LLMs with visual inputs, ie, Vision Language Models (VLMs), have the capacity to process
state information as visual-textual prompts and respond with policy decisions in text. We …

Diffusion models have been adopted for behavioral cloning in a sequence modeling
fashion, benefiting from their exceptional capabilities in modeling complex data distributions …

Humans are remarkably flexible in understanding viewpoint changes due to visual cortex
supporting the perception of 3D structure. In contrast, most of the computer vision models …

Parcel singulation has emerged as a critical bottleneck in the swiftly advancing logistics
processes. In the pursuit of a balance between cost-effectiveness and singulation efficiency …

Offline reinforcement learning (RL) has been shown to be successfully modeled as a
sequence modeling problem, drawing inspiration from the success of Transformers. Offline …

Existing safe imitation learning (safe IL) methods mainly focus on learning safe policies that
are similar to expert ones, but may fail in applications requiring different safety constraints. In …

In this work, we investigate Active Vision Reinforcement Learning (ActiveVision-RL), where
an embodied agent simultaneously learns action policy for the task while also controlling its …

Offline preference-based reinforcement learning (PbRL) typically operates in two phases:
first, use human preferences to learn a reward model and annotate rewards for a reward …

Reinforcement Learning (RL) is a dynamic and influential field within artificial intelligence
that focuses on how agents should take actions in an environment to maximize a cumulative …