This work aims at reviewing, analyzing and comparing a range of state-of-the-art
approaches to inertial parameter identification in the context of robotics. We introduce …

Developing robot agnostic software frameworks involves synthesizing the disparate fields of
robotic theory and software engineering while simultaneously accounting for a large …

Robots are important in high-mix low-volume manufacturing because of their versatility and
repeatability in performing manufacturing tasks. However, robots have not been widely used …

For collaborative robots to become useful, end users who are not robotics experts must be
able to instruct them to perform a variety of tasks. With this goal in mind, we developed a …

While deep reinforcement learning techniques have recently produced considerable
achievements on many decision-making problems, their use in robotics has largely been …

We present a new constraint-based framework for task and motion planning (TMP). Our
approach is extensible, probabilistically complete, and offers improved performance and …

This paper introduces a computationally efficient control scheme for safe human–robot
interaction. The method relies on the Explicit Reference Governor (ERG) formalism to …

Modern approaches for robot kinematics employ the product of exponentials formulation,
represented using homogeneous transformation matrices. Quaternions over dual numbers …

We present the first platform-independent evaluation method for task and motion planning
(TAMP). Previously point, various problems have been used to test individual planners for …

The complexity of robotic path planning problems in industrial manufacturing increases
significantly with the current trends of product individualization and flexible production …