该系统实现复杂长时序抓取任务的高精度真实迁移，无需修改算法。
Robotic Sim-to-Real Transfer for Long-Horizon Pick-and-Place Tasks in the Robotic Sim2Real Competition
- 采用轻量抗噪视觉感知与非线性鲁棒伺服系统
- 视觉每帧仅需11毫秒，伺服精度达亚厘米级
- 在ICRA 2024竞赛中获矿物搜索任务第一名
本文提出一种全自主机器人系统，可在包含多个障碍物的复杂环境中完成涉及导航、识别、抓取和堆叠的长时序任务。系统通过轻量级抗噪视觉感知与非线性鲁棒伺服系统，有效克服模拟到现实迁移中的感知与执行差异，实现无需算法修改的稳定表现。在仿真与真实环境中的测试表明，视觉系统每帧处理仅需11毫秒，伺服系统在所提控制器下达到亚厘米级精度，两者在跨域迁移中均表现出高度一致性。凭借上述优势，该系统在ICRA 2024举办的机器人模拟到现实挑战赛的矿物搜索任务中获得第一名。
原文摘要 · Abstract (English)
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.
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