. We propose a model that unites (i) learning robot affordances and word descriptions with (ii) statistical recognition of human gestures with vision sensors. We discuss theoretical motivations, possible implementations, and we show initial results which highlight that, after having acquired knowledge of its surrounding environment, a humanoid robot can generalize this knowledge to the case when it observes another agent (human partner) performing the same motor actions previously executed during training.Comment: code available at https://github.com/gsaponaro/glu-gesture

Q-CP: Learning Action Values for Cooperative Planning

Research on multi-robot systems has demonstrated promising results in manifold applications and domains. Still, efficiently learning an effective robot behaviors is very difficult, due to unstructured scenarios, high uncertainties, and large state dimensionality (e.g. hyper-redundant and groups of robot). To alleviate this problem, we present Q-CP a cooperative model-based reinforcement learning algorithm, which exploits action values to both (1) guide the exploration of the state space and (2) generate effective policies. Specifically, we exploit Q-learning to attack the curse-of-dimensionality in the iterations of a Monte-Carlo Tree Search. We implement and evaluate Q-CP on different stochastic cooperative (general-sum) games: (1) a simple cooperative navigation problem among 3 robots, (2) a cooperation scenario between a pair of KUKA YouBots performing hand-overs, and (3) a coordination task between two mobile robots entering a door. The obtained results show the effectiveness of Q-CP in the chosen applications, where action values drive the exploration and reduce the computational demand of the planning process while achieving good performance

Modeling teamwork of multi-human multi-agent teams

Teamwork is important when humans work together with automated agents to perform tasks requiring monitoring, coordination, and complex decision making. While human-agent teams can bring many benefits such as higher productivity, adaptability and creativity, they may also fail for various reasons. It is important to understand the tradeoffs in teamwork. The purpose of this research is to investigate the process and outcomes of human-agent teamwork by running experiments and building quantitative simulation models. Preliminary results are discussed as well as future directions. We expect this research to deepen the under-standing of human-agent teamwork and provide recommendations for the design of teams and agents to support teamwork.This research is sponsored by the Office for Naval Research and the Air Force Office of Scientific Research

Human-robot teamwork: a knowledge-based solution

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de ComputadoresTeams of humans and robots pose new challenges to the teamwork field. This stems from the fact that robots and humans have significantly different perceptual, reasoning, communication and actuation capabilities. This dissertation contributes to solving this problem by proposing a knowledge-based multi-agent system to support design and execution of stereotyped (i.e. recurring) human-robot teamwork. The cooperative workflow formalism has been selected to specify team plans, and adapted to allow activities to share structured data, even during their execution. This novel functionality enables tightly coupled interactions among team members. Rather than focusing on automatic teamwork planning, this dissertation proposes a complementary and intuitive knowledge-based solution for fast deployment and adaptation of small scale human-robot teams. In addition, the system has been designed in order to improve task awareness of each mission participant, and of the human overall mission awareness. A set of empirical results obtained from simulated and real missions proved the concept and the reusability of such a system. Practical results showed that this approach used is an effective solution for small scale teams in stereotyped human-robot teamwork