LocoMuJoCo：移動運動のための包括的模倣学習ベンチマーク
LocoMuJoCo: A Comprehensive Imitation Learning Benchmark for Locomotion
四足・二足・筋骨格モデルを含む多様な環境と実データ・専門家データ・準最適データを備え、模倣学習アルゴリズムを厳密に評価・比較するためのベンチマークを提案した。
著者: Firas Al-Hafez, Guoping Zhao, Jan Peters, Davide Tateo
分類: cs.LG, cs.RO
原文アブストラクト
Imitation Learning (IL) holds great promise for enabling agile locomotion in embodied agents. However, many existing locomotion benchmarks primarily focus on simplified toy tasks, often failing to capture the complexity of real-world scenarios and steering research toward unrealistic domains. To advance research in IL for locomotion, we present a novel benchmark designed to facilitate rigorous evaluation and comparison of IL algorithms. This benchmark encompasses a diverse set of environments, including quadrupeds, bipeds, and musculoskeletal human models, each accompanied by comprehensive datasets, such as real noisy motion capture data, ground truth expert data, and ground truth sub-optimal data, enabling evaluation across a spectrum of difficulty levels. To increase the robustness of learned agents, we provide an easy interface for dynamics randomization and offer a wide range of partially observable tasks to train agents across different embodiments. Finally, we provide handcrafted metrics for each task and ship our benchmark with state-of-the-art baseline algorithms to ease evaluation and enable fast benchmarking.
