Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration
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. Many parameter directions are weakly observable or unidentifiable, and even when identifiable directions are selected, omitted directions can still influence exploration and distort information measures. To address this challenge, we propose...
| Authors | Youwei Yu, Jionghao Wang, Zhengming Yu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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