A fundamental task in robotics is to plan collision‐free motions among a set of obstacles.
Recently, learning‐based motion‐planning methods have shown significant advantages in …

Robotic object rearrangement combines the skills of picking and placing objects. When
object models are unavailable, typical collision-checking models may be unable to predict …

In response to the growing demands for faster production and cost‐efficiency, collaborative
and autonomous robots are playing increasingly important roles in various industries …

High-quality and representative data is essential for both Imitation Learning (IL)-and
Reinforcement Learning (RL)-based motion planning tasks. For real robots, it is challenging …

Robot motion planning has made vast advances over the past decades, but the challenge
remains: robot mobile manipulators struggle to plan long-range whole-body motion in …

Many applications in manipulators require computing the minimum self-collision distance
among links for safety. The calculation is time-consuming, especially when whole-body …

In this paper, we deal with the problem of self-collision detection for a mobile-manipulating
robot. Typically, this problem is solved by the method that precisely checks the collision …

This work presents a Robust Stochastic Model Predictive Control (RSMPC) framework for
real-time motion planning autonomous vehicles, addressing the complex multi-modal …

Controlling robot behavior has been realized in the past by means of manual programming
of behaviors. This requires too much human programming effort and is typically too specific …

When operating in unstructured and semi-structured environments such as warehouses,
homes, and retail centers, robots are frequently required to interactively search for and …