Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration

ArXi:2605.12084v1 Announce Type: cross Designing learnable information-theoretic objectives for robot exploration remains challenging. Such objectives aim to guide exploration toward data that reduces uncertainty in model parameters, yet it is often unclear what information the collected data can actually reveal. Although reinforcement learning (RL) can optimize a given objective, constructing objectives that reflect parametric learnability is difficult in high-dimensional robotic systems.