RobotAgentBench：Antigravity CLI（Gemini 4 Argon）+ InternVLA-A

Biwei Huang @huang_biwei原推发布于 2026.10.28

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Robots can now perform an increasingly wide range of manipulation tasks. But how far are we from robot assistants that can operate autonomously in the open world? We introduce RobotAgentBench (RAB), a comprehensive benchmark for evaluating open-world robot autonomy in realistic household environments. We evaluate 8 VLA models, 3 WAMs, and diverse robot agent harness designs across five dimensions: task discovery, context reasoning, manipulation, failure recovery, and robustness. Four open-ended household scenarios test how these capabilities work together to fulfill high-level user needs. What we find: - Manipulation ≠ autonomy. Standalone VLA/WAM policies struggle with task discovery and open-ended scenarios. Agent harnesses substantially improve performance, but reliable open-world autonomy remains challenging. - Hybrid Code-VLA/WAM Policy performs best. Combining learned policies with code-based control achieves the strongest performance among evaluated configurations. - RSI further improves robot agents. Refining reusable tools and accumulating experience lead to additional gains across iterations. - Best couple under our default agent setting: Antigravity CLI (Gemini 4 Argon) + InternVLA-A1.5. This pairing performs best among the planner-executor combinations we evaluated, achieving an open-ended score of 10.4, compared with 8.4 for fully code-based Codex (GPT-6 Astra) and 1.1 for standalone π0.5.