Efficient and Reactive Planning for High Speed Robot Air Hockey
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Abstract (arXiv)
Highly dynamic robotic tasks require high-speed and reactive robots. These tasks are particularly challenging due to the physical constraints, hardware limitations, and the high uncertainty of dynamics and sensor measures. To face these issues, it's crucial to design robotics agents that generate precise and fast trajectories and react immediately to environmental changes. Air hockey is an example of this kind of task. Due to the environment's characteristics, it is possible to formalize the problem and derive clean mathematical solutions. For these reasons, this environment is perfect for pushing to the limit the performance of currently available general-purpose robotic manipulators. Using two Kuka Iiwa 14, we show how to design a policy for general-purpose robotic manipulators for the air hockey game. We demonstrate that a real robot arm can perform fast-hitting movements and that the two robots can play against each other on a medium-size air hockey table in simulation.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Jul 2021.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 5; 2025: 1.
8 papers.
- 2024Safe Reinforcement Learning on the Constraint Manifold: Theory and Applicationscited by 12
- 2023Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networkscited by 8
- 2022PUCK: Parallel Surface and Convolution-kernel Tracking for Event-Based Camerascited by 4
- 2024Robot Air Hockey: A Manipulation Testbed for Robot Learning with Reinforcement Learningcited by 4
- 2024Bridging the gap between Learning-to-plan, Motion Primitives and Safe Reinforcement Learningcited by 4
- 2024Learning Diverse Robot Striking Motions with Diffusion Models and Kinematically Constrained Gradient Guidancecited by 4
- 2024A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-world Roboticscited by 1
- 2025Robotic Skill Diversification via Active Mutation of Reward Functions in Reinforcement Learning During a Liquid Pouring Taskcited by 0
- 2025Robotic Skill Diversification via Active Mutation of Reward Functions in Reinforcement Learning During a Liquid Pouring Task22 Sep 2025
- 2024A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-world Robotics8 Nov 2024
- 2024Learning Diverse Robot Striking Motions with Diffusion Models and Kinematically Constrained Gradient Guidance23 Sep 2024
- 2024Bridging the gap between Learning-to-plan, Motion Primitives and Safe Reinforcement Learning26 Aug 2024
- 2024Robot Air Hockey: A Manipulation Testbed for Robot Learning with Reinforcement Learning6 May 2024
- 2024Safe Reinforcement Learning on the Constraint Manifold: Theory and Applications13 Apr 2024
- 2023Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks11 Jan 2023
- 2022PUCK: Parallel Surface and Convolution-kernel Tracking for Event-Based Cameras16 May 2022