Embodied Playground：物理・戦略・空間推論を評価する長期的エージェント型ロボティクスベンチマーク
Embodied Playground: A Long-Horizon Agentic Robotics Benchmark for Physical, Strategic, and Spatial Reasoning
基盤モデルエージェントが長期タスクで物理・戦略・空間推論を一貫して行えるかを評価する410環境のベンチマークを提案し、代表100タスクや難易度分割を含む統一評価プロトコルを整備した。
著者: Feng Chen, Pei Zhou, Lixuan He, Yunchao Yao, Li Sun, Shenghua Gao, Mingyu Ding, Kaifeng Zhang, Kaichen Zhou, Yi Ma
分類: cs.RO
原文アブストラクト
Foundation models offer a promising basis for robotic agents, but long-horizon tasks require them to sustain coherent reasoning and reliable physical execution across interdependent stages. We introduce Embodied Playground, a benchmark for evaluating how foundation-model agents coordinate physical, strategic, and spatial reasoning throughout such tasks. Agents must track task progress, satisfy intermediate constraints, and revise their plans using execution feedback as earlier actions shape subsequent decisions. The playground comprises 410 task environments spanning basic manipulation, sequential puzzles, and multi-stage household activities, with variations in planning depth, geometric constraints, and physical execution demands. We curate EP-100, a representative selection of 100 tasks across five suites and 16 task types, together with the EP-Easy and EP-Hard difficulty splits and the compact EP-20 selection. A shared protocol specifies observation and tool access, resource budgets, repeated trials, and independent checks of goal completion, task-rule compliance, and physical validity. Embodied Playground provides a unified framework for studying whether foundation-model agents can sustain effective reasoning and action over extended task horizons. https://winniechen2002.github.io/Agent-bench-website/