A2Perf: Real-World Autonomous Agents Benchmark
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912260188798976 |
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| author | Uchendu, Ikechukwu Jabbour, Jason Berghe, Korneel Van den Runevic, Joel Stewart, Matthew Ma, Jeffrey Krishnan, Srivatsan Gur, Izzeddin Huang, Austin Bishop, Colton Bailey, Paige Jiang, Wenjie Songhori, Ebrahim M. Guadarrama, Sergio Tan, Jie Terry, Jordan K. Faust, Aleksandra Reddi, Vijay Janapa |
| author_facet | Uchendu, Ikechukwu Jabbour, Jason Berghe, Korneel Van den Runevic, Joel Stewart, Matthew Ma, Jeffrey Krishnan, Srivatsan Gur, Izzeddin Huang, Austin Bishop, Colton Bailey, Paige Jiang, Wenjie Songhori, Ebrahim M. Guadarrama, Sergio Tan, Jie Terry, Jordan K. Faust, Aleksandra Reddi, Vijay Janapa |
| contents | Autonomous agents and systems cover a number of application areas, from robotics and digital assistants to combinatorial optimization, all sharing common, unresolved research challenges. It is not sufficient for agents to merely solve a given task; they must generalize to out-of-distribution tasks, perform reliably, and use hardware resources efficiently during training and inference, among other requirements. Several methods, such as reinforcement learning and imitation learning, are commonly used to tackle these problems, each with different trade-offs. However, there is a lack of benchmarking suites that define the environments, datasets, and metrics which can be used to provide a meaningful way for the community to compare progress on applying these methods to real-world problems. We introduce A2Perf--a benchmark with three environments that closely resemble real-world domains: computer chip floorplanning, web navigation, and quadruped locomotion. A2Perf provides metrics that track task performance, generalization, system resource efficiency, and reliability, which are all critical to real-world applications. Using A2Perf, we demonstrate that web navigation agents can achieve latencies comparable to human reaction times on consumer hardware, reveal reliability trade-offs between algorithms for quadruped locomotion, and quantify the energy costs of different learning approaches for computer chip-design. In addition, we propose a data cost metric to account for the cost incurred acquiring offline data for imitation learning and hybrid algorithms, which allows us to better compare these approaches. A2Perf also contains several standard baselines, enabling apples-to-apples comparisons across methods and facilitating progress in real-world autonomy. As an open-source benchmark, A2Perf is designed to remain accessible, up-to-date, and useful to the research community over the long term. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03056 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A2Perf: Real-World Autonomous Agents Benchmark Uchendu, Ikechukwu Jabbour, Jason Berghe, Korneel Van den Runevic, Joel Stewart, Matthew Ma, Jeffrey Krishnan, Srivatsan Gur, Izzeddin Huang, Austin Bishop, Colton Bailey, Paige Jiang, Wenjie Songhori, Ebrahim M. Guadarrama, Sergio Tan, Jie Terry, Jordan K. Faust, Aleksandra Reddi, Vijay Janapa Machine Learning Autonomous agents and systems cover a number of application areas, from robotics and digital assistants to combinatorial optimization, all sharing common, unresolved research challenges. It is not sufficient for agents to merely solve a given task; they must generalize to out-of-distribution tasks, perform reliably, and use hardware resources efficiently during training and inference, among other requirements. Several methods, such as reinforcement learning and imitation learning, are commonly used to tackle these problems, each with different trade-offs. However, there is a lack of benchmarking suites that define the environments, datasets, and metrics which can be used to provide a meaningful way for the community to compare progress on applying these methods to real-world problems. We introduce A2Perf--a benchmark with three environments that closely resemble real-world domains: computer chip floorplanning, web navigation, and quadruped locomotion. A2Perf provides metrics that track task performance, generalization, system resource efficiency, and reliability, which are all critical to real-world applications. Using A2Perf, we demonstrate that web navigation agents can achieve latencies comparable to human reaction times on consumer hardware, reveal reliability trade-offs between algorithms for quadruped locomotion, and quantify the energy costs of different learning approaches for computer chip-design. In addition, we propose a data cost metric to account for the cost incurred acquiring offline data for imitation learning and hybrid algorithms, which allows us to better compare these approaches. A2Perf also contains several standard baselines, enabling apples-to-apples comparisons across methods and facilitating progress in real-world autonomy. As an open-source benchmark, A2Perf is designed to remain accessible, up-to-date, and useful to the research community over the long term. |
| title | A2Perf: Real-World Autonomous Agents Benchmark |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.03056 |