7 accepted papers
Co-training, which combines limited in-domain real-world data with abundant surrogate data such as simulation or cross-embodiment demonstrations, has been widely adopted for training generative visuomotor robot policies. Despite its empirical success, the mechanisms underlying when and why co-traini…
Recent advances in robot learning have accelerated progress toward generalist robots that can operate across diverse tasks and environments. Yet despite this momentum, it remains difficult to gauge how close we are to this goal, as the field lacks a reproducible, large-scale benchmark for systematic…
Large real-world robot datasets hold great potential for developing generalist robot policies, but scaling real-world data collection is time-consuming, costly, and resource-intensive. Simulation offers a promising solution, with recent advances in generative AI and synthetic data generation tools e…
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. However, creating such datasets is challenging: collecting robot manipulation data in diverse environments poses logistica…
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for man
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for man…
Recent advancements in Artificial Intelligence (AI) have largely been propelled by scaling. In Robotics, scaling is hindered by the lack of access to massive robot datasets. We advocate using realistic physical simulation as a means to scale environments, tasks, and datasets for robot learning metho…