62 citations · 107 across the 5 of their papers we have counts for
5 papers
BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation
Chengshu Li, Ruohan Zhang, Josiah Wong +32
We present BEHAVIOR-1K, a comprehensive simulation benchmark for human-centered robotics. BEHAVIOR-1K includes two components, guided and motivated by the results of an extensive s…
Task-Driven Graph Attention for Hierarchical Relational Object Navigation
Michael Lingelbach, Chengshu Li, Minjune Hwang +6
Embodied AI agents in large scenes often need to navigate to find objects. In this work, we study a naturally emerging variant of the object navigation task, hierarchical relationa…
Modeling Dynamic Environments with Scene Graph Memory
Andrey Kurenkov, Michael Lingelbach, Tanmay Agarwal +7
Embodied AI agents that search for objects in large environments such as households often need to make efficient decisions by predicting object locations based on partial informati…
BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments
Sanjana Srivastava, Chengshu Li, Michael Lingelbach +11
We introduce BEHAVIOR, a benchmark for embodied AI with 100 activities in simulation, spanning a range of everyday household chores such as cleaning, maintenance, and food preparat…
iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks
Chengshu Li, Fei Xia, Roberto Martín-Martín +13
Recent research in embodied AI has been boosted by the use of simulation environments to develop and train robot learning approaches. However, the use of simulation has skewed the…