RoboARCade: Benchmarking Agentic Robot Control
Overview
This research introduces RoboARCade, a benchmark designed to evaluate agentic robot control across long-horizon manipulation tasks in three different embodiments.
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Environment: simulation
“nchmarks, which we make harder by widening the range of initial conditions. RoboARCade is easy to use and extend: all tasks run on a single simulation backend, MuJoCo, each task is a self-contained data pack, and agents connect through interchangeable interfaces, so new tasks, agents, and interfaces can be added independently. The benchmark separates leading agents: GPT-6 Astra achieves 49.8% compared with 43.6% for”
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Artifact availability · code: reported pending
“e without human input: one successful demonstration in context raises Astra's score from 53.5% to 73.0% on a five-task subset, and a prompt the agent writes and revises itself reaches 72.0%. Code, videos and full traces are available at roboarcade.org.”
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- arXiv Robotics — research abstractsSignalRoboARCade: Benchmarking Agentic Robot Control
This research introduces RoboARCade, a benchmark designed to evaluate agentic robot control across long-horizon manipulation tasks in three different embodiments.
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