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ReSPEC: A Framework for Online Multispectral Sensor Reconfiguration in Dynamic Environments

Yanchen Liu, Yuang Fan, Minghui Zhao, Xiaofan Jiang

TL;DR

ReSPEC introduces a closed-loop framework that directly links multispectral perception with real-time sensor reconfiguration. By extracting modality contribution scores from a detection backbone and feeding them into a reinforcement learning agent, the system dynamically tunes sensor sampling rate, resolution, and sensing range to balance accuracy and efficiency. Empirical results on a SPEC rover show substantial reductions in computational load (≈29.3%) with minimal accuracy degradation (≈5.3%), and a demonstrated ability to adapt sensor usage to challenging conditions such as poor lighting and occlusion. This approach provides a practical pathway toward resource-aware adaptive sensing in embedded robotics with broad applicability beyond detection to tracking, navigation, and multi-robot collaboration.

Abstract

Multi-sensor fusion is central to robust robotic perception, yet most existing systems operate under static sensor configurations, collecting all modalities at fixed rates and fidelity regardless of their situational utility. This rigidity wastes bandwidth, computation, and energy, and prevents systems from prioritizing sensors under challenging conditions such as poor lighting or occlusion. Recent advances in reinforcement learning (RL) and modality-aware fusion suggest the potential for adaptive perception, but prior efforts have largely focused on re-weighting features at inference time, ignoring the physical cost of sensor data collection. We introduce a framework that unifies sensing, learning, and actuation into a closed reconfiguration loop. A task-specific detection backbone extracts multispectral features (e.g. RGB, IR, mmWave, depth) and produces quantitative contribution scores for each modality. These scores are passed to an RL agent, which dynamically adjusts sensor configurations, including sampling frequency, resolution, sensing range, and etc., in real time. Less informative sensors are down-sampled or deactivated, while critical sensors are sampled at higher fidelity as environmental conditions evolve. We implement and evaluate this framework on a mobile rover, showing that adaptive control reduces GPU load by 29.3\% with only a 5.3\% accuracy drop compared to a heuristic baseline. These results highlight the potential of resource-aware adaptive sensing for embedded robotic platforms.

ReSPEC: A Framework for Online Multispectral Sensor Reconfiguration in Dynamic Environments

TL;DR

ReSPEC introduces a closed-loop framework that directly links multispectral perception with real-time sensor reconfiguration. By extracting modality contribution scores from a detection backbone and feeding them into a reinforcement learning agent, the system dynamically tunes sensor sampling rate, resolution, and sensing range to balance accuracy and efficiency. Empirical results on a SPEC rover show substantial reductions in computational load (≈29.3%) with minimal accuracy degradation (≈5.3%), and a demonstrated ability to adapt sensor usage to challenging conditions such as poor lighting and occlusion. This approach provides a practical pathway toward resource-aware adaptive sensing in embedded robotics with broad applicability beyond detection to tracking, navigation, and multi-robot collaboration.

Abstract

Multi-sensor fusion is central to robust robotic perception, yet most existing systems operate under static sensor configurations, collecting all modalities at fixed rates and fidelity regardless of their situational utility. This rigidity wastes bandwidth, computation, and energy, and prevents systems from prioritizing sensors under challenging conditions such as poor lighting or occlusion. Recent advances in reinforcement learning (RL) and modality-aware fusion suggest the potential for adaptive perception, but prior efforts have largely focused on re-weighting features at inference time, ignoring the physical cost of sensor data collection. We introduce a framework that unifies sensing, learning, and actuation into a closed reconfiguration loop. A task-specific detection backbone extracts multispectral features (e.g. RGB, IR, mmWave, depth) and produces quantitative contribution scores for each modality. These scores are passed to an RL agent, which dynamically adjusts sensor configurations, including sampling frequency, resolution, sensing range, and etc., in real time. Less informative sensors are down-sampled or deactivated, while critical sensors are sampled at higher fidelity as environmental conditions evolve. We implement and evaluate this framework on a mobile rover, showing that adaptive control reduces GPU load by 29.3\% with only a 5.3\% accuracy drop compared to a heuristic baseline. These results highlight the potential of resource-aware adaptive sensing for embedded robotic platforms.
Paper Structure (16 sections, 3 equations, 5 figures)

This paper contains 16 sections, 3 equations, 5 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Active and Dynamic Multi-Sensor Perception
  4. Computational Fusion and Sensor Importance
  5. Robust Fusion Under Missing Sensors
  6. Reinforcement Learning for Sensor Configuration
  7. Method
  8. ReSPEC Framework
  9. Modality Contribution Extraction for Detection
  10. RL for Real-time Sensor Reconfiguration
  11. SPEC Rover Platform
  12. Experiments and Analysis
  13. Contribution Estimation Validation
  14. End-to-End Testing on the SPEC Rover
  15. Discussion and Limitations
  16. ...and 1 more sections

Figures (5)

  • Figure 1: Overview of the proposed ReSPEC framework. A configurable suite of heterogeneous sensors (e.g., RGB, IR, LiDAR, radar) feeds data into a task-specific fusion model wrapped with a contribution extractor, which produces both task predictions and per-modality contribution scores. These scores, combined with platform dynamics and scene attributes, define the state for an RL-based reconfiguration agent. The agent then adjusts sensor parameters in real time (e.g., sampling rate, resolution, sensing range), enabling adaptive perception that maintains accuracy while improving efficiency across different tasks and robotic platforms.
  • Figure 2: Implementation of the ReSPEC framework for detection tasks. A configurable suite of heterogeneous sensors (RGB, IR, radar, depth) provides synchronized inputs to a detection backbone composed of two lightweight feature extractors and a mid-level fusion module. A sensor contribution extractor produces per-target and scene-level contribution scores, which are combined with environment and system attributes to define the state of an RL-based reconfiguration agent. The agent dynamically adjusts sensor parameters in real time, closing the loop between perception and sensing control.
  • Figure 3: SPEC rover platform equipped with a multispectral sensor perception module (RGB, thermal, depth, IMU, and mmWave radar) and the experimental environment. The environment includes controllable lighting and occlusion elements to emulate challenging real-world deployment scenarios, with target trajectories defined for evaluation.
  • Figure 4: Per-modality contribution scores across diverse open datasets under varying environmental conditions.
  • Figure 5: End-to-end evaluation of ReSPEC on the SPEC rover platform across controlled in-lab scenarios with varying lighting, target motion, and occlusion. The figure compares adaptive reconfiguration with static (all sensors on) and heuristic (rule-based) baselines, reporting the stabilized actions selected by our system after iterative updates. Results are shown in terms of chosen actions within the defined action space, detection accuracy, and system load. (The FPS cap was 27 instead of 30, as the Jetson Orin Nano reached full load at 27 FPS.)