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Reinforcement Learning Within the Classical Robotics Stack: A Case Study in Robot Soccer

2024-12-12 · Adam Labiosa, Zhihan Wang, Siddhant Agarwal, William Cong, Geethika Hemkumar, Abhinav Narayan Harish, Benjamin Hong, Josh Kelle, Chen Li, Yuhao Li, Zisen Shao, Peter Stone, Josiah P. Hanna

Robot decision-making in partially observable, real-time, dynamic, and multi-agent environments remains a difficult and unsolved challenge. Model-free reinforcement learning (RL) is a promising approach to learning decision-making in such domains, however, end-to-end RL in complex environments is often intractable. To address this challenge in the RoboCup Standard Platform League (SPL) domain, we developed a novel architecture integrating RL within a classical robotics stack, while employing a multi-fidelity sim2real approach and decomposing behavior into learned sub-behaviors with heuristic selection. Our architecture led to victory in the 2024 RoboCup SPL Challenge Shield Division. In this work, we fully describe our system's architecture and empirically analyze key design decisions that contributed to its success. Our approach demonstrates how RL-based behaviors can be integrated into complete robot behavior architectures.

📄 PDF Abstract BibTeX arXiv:2412.09417

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Decision Making Reinforcement Learning (RL)

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Data-efficient Deep Reinforcement Learning for Dexterous Manipulation

2017-04-10 · ICLR 2018 1 · Ivaylo Popov, Nicolas Heess, Timothy Lillicrap, Roland Hafner 외

Deep learning and reinforcement learning methods have recently been used to solve a variety of problems in continuous control domains. An obvious application of these techniques is dexterous manipulation tasks in robotic…

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Robust Reinforcement Learning as a Stackelberg Game via Adaptively-Regularized Adversarial Training

2022-02-19 · Peide Huang, Mengdi Xu, Fei Fang, Ding Zhao

Robust Reinforcement Learning (RL) focuses on improving performances under model errors or adversarial attacks, which facilitates the real-life deployment of RL agents. Robust Adversarial Reinforcement Learning (RARL) is…

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On the Computational Complexity of Stackelberg Planning and Meta-Operator Verification: Technical Report

2024-03-26 · Gregor Behnke, Marcel Steinmetz

Stackelberg planning is a recently introduced single-turn two-player adversarial planning model, where two players are acting in a joint classical planning task, the objective of the first player being hampering the seco…

Representation Matters: Improving Perception and Exploration for Robotics

2020-11-03 · Markus Wulfmeier, Arunkumar Byravan, Tim Hertweck, Irina Higgins 외

Projecting high-dimensional environment observations into lower-dimensional structured representations can considerably improve data-efficiency for reinforcement learning in domains with limited data such as robotics. Ca…

Disentanglement

SubTGraph: Large-Scale Subterranean Environment Synthesis with Controllable Topological Variability for Robotic Autonomy Validation

2026-05-20 · F. Labra Caso, A. Saradagi, S. Fredriksson, S. Nordström 외 arxiv

Subterranean (SubT) environments have been a frontier for autonomous robotics, driven by the push for automation of mining operations and the interest in planetary exploration (Martian Lava Tubes). Due to the challenges …

Semantic Segmentation