13 citations · 20 across the 13 of their papers we have counts for
6 papers · 1 filter
Retriever: Composing the Perception-Reasoning-Action Loop for Long-Horizon Manipulation
Linfeng Zhao, Haojie Huang, Jiayuan Mao +3
Building long-horizon robot agents requires composing closed-loop pipelines -- perception, belief update, planning, and control -- whose components run at different clocks and with…
MoMaGen: Generating Demonstrations under Soft and Hard Constraints for Multi-Step Bimanual Mobile Manipulation
Chengshu Li, Mengdi Xu, Arpit Bahety +11
Imitation learning from large-scale, diverse human demonstrations has been shown to be effective for training robots, but collecting such data is costly and time-consuming. This ch…
Learning Compositional Behaviors from Demonstration and Language
Weiyu Liu, Neil Nie, Ruohan Zhang +2
We introduce Behavior from Language and Demonstration (BLADE), a framework for long-horizon robotic manipulation by integrating imitation learning and model-based planning. BLADE l…
Composable Part-Based Manipulation
Weiyu Liu, Jiayuan Mao, Joy Hsu +3
In this paper, we propose composable part-based manipulation (CPM), a novel approach that leverages object-part decomposition and part-part correspondences to improve learning and…
Learning Planning Abstractions from Language
Weiyu Liu, Geng Chen, Joy Hsu +2
This paper presents a framework for learning state and action abstractions in sequential decision-making domains. Our framework, planning abstraction from language (PARL), utilizes…
Foundation Models in Robotics: Applications, Challenges, and the Future
Roya Firoozi, Johnathan Tucker, Stephen Tian +12
We survey applications of pretrained foundation models in robotics. Traditional deep learning models in robotics are trained on small datasets tailored for specific tasks, which li…