MuJoCo MPCによるヒューマノイド制御：HumanoidBenchでの評価
MuJoCo MPC for Humanoid Control: Evaluation on HumanoidBench
MuJoCo MPCを用いてヒューマノイド全身制御ベンチマークHumanoidBenchに取り組み、スパース報酬の問題を正則化項で解決し、一部タスクで最高スコアと自然な姿勢・滑らかな制御を両立した。
著者: Moritz Meser, Aditya Bhatt, Boris Belousov, Jan Peters
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
We tackle the recently introduced benchmark for whole-body humanoid control HumanoidBench using MuJoCo MPC. We find that sparse reward functions of HumanoidBench yield undesirable and unrealistic behaviors when optimized; therefore, we propose a set of regularization terms that stabilize the robot behavior across tasks. Current evaluations on a subset of tasks demonstrate that our proposed reward function allows achieving the highest HumanoidBench scores while maintaining realistic posture and smooth control signals. Our code is publicly available and will become a part of MuJoCo MPC, enabling rapid prototyping of robot behaviors.
