Recent advances in machine learning have led to increased interest in solving visual
computing problems using methods that employ coordinate‐based neural networks. These …

This work proposes an optimization-based manipulation planning framework where the
objectives are learned functionals of signed-distance fields that represent objects in the …

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 …

Autonomous robots should operate in real-world dynamic environments and collaborate
with humans in tight spaces. A key component for allowing robots to leave structured lab and …

In this work, we propose to learn robot geometry as distance fields (RDF), which extend the
signed distance field (SDF) of the robot with joint configurations. Unlike existing methods …

When facing a new motion-planning problem, most motion planners solve it from scratch, eg,
via sampling and exploration or starting optimization from a straight-line path. However …

We propose a method for learning constraints represented as Gaussian processes (GPs)
from locally-optimal demonstrations. Our approach uses the Karush-Kuhn-Tucker (KKT) …

Safe robot motion generation is critical for practical applications from manufacturing to
homes. In this work, we proposed a stochastic optimization-based motion generation …

Constrained Motion Planning (CMP) aims to find a collision-free path between the given
start and goal configurations on the kinematic constraint manifolds. These problems appear …

This paper focuses on an accurate and fast interpolation approach for image transformation
employed in the design of CNN architectures. Standard Spatial Transformer Networks …