ロボットSim2Realチャレンジにおける長期的ピックアンドプレースのためのSim-to-Real転移
Robotic Sim-to-Real Transfer for Long-Horizon Pick-and-Place Tasks in the Robotic Sim2Real Competition
軽量なノイズ耐性視覚認識と非線形性に頑健なサーボ系を組み合わせ、アルゴリズム変更なしでシミュレーションから実機への転移を実現し、ICRA 2024のRobotic Sim2Real Challengeで優勝した。
著者: Ming Yang, Hongyu Cao, Lixuan Zhao, Chenrui Zhang, Yaran Chen
分類: cs.RO
原文アブストラクト
This paper presents a fully autonomous robotic system that performs sim-to-real transfer in complex long-horizon tasks involving navigation, recognition, grasping, and stacking in an environment with multiple obstacles. The key feature of the system is the ability to overcome typical sensing and actuation discrepancies during sim-to-real transfer and to achieve consistent performance without any algorithmic modifications. To accomplish this, a lightweight noise-resistant visual perception system and a nonlinearity-robust servo system are adopted. We conduct a series of tests in both simulated and real-world environments. The visual perception system achieves the speed of 11 ms per frame due to its lightweight nature, and the servo system achieves sub-centimeter accuracy with the proposed controller. Both exhibit high consistency during sim-to-real transfer. Benefiting from these, our robotic system took first place in the mineral searching task of the Robotic Sim2Real Challenge hosted at ICRA 2024.
