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CHALET: Cornell House Agent Learning Environment

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arxiv 1801.07357 v2 pith:HIE5QQ56 submitted 2018-01-23 cs.AI

CHALET: Cornell House Agent Learning Environment

Claudia Yan , Dipendra Misra , Andrew Bennnett , Aaron Walsman , Yonatan Bisk , Yoav Artzi This is my paper
classification cs.AI
keywords chalethouseenvironmentcreateincludingobjectsactionsactivities
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We present CHALET, a 3D house simulator with support for navigation and manipulation. CHALET includes 58 rooms and 10 house configuration, and allows to easily create new house and room layouts. CHALET supports a range of common household activities, including moving objects, toggling appliances, and placing objects inside closeable containers. The environment and actions available are designed to create a challenging domain to train and evaluate autonomous agents, including for tasks that combine language, vision, and planning in a dynamic environment.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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

    cs.CV 2021-09 accept novelty 8.0

    HM3D offers 1000 building-scale 3D environments that are larger and higher-fidelity than existing datasets, enabling better-performing embodied AI agents for tasks like PointGoal navigation.

  2. LLawCo: Learning Laws of Cooperation for Modeling Embodied Multi-Agent Behavior

    cs.LG 2026-06 unverdicted novelty 6.0

    LLawCo extracts misaligned behavioral patterns from agent failures to derive laws, incorporates them via SFT into LLM reasoning, and reports 4.5% and 6.8% success rate gains on PARTNR-Dialog and TDW-MAT benchmarks.

  3. To Learn or Not to Learn: Analyzing the Role of Learning for Navigation in Virtual Environments

    cs.CV 2019-07 unverdicted novelty 4.0

    Classical agents outperform learning-based ones on MINOS and Stanford 3D Indoor Spaces, with learned agents weaker at collision avoidance and memory but stronger at handling ambiguity and noise.