Keskeiset käsitteet
HumanoidBenchは、高次元のシミュレートされたロボット学習ベンチマークであり、人間のような形態を活用した複雑な全身制御に関する研究を加速することを目的としています。
Tiivistelmä
I. Introduction to Humanoid Robots:
Humanoid robots offer promise in daily life integration.
Challenges include costly hardware setups and limited whole-body control capabilities.
II. Related Work:
Deep reinforcement learning has shown progress in robotic manipulation and locomotion.
Existing benchmarks focus on specific tasks like picking and placing.
III. Simulated Humanoid Robot Environment:
Utilizes Unitree H1 humanoid robot with dexterous hands in MuJoCo physics engine.
Observations include proprioceptive states, visual inputs, and tactile sensing.
IV. HumanoidBench Tasks:
Consists of 27 tasks including locomotion and manipulation tasks.
Tasks range from simple locomotion to complex manipulation scenarios.
V. Benchmarking Results:
State-of-the-art RL algorithms struggle with complex tasks requiring long-horizon planning and whole-body coordination.
Hierarchical learning approach outperforms flat policies in certain tasks.
VI. Conclusion and Future Work:
HumanoidBench aims to accelerate development of whole-body algorithms for robotic platforms.
Future work includes multi-modal observations, realistic objects, and sim-to-real transfer studies.
Tilastot
HumanoidBenchは15の全身操作タスクと12の移動タスクから成ります。
最新の強化学習アルゴリズムは多くのタスクで苦戦しており、階層的学習アプローチが一部のタスクで優れたパフォーマンスを発揮しています。