This paper introduces Diffusion Policy, a new way of generating robot behavior by
representing a robot's visuomotor policy as a conditional denoising diffusion process. We …

Large text-guided diffusion models, such as DALLE-2, are able to generate stunning
photorealistic images given natural language descriptions. While such models are highly …

Molecular graph representation learning is a fundamental problem in modern drug and
material discovery. Molecular graphs are typically modeled by their 2D topological …

We find that across a wide range of robot policy learning scenarios, treating supervised
policy learning with an implicit model generally performs better, on average, than commonly …

Since their introduction, diffusion models have quickly become the prevailing approach to
generative modeling in many domains. They can be interpreted as learning the gradients of …

Energy-Based Models (EBMs), also known as non-normalized probabilistic models, specify
probability density or mass functions up to an unknown normalizing constant. Unlike most …

Imitation learning (IL), a burgeoning frontier in machine learning, holds immense promise
across diverse domains. In recent years, its integration into robotics has sparked significant …

We present energy-based generative flow networks (EB-GFN), a novel probabilistic
modeling algorithm for high-dimensional discrete data. Building upon the theory of …

Humans are able to rapidly understand scenes by utilizing concepts extracted from prior
experience. Such concepts are diverse, and include global scene descriptors, such as the …

Controllable generation is one of the key requirements for successful adoption of deep
generative models in real-world applications, but it still remains as a great challenge. In …