The behavior of self-driving cars must be compatible with an enormous set of conflicting and
ambiguous objectives, from law, from ethics, from the local culture, and so on. This paper …

This chapter presents an overview of learning approaches for the acquisition of controllers
and movement skills in humanoid robots. The term learning control refers to the process of …

Bimanual operations in humanoids offer the possibility to carry out more than one
manipulation task at the same time, which in turn introduces the problem of task …

Based on the recent advances of Deep Reinforcement Learning (DRL) and promising
results, in this paper, we propose a framework for strict priority assignment in the context of …

When learning skills from demonstrations, one is often required to think in advance about
the appropriate task representation (usually in either operational or configuration space). We …

Movement prioritization is a common approach to combine controllers of different tasks for
redundant robots, where each task is assigned a priority. The priorities of the tasks are often …

Tracking multiple prioritized tasks simultaneously with redundant robots have been
investigated extensively over the last decades. Recent research focuses on combining …

Dexterous manipulation tasks usually have multiple objectives. The priorities of these
objectives may vary at different phases of a manipulation task. Current methods do not …

Generating complex movements in redundant robots like humanoids is usually done by
means of multi-task controllers based on quadratic programming, where a multitude of tasks …

In this paper, we present a software-based approach for collision avoidance that can be
applied in human–robot collaboration scenarios. One of the contributions is a method for …