Since the emergence of ChatGPT, research on large language models (LLMs) has actively
progressed across various fields. LLMs, pre-trained on vast text datasets, have exhibited …

We are interested in enabling visual planning for complex long-horizon tasks in the space of
generated videos and language, leveraging recent advances in large generative models …

We present ChainedDiffuser, a policy architecture that unifies action keypose prediction and
trajectory diffusion generation for learning robot manipulation from demonstrations. Our …

We propose a system for rearranging objects in a scene to achieve a desired object-scene
placing relationship, such as a book inserted in an open slot of a bookshelf. The pipeline …

Pre-trained text-to-image generative models can produce diverse, semantically rich, and
realistic images from natural language descriptions. Compared with language, images …

Object rearrangement is pivotal in robotic-environment interactions, representing a
significant capability in embodied AI. In this paper, we present SG-Bot, a novel …

3D perceptual representations are well suited for robot manipulation as they easily encode
occlusions and simplify spatial reasoning. Many manipulation tasks require high spatial …

3D perceptual representations are well suited for robot manipulation as they easily encode
occlusions and simplify spatial reasoning. Many manipulation tasks require high spatial …

Learning from Demonstrations, the field that proposes to learn robot behavior models from
data, is gaining popularity with the emergence of deep generative models. Although the …

Generalist robot manipulators need to learn a wide variety of manipulation skills across
diverse environments. Current robot training pipelines rely on humans to provide kinesthetic …