ManiLadder：カテゴリ別・多難易度タスクラダーによるロボットマニピュレーションのベンチマーク
ManiLadder: Benchmarking Robot Manipulation Through a Categorized and Multi-Difficulty Level Task Ladder
112タスクを4難易度・8カテゴリに階層化した大規模シミュレーションベンチマークを構築し、同一条件の模倣学習ポリシーで難易度を校正して実機でも順序性を検証した論文。
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著者: Chongkai Gao, Jieao Shi, Zhaoyu Mu, Yudi Lin, Xuanwei Liu, Wei Ken Ng, Baiye Cheng, Anqi Chen, Kerun Liu, Zhuoran Li, Xuchuan Huang, Haoyang Li, Shengjia Zhu, Nga Teng Chan, Yuxuan Zhang, Xingyu Chen, Lin Shao
分類: cs.RO
原文アブストラクト
Recent advances in robotic manipulation have enabled increasingly complex and dexterous behaviors, yet existing benchmarks still focus on simple pick-and-place tasks and lack a systematic framework for measuring progress in robot learning along the axis of task difficulty. We present ManiLadder, a large-scale and organized simulation benchmark for robotic manipulation. ManiLadder contains 112 carefully designed tasks stratified into 4 difficulty levels, with 8 categories in each level, spanning rigid-body and deformable-object manipulation, single-arm and dual-arm settings, and gripper and dexterous-hand end-effectors. To calibrate difficulty, we fix the data amount the same for every task and use a unified flow- matching imitation learning policy under the same training and evaluation protocols, then use its evaluation success rate as an operational empirical difficulty signal. All tasks are iteratively refined until they fall into target difficulty ranges, which are then verified across different random seeds, policies, and agentic learning systems. We further analyze the task properties, compare different policy performances and training settings, analyze task transfer and data scaling behaviors, and conduct real-world experiments showing that the designed tasks preserve their difficulty ordering beyond simulation. ManiLad- der provides a calibrated ladder for measuring, comparing, and advancing robotic manipulation capabilities. More results are available at https://maniladder-benchmark.github.io/.