Active Sensing Subserves Task Control

---
name: active-sensing-subserves-task-control
description: "Proposes that active sensing (energy expenditure for information) is not driven by sensory goals but is necessary for task-level control. Integrates empirical data and control theory to explain explore-exploit mode switching in biological sensorimotor systems. Use when researching active sensing, sensorimotor control, control theory in neuroscience, explore-exploit tradeoffs, or bio-inspired robotics."
arxiv_id: "2605.22988"
published: "2026-05-21"
authors: ["Andrew Lamperski", "Debojyoti Biswas", "Eric S. Fortune", "John Guckenheimer", "Kathleen Hoffman", "Noah J. Cowan"]
tags: [active sensing, sensorimotor control, control theory, explore-exploit, bio-inspired robotics, feedback control, adaptive sensors]
---

# Active Sensing Subserves Task-Level Control

**arXiv:2605.22988** | Submitted 21 May 2026 | q-bio.NC, cs.LG, cs.RO, eess.SY

## Overview

Traditional definitions treat active sensing as energy expenditure for information acquisition. This paper inverts that view: **active sensing is not driven by sensory goals but is a necessary consequence of task-level control** in systems with adaptive sensors and sensorimotor coupling.

## Key Contributions

### 1. Active sensing as a control necessity
- Reliance on adaptive sensors + linkage between movement and sensing + task-level control → active sensing movements emerge inevitably
- Active sensing is not about minimizing uncertainty — it's required for control

### 2. Explore-exploit mode switching
- Animals switch between two behavioral modes with distinct control policies:
  - **Explore mode**: dynamic movements to shape sensory feedback
  - **Exploit mode**: slower compensatory movements directly related to task goals
- These discrete epochs of active sensing are interspersed with goal-oriented behavior

### 3. Biological vs. engineered systems
- Engineered systems outperform animals on cost functions (force, precision, speed)
- Yet animals achieve robust, graceful behaviors unmatched by engineering
- Current engineered control systems are **insufficient** — bio-inspired approach may be critical

## Mathematical Formulation

- Control-theoretic framework expressed in terms of observability, controllability
- Adaptive sensor models with state-dependent measurement properties
- Mode-switching control policies (explore/exploit)

## Interdisciplinary Relevance

- **Neuroscience**: explains why active sensing movements occur in biological systems
- **Robotics (cs.RO)**: implications for robotic sensing and control architecture design
- **Machine Learning (cs.LG)**: explore-exploit framework for reinforcement learning
- **Control Systems (eess.SY)**: challenges conventional assumptions about sensor design

## Activation

- **Keywords**: active sensing, sensorimotor control, control theory, explore-exploit, bio-inspired robotics, feedback control, adaptive sensors