AIConfPaper
← Search

Alexander Clegg

14 accepted papers

2025

PARTNR: A Benchmark for Planning and Reasoning in Embodied Multi-agent Tasks

Matthew Chang, Gunjan Chhablani, Alexander Clegg, Mikael Dallaire Cote, Ruta Desai, Michal Hlavac +14
ICLR 2025poster

We present a benchmark for Planning And Reasoning Tasks in humaN-Robot collaboration (PARTNR) designed to study human-robot coordination in household activities. PARTNR tasks exhibit characteristics of everyday tasks, such as spatial, temporal, and heterogeneous agent capability constraints. We empl…

Cited by 5Source ↗PDF ↗code ↗Scholar ↗
2024

ASC: Adaptive Skill Coordination for Robotic Mobile Manipulation

Naoki Yokoyama, Alexander Clegg, Joanne Truong, Eric Undersander, Tsung-Yen Yang, Sergio Arnaud +3
RA-L 2024

We present Adaptive Skill Coordination (ASC) – an approach for accomplishing long-horizon tasks like mobile pick-and-place (i.e., navigating to an object, picking it, navigating to another location, and placing it). ASC consists of three components – (1) a library of basic visuomotor <italic xmlns:m

Cited by 74Source ↗Scholar ↗
2024

Controllable Human-Object Interaction Synthesis

Jiaman Li*, Alexander Clegg, Roozbeh Mottaghi, Jiajun Wu, Xavier Puig, C. Karen Liu
ECCV 2024oral

"Synthesizing semantic-aware, long-horizon, human-object interaction is critical to simulate realistic human behaviors. In this work, we address the challenging problem of generating synchronized object motion and human motion guided by language descriptions in 3D scenes. We propose Controllable Hum…

Cited by 58Source ↗PDF ↗Scholar ↗
2024

Habitat 3.0: A Co-Habitat for Humans, Avatars, and Robots

Xavier Puig, Eric Undersander, Andrew Szot, Mikael Dallaire Cote, Tsung-Yen Yang, Ruslan Partsey +17
ICLR 2024poster

We present Habitat 3.0: a simulation platform for studying collaborative human-robot tasks in home environments. Habitat 3.0 offers contributions across three dimensions: (1) Accurate humanoid simulation: addressing challenges in modeling complex deformable bodies and diversity in appearance and mot…

Cited by 111Source ↗PDF ↗Scholar ↗
2024

Habitat Synthetic Scenes Dataset (HSSD-200): An Analysis of 3D Scene Scale and Realism Tradeoffs for ObjectGoal Navigation

Mukul Khanna, Yongsen Mao, Hanxiao Jiang, Sanjay Haresh, Brennan Shacklett, Dhruv Batra +4
CVPR 2024poster

We contribute the Habitat Synthetic Scene Dataset a dataset of 211 high-quality 3D scenes and use it to test navigation agent generalization to realistic 3D environments. Our dataset represents real interiors and contains a diverse set of 18656 models of real-world objects. We investigate the impact…

Cited by 53Source ↗PDF ↗Scholar ↗
2023

HomeRobot: Open-Vocabulary Mobile Manipulation

Sriram Yenamandra, Arun Ramachandran, Karmesh Yadav, Austin S Wang, Mukul Khanna, Theophile Gervet +12
CoRL 2023poster

HomeRobot (noun): An affordable compliant robot that navigates homes and manipulates a wide range of objects in order to complete everyday tasks. Open-Vocabulary Mobile Manipulation (OVMM) is the problem of picking any object in any unseen environment, and placing it in a commanded location. This i…

Cited by 98Source ↗code ↗Scholar ↗
2022

SoundSpaces 2.0: A Simulation Platform for Visual-Acoustic Learning

Changan Chen, Carl Schissler, Sanchit Garg, Philip Kobernik, Alexander Clegg, Paul Calamia +3
NeurIPS 2022accept

