Imitation learning from human demonstrations has shown impressive performance in
robotics. However, most results focus on table-top manipulation, lacking the mobility and …

The creation of large, diverse, high-quality robot manipulation datasets is an important
stepping stone on the path toward more capable and robust robotic manipulation policies …

Open-vocabulary generalization requires robotic systems to perform tasks involving complex
and diverse environments and task goals. While the recent advances in vision language …

Many robotic systems, such as mobile manipulators or quadrotors, cannot be equipped with
high-end GPUs due to space, weight, and power constraints. These constraints prevent …

Open-sourced, user-friendly tools form the bedrock of scientific advancement across
disciplines. The widespread adoption of data-driven learning has led to remarkable …

Recent developments in foundation models, like Large Language Models (LLMs) and Vision-
Language Models (VLMs), trained on extensive data, facilitate flexible application across …

This work introduces DiffuseLoco, a framework for training multi-skill diffusion-based policies
for dynamic legged locomotion from offline datasets, enabling real-time control of diverse …

Recent years in robotics and imitation learning have shown remarkable progress in training
large-scale foundation models by leveraging data across a multitude of embodiments. The …

Training general robotic policies from heterogeneous data for different tasks is a significant
challenge. Existing robotic datasets vary in different modalities such as color, depth, tactile …

While learning from demonstrations is powerful for acquiring visuomotor policies, high-
performance imitation without large demonstration datasets remains challenging for tasks …