Extended Data Fig. 2: MBRL Model Training and Adaptation.
From: Model-based reinforcement learning for ultrasound-driven autonomous microrobots
a. Simulated Randomized Environment Complexity: Displays six images, each at a different time step, showing the increasing complexity of simulated randomized environments with obstacles (black) and paths (white). The microrobot (red) navigates towards the target (blue) through these evolving challenges. b. Generalization Across Environments: Depicts adaptation over 12 environments, including 10 original plus 2 newly introduced randomized environments, culminating in a 70% success rate in a novel testing environment, highlighting effective generalization. Solid lines show the exponentially weighted moving average (EWMA, α = 0.01) of success rates, with shaded areas indicating ±½ of the rolling standard deviation (window = 50 steps). A red dashed line marks the environment transition. The final box plot shows the post-adaptation performance, with boxes representing the 25th–75th percentile and whiskers extending to 1.5×IQR.
