Eric Undersander — accepted AI-conference 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 5 Source ↗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 74 Source ↗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 111 Source ↗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 53 Source ↗PDF ↗Scholar ↗

2023

Galactic: Scaling End-to-End Reinforcement Learning for Rearrangement at 100k Steps-per-Second

Vincent-Pierre Berges, Andrew Szot, Devendra Singh Chaplot, Aaron Gokaslan, Roozbeh Mottaghi, Dhruv Batra +1

CVPR 2023poster

We present Galactic, a large-scale simulation and reinforcement-learning (RL) framework for robotic mobile manipulation in indoor environments. Specifically, a Fetch robot (equipped with a mobile base, 7DoF arm, RGBD camera, egomotion, and onboard sensing) is spawned in a home environment and asked…

Cited by 8 Source ↗PDF ↗code ↗Scholar ↗

2022

Efficient and Interpretable Robot Manipulation With Graph Neural Networks

Yixin Lin, Austin S. Wang, Eric Undersander, Akshara Rai

RA-L 2022

Manipulation tasks, like loading a dishwasher, can be seen as a sequence of spatial constraints and relationships between different objects. We aim to discover these rules from demonstrations by posing manipulation as a classification problem over a graph, whose nodes represent task-relevant entitie

Cited by 48 Source ↗Scholar ↗

2022

Habitat-Web: Learning Embodied Object-Search Strategies From Human Demonstrations at Scale

Ram Ramrakhya, Eric Undersander, Dhruv Batra, Abhishek Das

CVPR 2022poster

We present a large-scale study of imitating human demonstrations on tasks that require a virtual robot to search for objects in new environments - (1) ObjectGoal Navigation (e.g. 'find & go to a chair') and (2) Pick&Place (e.g. 'find mug, pick mug, find counter, place mug on counter'). First, we dev…

Cited by 117 PDF ↗code ↗Scholar ↗

2022

Transformers Are Adaptable Task Planners

Vidhi Jain, Yixin Lin, Eric Undersander, Yonatan Bisk, Akshara Rai

CoRL 2022poster

Every home is different, and every person likes things done in their particular way. Therefore, home robots of the future need to both reason about the sequential nature of day-to-day tasks and generalize to user's preferences. To this end, we propose a Transformer Task Planner (TTP) that learns hig…

Cited by 28 Source ↗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 579 Source ↗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 434 Source ↗code ↗Scholar ↗