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 letter addresses two challenges facing samplingbased kinodynamic motion planning: a
way to identify good candidate states for local transitions and the subsequent …

Sampling-based methods are widely adopted solutions for robot motion planning. The
methods are straightforward to implement, effective in practice for many robotic systems. It is …

We present a neural network collision checking heuristic, ClearanceNet, and a planning
algorithm, CN-RRT. ClearanceNet learns to predict separation distance (minimum distance …

We present MLNav, a learning-enhanced path planning framework for safety-critical and
resource-limited systems operating in complex environments, such as rovers navigating on …

We present an end-to-end online motion planning framework that uses a data-driven
approach to navigate a heterogeneous robot team towards a global goal while avoiding …

Borrowing elementary ideas from solid mechanics and differential geometry, this
presentation shows that the volume swept by a regular solid undergoing a wide class of …

Many methods have been developed for planning the motion of robotic arms for picking and
placing, ranging from local optimization to global search techniques, which are effective for …

Computation of the volume of space required for a robot to execute a sweeping motion from
a start to a goal has long been identified as a critical primitive operation in both task and …

Despite decades of research on efficient swept volume computation for robotics, computing
the exact swept volume is intractable and approximate swept volume algorithms have been …