Year: 2026
Type: conference-paper
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
Source: Robotics: Science and Systems
Institutions: ETH Zurich, Institute for Dynamic Systems and Control
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7204777077