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 …

The Cloud infrastructure and its extensive set of Internet-accessible resources has potential
to provide significant benefits to robots and automation systems. We consider robots and …

Massive data corpora like WebText, Wikipedia, Conceptual Captions, WebImageText, and
LAION have propelled recent dramatic progress in AI. Large neural models trained on such …

Interactive 3D simulations have enabled break-throughs in robotics and computer vision, but
simulating the broad diversity of environments needed for deep learning requires large …

Meta-reinforcement learning algorithms can enable robots to acquire new skills much more
quickly, by leveraging prior experience to learn how to learn. However, much of the current …

Large, high-capacity models trained on diverse datasets have shown remarkable successes
on efficiently tackling downstream applications. In domains from NLP to Computer Vision …

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 …

We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input
image. The training data consists of a texture-mapped 3D object model or images of the …

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 …

Vacuum-based end effectors are widely used in industry and are often preferred over
parallel-jaw and multifinger grippers due to their ability to lift objects with a single point of …