We introduce SoundSpaces 2.0, a platform for on-the-fly geometry-based audio rendering for 3D environments. Given a 3D mesh of a real-world environment, SoundSpaces can generate highly realistic acoustics for arbitrary sounds captured from arbitrary microphone locations. Together with existing 3D vi…

Cited by 94Source ↗PDF ↗Scholar ↗
2022

iSDF: Real-Time Neural Signed Distance Fields for Robot Perception

Joseph Ortiz, Alexander Clegg, Jing Dong, Edgar Sucar, David Novotny, Michael Zollhoefer +1
RSS 2022poster

We present iSDF, a continual learning system for real-time signed distance field (SDF) reconstruction. Given a stream of posed depth images from a moving camera, it trains a randomly initialised neural network to map input 3D coordinate to approximate signed distance. The model is self-supervised by…

Cited by 171Source ↗PDF ↗code ↗Scholar ↗
2021

Habitat 2.0: Training Home Assistants to Rearrange their Habitat

Andrew Szot, Alexander Clegg, Eric Undersander, Erik Wijmans, Yili Zhao, John M Turner +15
NeurIPS 2021spotlight

We introduce Habitat 2.0 (H2.0), a simulation platform for training virtual robots in interactive 3D environments and complex physics-enabled scenarios. We make comprehensive contributions to all levels of the embodied AI stack – data, simulation, and benchmark tasks. Specifically, we present: (i) R…

Cited by 579Source ↗PDF ↗code ↗Scholar ↗
2021

Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI

Santhosh Kumar Ramakrishnan, Aaron Gokaslan, Erik Wijmans, Oleksandr Maksymets, Alexander Clegg, John M Turner +7
NeurIPS 2021poster

We present the Habitat-Matterport 3D (HM3D) dataset. HM3D is a large-scale dataset of 1,000 building-scale 3D reconstructions from a diverse set of real-world locations. Each scene in the dataset consists of a textured 3D mesh reconstruction of interiors such as multi-floor residences, stores, and ot…

Cited by 434Source ↗code ↗Scholar ↗
2020

Learning to Collaborate From Simulation for Robot-Assisted Dressing

Alexander Clegg, Zackory Erickson, Patrick Grady, Greg Turk, Charles C. Kemp, C. Karen Liu
RA-L 2020

We investigated the application of haptic feedback control and deep reinforcement learning (DRL) to robot-assisted dressing. Our method uses DRL to simultaneously train human and robot control policies as separate neural networks using physics simulations. In addition, we modeled variations in human

Cited by 58Source ↗Scholar ↗
2020

Sim2Real Predictivity: Does Evaluation in Simulation Predict Real-World Performance?

Abhishek Kadian, Joanne Truong, Aaron Gokaslan, Alexander Clegg, Erik Wijmans, Stefan Lee +3
RA-L 2020

Does progress in simulation translate to progress on robots? If one method outperforms another in simulation, how likely is that trend to hold in reality on a robot? We examine this question for embodied PointGoal navigation - developing engineering tools and a research paradigm for evaluating a sim

Cited by 252Source ↗Scholar ↗
2017

Learning to navigate cloth using haptics

Alexander Clegg, Wenhao Yu, Zackory Erickson, Jie Tan, C. Karen Liu, Greg Turk
IROS 2017poster

We present a controller that allows an armlike manipulator to navigate deformable cloth garments in simulation through the use of haptic information. The main challenge of such a controller is to avoid getting tangled in, tearing or punching through the deforming cloth. Our controller aggregates for…

Cited by 34Source ↗Scholar ↗
2017

What does the person feel? Learning to infer applied forces during robot-assisted dressing

Zackory Erickson, Alexander Clegg, Wenhao Yu, Greg Turk, C. Karen Liu, Charles C. Kemp
ICRA 2017poster

During robot-assisted dressing, a robot manipulates a garment in contact with a person's body. Inferring the forces applied to the person's body by the garment might enable a robot to provide more effective assistance and give the robot insight into what the person feels. However, complex mechanics…

Cited by 46Source ↗Scholar ↗