The rise of deep learning has caused a paradigm shift in robotics research, favoring
methods that require large amounts of data. Unfortunately, it is prohibitively expensive to …

In recent years, a large number of manipulator robots have been deployed to replace or
assist humans in many repetitive and dangerous tasks. Yet, these robots have complex …

A deep reinforcement learning (DRL) agent observes its states through observations, which
may contain natural measurement errors or adversarial noises. Since the observations …

We study the robustness of reinforcement learning (RL) with adversarially perturbed state
observations, which aligns with the setting of many adversarial attacks to deep …

Evolutionary Robotics is a method for automatically generating artificial brains and
morphologies of autonomous robots. This approach is useful both for investigating the …

The pitfalls of naive robot simulations have been recognised for areas such as evolutionary
robotics. It has been suggested that carefully validated simulations with a proper treatment of …

A comprehensive introduction to new approaches in artificial intelligence and robotics that
are inspired by self-organizing biological processes and structures. New approaches to …

Using the language of dynamical systems theory, a general theoretical framework for the
synthesis and analysis of autonomous agents is sketched. In this framework, an agent and …

One category of research in Artificial Life is concerned with modeling and building so-called
adaptive autonomous agents, which are systems that inhabit a dynamic, unpredictable …

Catching Ourselves in the Act uses situated robotics, ethology, and developmental
psychology to erect a new framework for explaining human behavior. Rejecting the cognitive …