103 citations · 152 across the 16 of their papers we have counts for
4 papers · 1 filter
LHManip: A Dataset for Long-Horizon Language-Grounded Manipulation Tasks in Cluttered Tabletop Environments
Federico Ceola, Lorenzo Natale, Niko Sünderhauf +1
Instructing a robot to complete an everyday task within our homes has been a long-standing challenge for robotics. While recent progress in language-conditioned imitation learning…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning
Krishan Rana, Jesse Haviland, Sourav Garg +3
Large language models (LLMs) have demonstrated impressive results in developing generalist planning agents for diverse tasks. However, grounding these plans in expansive, multi-flo…
Contrastive Language, Action, and State Pre-training for Robot Learning
Krishan Rana, Andrew Melnik, Niko Sünderhauf
In this paper, we introduce a method for unifying language, action, and state information in a shared embedding space to facilitate a range of downstream tasks in robot learning. O…