TY - GEN
T1 - Robotic Sim-to-Real Transfer for Long-Horizon Pick-and-Place Tasks in the Robotic Sim2Real Competition
AU - Yang, Ming
AU - Cao, Hongyu
AU - Zhao, Lixuan
AU - Zhang, Chenrui
AU - Chen, Yaran
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - This paper presents a fully autonomous robotic system that performs sim-to-real transfer in complex longhorizon 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 nonlinearityrobust servo system are adopted. We conduct a series of tests in both simulated and realworld 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.
AB - This paper presents a fully autonomous robotic system that performs sim-to-real transfer in complex longhorizon 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 nonlinearityrobust servo system are adopted. We conduct a series of tests in both simulated and realworld 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.
UR - https://www.scopus.com/pages/publications/105016665662
U2 - 10.1109/ICRA55743.2025.11128837
DO - 10.1109/ICRA55743.2025.11128837
M3 - Conference Proceeding
AN - SCOPUS:105016665662
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 902
EP - 908
BT - 2025 IEEE International Conference on Robotics and Automation, ICRA 2025
A2 - Ott, Christian
A2 - Admoni, Henny
A2 - Behnke, Sven
A2 - Bogdan, Stjepan
A2 - Bolopion, Aude
A2 - Choi, Youngjin
A2 - Ficuciello, Fanny
A2 - Gans, Nicholas
A2 - Gosselin, Clement
A2 - Harada, Kensuke
A2 - Kayacan, Erdal
A2 - Kim, H. Jin
A2 - Leutenegger, Stefan
A2 - Liu, Zhe
A2 - Maiolino, Perla
A2 - Marques, Lino
A2 - Matsubara, Takamitsu
A2 - Mavromatti, Anastasia
A2 - Minor, Mark
A2 - O'Kane, Jason
A2 - Park, Hae Won
A2 - Park, Hae-Won
A2 - Rekleitis, Ioannis
A2 - Renda, Federico
A2 - Ricci, Elisa
A2 - Riek, Laurel D.
A2 - Sabattini, Lorenzo
A2 - Shen, Shaojie
A2 - Sun, Yu
A2 - Wieber, Pierre-Brice
A2 - Yamane, Katsu
A2 - Yu, Jingjin
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 IEEE International Conference on Robotics and Automation, ICRA 2025
Y2 - 19 May 2025 through 23 May 2025
ER -