Training an agent to achieve particular goals or perform desired behaviors is often accomplished through reinforcement learning, especially in the absence of expert demonstrations. However, supporting novel goals or behaviors through reinforcement learning requires the ad-hoc design of appropriate reward functions, which quickly becomes intractable. Toaddress thischallenge, wepropose Text-AwareDiffusion forPolicyLearning (TADPoLe), which uses apretrained, frozen text-conditioned diffusion model to compute dense zero-shot reward signals for text-aligned policy learning.

Human actions in egocentric videos often feature hand-object interactions composed of a verb (performed by the hand) applied to an object.

Material discovery holds transformative potential across numerous industries including carbon capture[38], batteries[28], photovoltaics[9], and energy storage[1]. LLMs excel atmodeling discrete values, but they can struggle with continuous values due to their reliance on finite precision representations.

Pre-trained multi-modal vision-language models (VLMs) are becoming increasingly popular due to their exceptional performance on downstream vision applications, particularly in the few-and zero-shot settings.