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: hardware CAD/BOM and assembly documentation at [https://idscethzurich.github.io/klask_hardware], inference software stack at [https://github.com/IDSCETHZurich/klask_software] and the simulation and MARL setup at [https://github.com/IDSCETHZurich/klask_rl].
Figures & tables
Fig. 1: KlaskTron robot with the 2x 2D gantry for each player
Fig. 2: System architecture of the KlaskTron platform. The environment is interchangeable between the Isaac Lab simulation (for training) and the physical system (for deployment). State estimation in simulation is provided directly by the physics engine; on hardware, an Extended Kalman Filter processes camera observations. The control stack uses PPO during training and the frozen actor network during inference. Actions pass through the learned actuator model in simulation or directly to physical motors on hardware.
Fig. 3: The Isaac Lab simulation environment for KlaskTron. Each agent observes ball and striker positions/velocities and outputs continuous velocity commands.
Fig. 4: Adaptive process noise regions on the KLASK board (left) with corresponding covariance matrices (right). Higher values in Q matrices indicate reduced confidence in the process model, causing the EKF to rely more on measurements.
Fig. 5: Validation of the learned actuator model (10-step history, three hidden layers of 64 units). Solid lines show real hardware response; dashed lines show simulated response to identical command sequences. Shaded bands show the spread across 8 training seeds, indicating that prediction quality is robust to initialisation. The model achieves a global validation RMSE of 0.013m/s , with peak errors of 0.032m/s (x) and 0.049m/s (y) on these representative high-frequency trajectories.
Game
Winner
Duration (s)
Defensive
Offensive
Goal-Directed
Possession
#
Blocks
Strikes
Hits
(%)
1
Human
28
6
4
1
42%
2
Human
35
8
5
2
45%
3
Robot
42
12
9
4
51%
4
Human
21
4
3
1
38%
5
Human
30
7
5
2
44%
TABLE I: 10-Game Human-Robot Tournament Statistics. The agent demonstrates emerging defensive competence but significant offensive limitations.
Parameter
Value
Discount factor ( γ )
0.99
GAE parameter ( λ )
0.95
Learning rate
3×10−4
Clip parameter ( ϵ )
0.2
Mini-batch size
4096
Entropy coefficient
0.01
TABLE II: PPO hyperparameters.
Parameter
Range
Static friction
[0.2,0.5]
Dynamic friction
[0.3,0.45]
Restitution
[0.95,0.99]
Ball mass
[1.8,2.2] mg
TABLE III: Domain randomization ranges (disabled for best agent).