Grasping is the process of picking up an object by applying forces and torques at a set of
contacts. Recent advances in deep learning methods have allowed rapid progress in robotic …

Retired electric-vehicle lithium-ion battery (EV-LIB) packs pose severe environmental
hazards. Efficient recovery of these spent batteries is a significant way to achieve closed …

Real world data, especially in the domain of robotics, is notoriously costly to collect. One way
to circumvent this can be to leverage the power of simulation to produce large amounts of …

Skilled robotic manipulation benefits from complex synergies between non-prehensile (eg
pushing) and prehensile (eg grasping) actions: pushing can help rearrange cluttered objects …

To reduce data collection time for deep learning of robust robotic grasp plans, we explore
training from a synthetic dataset of 6.7 million point clouds, grasps, and analytic grasp …

This article presents a robotic pick-and-place system that is capable of grasping and
recognizing both known and novel objects in cluttered environments. The key new feature of …

Instrumenting and collecting annotated visual grasping datasets to train modern machine
learning algorithms can be extremely time-consuming and expensive. An appealing …

General robot grasping in clutter requires the ability to synthesize grasps that work for
previously unseen objects and that are also robust to physical interactions, such as …

We approach the problem of high-DOF reaching-and-grasping via learning joint planning of
grasp and motion with deep reinforcement learning. To resolve the sample efficiency issue …

Recently, a number of grasp detection methods have been proposed that can be used to
localize robotic grasp configurations directly from sensor data without estimating object …