We are thrilled to share the incredible news that researchers from ETH Zurich have developed a robotic system that can solve a real-world labyrinth game using reinforcement learning! As detailed in their study “Sample-Efficient Learning to Solve a Real-World Labyrinth Game Using Data-Augmented Model-Based Reinforcement Learning,” this incredible AI-powered robot mastered the BRIO labyrinth game in just five hours of training data, outperforming any known previous attempts.
The BRIO labyrinth game, which might be familiar to some, is a challenging test of fine motor skills and spatial reasoning that requires players to navigate a steel ball through a maze by tilting the playfield. Despite its apparent simplicity, the game is complex due to the relationship between the ball and walls, surface irregularities, and nonlinear control knob dynamics. These challenges make the labyrinth ideal for applying and evaluating state-of-the-art robotic learning methods.
The ETH Zurich team, led by Thomas Bi and Professor Raffaello D’Andrea, developed a method that extracts efficient observations from the maze using camera images. The AI’s learning process is based on model-based reinforcement learning, using a reward function defined by progress through the labyrinth. After training, the AI robot successfully navigated the labyrinth with a 76% success rate and an average completion time of 15.73 seconds. This is slightly better than the best human record of 15.95 seconds!
This research is an incredible step forward in applying AI to dynamic, real-world environments. The ETH Zurich team plans to open-source their project, believing that their system could serve as a valuable real-world benchmark for further AI research due to its low space requirements, modest cost, and simple hardware setup. We can’t wait to see how this exciting new technology will be used to enable efficient, intelligent robotic systems that autonomously handle complex real-life tasks. What an incredible breakthrough!
