Large language models (LLMs) are shown to possess a wealth of actionable knowledge that
can be extracted for robot manipulation in the form of reasoning and planning. Despite the …

We introduce the first work to explore web-scale diffusion models for robotics. DALL-E-Bot
enables a robot to rearrange objects in a scene, by first inferring a text description of those …

Diffusion models have demonstrated highly-expressive generative capabilities in vision and
NLP. Recent studies in reinforcement learning (RL) have shown that diffusion models are …

Prompt engineering is a technique that involves augmenting a large pre-trained model with
task-specific hints, known as prompts, to adapt the model to new tasks. Prompts can be …

Long-horizon tasks, usually characterized by complex subtask dependencies, present a
significant challenge in manipulation planning. Skill chaining is a practical approach to …

Large policies pretrained on diverse robot datasets have the potential to transform robotic
learning: instead of training new policies from scratch, such generalist robot policies may be …

If generalist robots are to operate in truly unstructured environments, they need to be able to
recognize and reason about novel objects and scenarios. Such objects and scenarios might …

The grand aim of having a single robot that can manipulate arbitrary objects in diverse
settings is at odds with the paucity of robotics datasets. Acquiring and growing such datasets …

Language-conditioned robotic manipulation represents a cutting-edge area of research,
enabling seamless communication and cooperation between humans and robotic agents …

Reinforcement learning from human feedback (RLHF) is a variant of reinforcement learning
(RL) that learns from human feedback instead of relying on an engineered reward function …