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

2026-05-12

AI Summary

This work addresses the challenge of distorted information-theoretic exploration objectives in high-dimensional robotic systems, where most parameter directions are weakly observable or unidentifiable. To resolve this, the authors propose Quasi-Optimal Experimental Design (QOED), which introduces optimal experimental design to robotic exploration for the first time. QOED leverages eigenspace analysis of the Fisher information matrix to identify the observable subspace, adaptively focusing exploration on identifiable parameter directions while amplifying informative dimensions and suppressing irrelevant ones in the exploration objective. This approach yields a constant-factor approximation to the ideal information gain. Empirical results demonstrate significant performance improvements—35.23% in simulation and 21.98% in real-world navigation and manipulation tasks—substantially outperforming existing reinforcement learning baselines.

Abstract

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 Quasi-Optimal Experimental Design (Q{\footnotesize OED}), an adaptive information objective grounded in optimal experimental design. Q{\footnotesize OED} (i) performs eigenspace analysis of the Fisher information matrix to identify an observable subspace and select identifiable parameter directions, and (ii) modifies the exploration objective to emphasize these directions while suppressing nuisance effects from non-critical parameters. Under bounded nuisance influence and limited coupling between critical and nuisance directions, Q{\footnotesize OED} provides a constant-factor approximation to the ideal information objective that explores all parameters. We evaluate Q{\footnotesize OED} on simulated and real-world navigation and manipulation tasks, where identifiable-direction selection and nuisance suppression yield performance improvements of \SI{35.23}{\percent} and \SI{21.98}{\percent}, respectively. When integrated as an exploration objective in model-based policy optimization, Q{\footnotesize OED} further improves policy performance over established RL baselines.

Problem

• robot exploration
• information-theoretic objectives
• parameter identifiability
• Fisher information matrix
• optimal experimental design

Innovation

• Quasi-Optimal Experimental Design
• Fisher Information Matrix
• Identifiable Parameters
• Nuisance Suppression
• Information-Theoretic Exploration