Abstract The 5th Generation (5G) and beyond networks are expected to offer huge
throughputs, connect large number of devices, support low latency and large numbers of …

Large language models can encode a wealth of semantic knowledge about the world. Such
knowledge could be extremely useful to robots aiming to act upon high-level, temporally …

Navigation is one of the most heavily studied problems in robotics and is conventionally
approached as a geometric mapping and planning problem. However, real-world navigation …

In this paper, we study the problem of enabling a vision-based robotic manipulation system
to generalize to novel tasks, a long-standing challenge in robot learning. We approach the …

We present a framework for building interactive, real-time, natural language-instructable
robots in the real world, and we open source related assets (dataset, environment …

Reward and representation learning are two long-standing challenges for learning an
expanding set of robot manipulation skills from sensory observations. Given the inherent …

In this work, we present a scalable reinforcement learning method for training multi-task
policies from large offline datasets that can leverage both human demonstrations and …

We study the problem of learning a range of vision-based manipulation tasks from a large
offline dataset of robot interaction. In order to accomplish this, humans need easy and …

Goal-conditioned reinforcement learning (GCRL), related to a set of complex RL problems,
trains an agent to achieve different goals under particular scenarios. Compared to the …

Placement is a critical step in modern chip design, aiming to determine the positions of
circuit modules on the chip canvas. Recent works have shown that reinforcement learning …