The state-of-the-art reinforcement learning (RL) techniques have made innumerable
advancements in robot control, especially in combination with deep neural networks …

The vision behind this paper looks ahead to evolutionary robot systems where morphologies
and controllers are evolved together and 'newborn'robots undergo a learning process to …

Morphologically evolving robot systems need to include a learning period right after 'birth'to
acquire a controller that fits the newly created body. In this paper, we investigate learning …

In this paper, we employ a central pattern generator (CPG) driven radial basis function
network (RBFN) based controller to learn optimized locomotion for a complex dung beetle …

To resemble the body flexibility of biological snakes, snake-like robots are designed as a
chain of body modules, which gives them many degrees of freedom (DoF) on the one hand …

Snake robots can move flexibly due to their special bodies and gaits. However, it is difficult
to plan their motion in multi-obstacle environments due to their complex models. To solve …

Motor primitives provide a modular organization to complex behaviours in both vertebrates
and invertebrates. Inspired by this, here we generate motor primitives for a complex snake …

This paper presents a method for snake robots with orthogonal joints to follow a path via
crawler gait. Considering snake robot system's redundancy, a novel path integral …

Inspired by the versatile locomotion skills of biological snakes, researchers have designed
many kinds of snake-like robots to mimic the structural and functional properties of their …

This paper estimates the marginal accident externality of driving in Central London by
exploiting variation in traffic flow induced by the London Congestion Charge Zone using an …