Sampling-based motion planning is one of the fundamental paradigms to generate robot
motions, and a cornerstone of robotics research. This comparative review provides an up-to …

Many estimation, planning, and optimal control problems in robotics have an optimization
problem at their core. In most of these optimization problems, the objective to be maximized …

Abstract We introduce Habitat 2.0 (H2. 0), a simulation platform for training virtual robots in
interactive 3D environments and complex physics-enabled scenarios. We make …

We present Theseus, an efficient application-agnostic open source library for differentiable
nonlinear least squares (DNLS) optimization built on PyTorch, providing a common …

We present iSDF, a continual learning system for real-time signed distance field (SDF)
reconstruction. Given a stream of posed depth images from a moving camera, it trains a …

Collision-free motion generation in unknown environments is a core building block for robot
manipulation. Generating such motions is challenging due to multiple objectives; not only …

Learning priors on trajectory distributions can help accelerate robot motion planning
optimization. Given previously successful plans, learning trajectory generative models as …

This paper introduces the task of Planar Robot Casting (PRC): where one planar motion of a
robot arm holding one end of a cable causes the other end to slide across the plane toward …

We formulate grasp learning as a neural field and present Neural Grasp Distance Fields
(NGDF). Here, the input is a 6D pose of a robot end effector and output is a distance to a …

This paper explores the problem of collision-free motion generation for manipulators by
formulating it as a global motion optimization problem. We develop a parallel optimization …