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ArXiv:
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README.md
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license: apache-2.0
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<p align="center">the first benchmark in embodied simulation for evaluating
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<h1 align="center">EmbodiedCLBench: Evaluating Continual Learning for Self-Evolving Embodied Agents</h1>
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<p align="center">★ Equal contribution. † Corresponding author.</p>
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<p align="center">
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<img src="https://pku-value-lab.github.io/EmbodiedCLBench-homepage/static/images/paper/intro.png" alt="EmbodiedCLBench overview showing compositional generalization from basic to advanced tasks" width="100%">
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</p>
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## Core design
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We operationalize continual learning through compositional generalization: an agent first completes simpler tasks to accumulate experience, then is tested on whether it can recombine the acquired skills to solve more complex, unseen tasks.
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## Abstract
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license: apache-2.0
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<p align="center">the first benchmark in embodied simulation for evaluating<br>
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the continual learning capability of self-evolving agents</p>
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<h1 align="center">EmbodiedCLBench: Evaluating Continual Learning for Self-Evolving Embodied Agents</h1>
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<p align="center">★ Equal contribution. † Corresponding author.</p>
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## Core design
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<p align="center">
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<img src="https://pku-value-lab.github.io/EmbodiedCLBench-homepage/static/images/paper/intro.png" alt="EmbodiedCLBench overview showing compositional generalization from basic to advanced tasks" width="100%">
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</p>
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We operationalize continual learning through compositional generalization: an agent first completes simpler tasks to accumulate experience, then is tested on whether it can recombine the acquired skills to solve more complex, unseen tasks.
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## Abstract
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