Robotics: Science and Systems XXII

KlaskTron: An Open-Source Platform for Physical Adversarial Multi-Agent RL

Aswin Karthik Ramachandran Venkatapathy, Jona Schulz, Maurus Derungs, Carlo Angelini, Tobias Meier, Raffaello D'andrea

Abstract:

Progress in robot learning, particularly physical multi-agent reinforcement learning (MARL), is currently challenged by a limited availability of accessible, standardized benchmarks. While simulation-based MARL has produced remarkable emergent behaviors from coordinated team play to complex tool use, translating these advances to physical systems remains difficult. A key barrier is infrastructural: physical platforms are often prohibitively expensive, require specialized facilities, or lack open mechanisms and designs for reproducibility. We introduce KlaskTron, an open-source, low-cost (<$2500 USD), desktop-scale robotic testbed for adversarial MARL based on the dynamic tabletop game KLASK. The platform features dual CoreXY gantries with high-torque brushless motors, enabling the high-speed, precise manipulation required for competitive play. Crucially, we provide a complete ecosystem: hardware designs (CAD, BOM), a GPU-native digital twin in NVIDIA Isaac Lab for high-fidelity modeling and simulation, and a validated sim-to-real baseline using a learned neural actuator model. We demonstrate that this baseline enables zero-shot policy transfer, with trained agents exhibiting emergent adversarial behaviors. To ensure full reproducibility of our results, all project assets are released publicly: documentation at [Anonymized Link], hardware CAD/BOM at [Anonymized Link], and software/simulation at [Anonymized Link].

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Bibtex:

  
@INPROCEEDINGS{VenkatapathyA-RSS-26, 
    AUTHOR    = {Aswin Karthik Ramachandran Venkatapathy AND Jona Schulz AND Maurus Derungs AND Carlo Angelini AND Tobias Meier AND Raffaello D'andrea}, 
    TITLE     = {{KlaskTron: An Open-Source Platform for Physical Adversarial Multi-Agent RL}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2026}, 
    ADDRESS   = {Sydney, Australia}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2026.XXII.038} 
}