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

Identifying barriers in telesurgery by studying current team practices in robot-assisted surgery

This paper investigates challenges in current practices in robot-assisted surgery. In addition, by using the method of proxy technology assessment, we provide insights into the current barriers to wider application of robot-assisted telesurgery, where the surgeon and console are physically remote from the patient and operating team. Research in this field has focused on the financial and technological constraints that limit such application; less has been done to clarify the complex dynamics of an operating team that traditionally works in close symbiosis. Results suggest that there are implications for working practices in transitioning from traditional robot-assisted surgery to remote robotic surgery that need to be addressed, such as possible communication problems which might have a negative impact on patient outcomes

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

A Context-based Approach to Robot-human Interaction

AbstractCARIL (Context-Augmented Robotic Interaction Layer) is a human-robot interaction system that leverages cognitive representations of shared context as a basis for a fundamentally new approach to human-robotic interaction. CARIL gives a robot a human-like representation of context and an ability to reason about context in order to adapt its behavior to that of the humans around it. This capability is “action compliance.” A prototype CARIL implementation focuses on a fundamental form of action compliance called non-interference -- “not being underfoot or in a human's way”. Non-interference is key for the safety of human-co-workers, and is also foundational to more complex interactive and teamwork skills. CARIL is tested via simulation in a space-exploration use-case. The live CARIL prototype directs a single simulated robot in a simulated space station where four simulated astronauts are engaging in a variety of tightly-scheduled work activities. The robot is scheduled to perform background tasks away from the astronauts, but must quickly adapt and not be underfoot as astronaut activities diverge from plan and encroach on the robot's space. The robot, driven by CARIL, demonstrates non-interference action compliance in three benchmarks situations, demonstrating the viability of the CARIL technology and concept

Human variability, task complexity and motivation contribution in manufacturing

This paper is a preliminary study of the human contribution to variability in manufacturing industry and how motivation and learning play a key role in this contribution. The longer term aim is to incorporate this understanding in a methodology, using principles and guidelines, that aims to help in the design of intelligent automation that reduces product variability. This paper reports on the early stages that are concerned with understanding relationships between human-induced product variability, task complexity and human characteristics and capabilities. Two areas have been selected for initial study in manufacturing industry: (a) the relationship between manual task complexity and product variability and (b) the relationship between employee motivational factors and learning behaviours. The paper discusses the progress to date in conducting initial empirical studies and surveys in industry and draws tentative conclusions of the value of this knowledge to the overall objective of intelligent automation