CHALET: Cornell House Agent Learning Environment

Abstract

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.

References in corpus (8)

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• MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments
• Grounded Language Learning in a Simulated 3D World
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Cited by in corpus (24)

• Unity: A General Platform for Intelligent Agents
• ViZDoom Competitions: Playing Doom from Pixels
• Spatial Action Maps for Mobile Manipulation
• The StreetLearn Environment and Dataset
• Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI
• A Survey of Embodied AI: From Simulators to Research Tasks
• Habitat 2.0: Training Home Assistants to Rearrange their Habitat
• Language Tasks and Language Games: On Methodology in Current Natural Language Processing Research
• Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction
• To Learn or Not to Learn: Analyzing the Role of Learning for Navigation in Virtual Environments
• What Should I Do Now? Marrying Reinforcement Learning and Symbolic Planning
• CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning Agents
• Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence
• Sim-Real Joint Reinforcement Transfer for 3D Indoor Navigation
• Are We There Yet? Learning to Localize in Embodied Instruction Following
• SAILenv: Learning in Virtual Visual Environments Made Simple
• SSCNav: Confidence-Aware Semantic Scene Completion for Visual Semantic Navigation
• LUMINOUS: Indoor Scene Generation for Embodied AI Challenges
• ArraMon: A Joint Navigation-Assembly Instruction Interpretation Task in Dynamic Environments
• Explore the Potential Performance of Vision-and-Language Navigation Model: a Snapshot Ensemble Method
• MaAST: Map Attention with Semantic Transformersfor Efficient Visual Navigation
• Game-Based Video-Context Dialogue
• Messing Up 3D Virtual Environments: Transferable Adversarial 3D Objects
• Language coverage and generalization in RNN-based continuous sentence embeddings for interacting agents