Learning the dynamics of the external world: Brain inspired learning for robotic applications

Humans have exceptional abilities to learn new skills, manipulate tools and objects, and interact with our environment. In order to be successful at these tasks, our brain has become exceptionally well adapted to learning to deal not only with the complex dynamics of our own limbs but also with novel dynamics in the external world. While learning of these dynamics includes learning the complex time-varying forces at the end of limbs through the updating of internal models, it must also include learning the appropriate mechanical impedance in order to stabilize both the limb and any objects contacted in the environment. This article reviews the field of human learning by examining recent experimental evidence about adaptation to novel unstable dynamics and explores how this knowledge about the brain and neuro-muscular system can expand the learning capabilities of robotics and prosthetics. © 2006.

Low-carbon off-grid electrification for rural areas in the United Kingdom: Lessons from the developing world

Low-carbon off-grid electrification for rural areas is becoming increasingly popular in the United Kingdom. However, many developing countries have been electrifying their rural areas in this way for decades. Case study fieldwork in Nepal and findings from United Kingdom based research will be used to examine how developed nations can learn from the experience of developing countries with regard to the institutional environment and delivery approach adopted in renewable energy off-grid rural electrification. A clearer institutional framework and more direct external assistance during project development are advised. External coordinators should also engage the community in a mobilization process a priori to help alleviate internal conflicts of interest that could later impede a project. © 2011 Elsevier Ltd.

A reason for unreason: returns-based beliefs in game theory

Players cooperate in experiments more than game theory would predict. We introduce the ‘returns-based beliefs’ approach: the expected returns of a particular strategy in proportion to total expected returns of all strategies. Using a decision analytic solution concept, Luce’s (1959) probabilistic choice model, and ‘hyperpriors’ for ambiguity in players’ cooperability, our approach explains empirical observations in various classes of games including the Prisoner’s and Traveler’s Dilemmas. Testing the closeness of fit of our model on Selten and Chmura (2008) data for completely mixed 2 × 2 games shows that with loss aversion, returns-based beliefs explain the data better than other equilibrium concepts.

Low-carbon off-grid electrification for rural areas: Lessons from the developing world

Low-carbon off-grid electrification for rural areas is becoming increasingly popular in developed nations such as the United Kingdom. However, many developing countries have been electrifying their rural areas in this way for decades. Case study fieldwork in Nepal and findings from UK-based research will be used to examine how developed nations can learn from the experience of developing countries with regards the institutional environment and delivery approach adopted in renewable energy off-grid rural electrification. A clearer institutional framework and more direct external assistance during project development are advised. External coordinators should also engage the community in a mobilization process a priori to help alleviate internal conflicts of interest that could later impede a project. ©2010 IEEE.

Linear quadratic game and non-cooperative predictive methods for potential application to modelling driver-AFS interactive steering control

This paper is concerned with the modelling of strategic interactions between the human driver and the vehicle active front steering (AFS) controller in a path-following task where the two controllers hold different target paths. The work is aimed at extending the use of mathematical models in representing driver steering behaviour in complicated driving situations. Two game theoretic approaches, namely linear quadratic game and non-cooperative model predictive control (non-cooperative MPC), are used for developing the driver-AFS interactive steering control model. For each approach, the open-loop Nash steering control solution is derived; the influences of the path-following weights, preview and control horizons, driver time delay and arm neuromuscular system (NMS) dynamics are investigated, and the CPU time consumed is recorded. It is found that the two approaches give identical time histories as well as control gains, while the non-cooperative MPC method uses much less CPU time. Specifically, it is observed that the introduction of weight on the integral of vehicle lateral displacement error helps to eliminate the steady-state path-following error; the increase in preview horizon and NMS natural frequency and the decline in time delay and NMS damping ratio improve the path-following accuracy. © 2013 Copyright Taylor and Francis Group, LLC.