Reinforcement learning (RL), imitation learning (IL), and task and motion planning (TAMP)
have demonstrated impressive performance across various robotic manipulation tasks …

In order to develop robots that can effectively serve as versatile and capable home
assistants, it is crucial for them to reliably perceive and interact with a wide variety of objects …

Self-supervised learning of image representations by predicting future frames is a promising
direction but still remains a challenge. This is because of the under-determined nature of …

Contemporary artificial intelligence systems exhibit rapidly growing abilities accompanied by
the growth of required resources, expansive datasets and corresponding investments into …

We introduce OpenBot-Fleet, a comprehensive open-source cloud robotics system for
navigation. OpenBot-Fleet uses smartphones for sensing, local compute and …

We present CloudGripper, an open source cloud robotics testbed, consisting of a scalable,
space and cost-efficient design constructed as a rack of 32 small robot arm work cells. Each …

Experimentation on real robots is demanding in terms of time and costs. For this reason, a
large part of the reinforcement learning (RL) community uses simulators to develop and …

Robotic manipulation and control has increased in importance in recent years. However,
state of the art techniques still have limitations when required to operate in real world …

We present a new reproducible benchmark for evaluating robot manipulation in the real
world, specifically focusing on a pick-and-place task. Our benchmark uses the YCB object …

The ecosystem of artificial intelligence competitions is a diverse and multifaceted landscape,
encompassing a variety of platforms that each host numerous competitions annually …