143 citations · 272 across the 33 of their papers we have counts for
7 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…
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…
Learning Reusable Manipulation Strategies
Jiayuan Mao, Joshua B. Tenenbaum, Tomás Lozano-Pérez +1
Humans demonstrate an impressive ability to acquire and generalize manipulation "tricks." Even from a single demonstration, such as using soup ladles to reach for distant objects,…
Learning to Act from Actionless Videos through Dense Correspondences
Po-Chen Ko, Jiayuan Mao, Yilun Du +2
In this work, we present an approach to construct a video-based robot policy capable of reliably executing diverse tasks across different robots and environments from few video dem…