activity
20192025
most citedInteractive Gibson Benchmark (iGibson 0.5): A Benchmark for Interactive Navigation in Cluttered Environments

126 citations · 354 across the 14 of their papers we have counts for

collaborators

14 papers

cs.RO2025

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…

cs.CV2024★ 1 cited

BEHAVIOR Vision Suite: Customizable Dataset Generation via Simulation

Yunhao Ge, Yihe Tang, Jiashu Xu +20

The systematic evaluation and understanding of computer vision models under varying conditions require large amounts of data with comprehensive and customized labels, which real-wo…

cs.RO2024★ 6 cited

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…

cs.CL2023★ 12 cited

Chain of Code: Reasoning with a Language Model-Augmented Code Emulator

Chengshu Li, Jacky Liang, Andy Zeng +7

Code provides a general syntactic structure to build complex programs and perform precise computations when paired with a code interpreter - we hypothesize that language models (LM…

cs.AI2023★ 1 cited

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…

cs.LG2023★ 2 cited

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…

cs.RO2023★ 16 cited

Principles and Guidelines for Evaluating Social Robot Navigation Algorithms

Anthony Francis, Claudia Pérez-D'Arpino, Chengshu Li +28

A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation. While the field of social navigation ha…

cs.RO2023

Sonicverse: A Multisensory Simulation Platform for Embodied Household Agents that See and Hear

Ruohan Gao, Hao Li, Gokul Dharan +6

Developing embodied agents in simulation has been a key research topic in recent years. Exciting new tasks, algorithms, and benchmarks have been developed in various simulators. Ho…

cs.RO2021★ 36 cited

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…

cs.RO2021★ 62 cited

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…

cs.AI2020★ 43 cited

iGibson 1.0: a Simulation Environment for Interactive Tasks in Large Realistic Scenes

Bokui Shen, Fei Xia, Chengshu Li +12

We present iGibson 1.0, a novel simulation environment to develop robotic solutions for interactive tasks in large-scale realistic scenes. Our environment contains 15 fully interac…

cs.AI2020★ 38 cited

ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation

Fei Xia, Chengshu Li, Roberto Martín-Martín +3

Many Reinforcement Learning (RL) approaches use joint control signals (positions, velocities, torques) as action space for continuous control tasks. We propose to lift the action s…

cs.LG2019★ 11 cited

HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile Manipulators

Chengshu Li, Fei Xia, Roberto Martin-Martin +1

Most common navigation tasks in human environments require auxiliary arm interactions, e.g. opening doors, pressing buttons and pushing obstacles away. This type of navigation task…

cs.RO2019★ 126 cited

Interactive Gibson Benchmark (iGibson 0.5): A Benchmark for Interactive Navigation in Cluttered Environments

Fei Xia, William B. Shen, Chengshu Li +6

We present Interactive Gibson Benchmark, the first comprehensive benchmark for training and evaluating Interactive Navigation: robot navigation strategies where physical interactio…