| 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
