Modular robotic systems

TL;DR: In this paper, the authors present a test suite of robots with different shapes and sizes and compare two learning algorithms, Bayesian optimization and HyperNEAT, for the task of directed locomotion in evolvable modular robots.

TL;DR: An extensive survey of the current state of the art of self/reconfiguration algorithms and underlying models in modular robotic and self-organizing particle systems is proposed and three approaches for solving the shape formation problem are identified.

TL;DR: A dynamic algorithm of robot actions that can be used in the decision module has been considered and the experiment results showed that Mivar decision-making systems can control groups of small robots and even an unmanned autonomous car in real time.

TL;DR: This paper proposes a method for constructing a parametric scaffolding model that increases the parallelism of the reconfiguration, supports its mechanical stability, and simplifies planning and coordination between agents.

TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.

TL;DR: In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning.

TL;DR: Self-assembling processes are common throughout nature and technology and involve components from the molecular to the planetary scale and many different kinds of interactions.

TL;DR: Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (/spl ap/150 MIPS), after learning for relatively short periods of time (a few dozen seconds).