This article is about deep learning (DL) and deep reinforcement learning (DRL) works
applied to robotics. Both tools have been shown to be successful in delivering data-driven …

This work explores the use of topological tools for achieving effective non-prehensile
manipulation in cluttered, constrained workspaces. In particular, it proposes the use of …

Robotic manipulation is inherently continuous, but typically has an underlying discrete
structure, such as if an object is grasped. Many problems like these are multimodal, such as …

Many algorithms in probabilistic sampling-based motion planning have been proposed to
create a path for a robot in an environment with obstacles. Due to the randomness of …

Many robotic manipulation tasks are constrained due to kinematic limitations placed on the
object being manipulated. This increases the complexity of manipulation tasks that operate …

We address the problem of planning robot motions in constrained configuration spaces
where the constraints change throughout the motion. The problem is formulated as a …

Contact-based motion planning for manipulation, object exploration or balancing often
requires finding sequences of fixed and sliding contacts and planning the transition from one …

Traditionally, models for control and motion planning were derived from physical properties
of the system. While such a classical approach provides mathematical performance …