Multimodal Active Sensing

• Multimodal active sensing is a framework that integrates varied sensor modalities and actively selects optimal sensing strategies to maximize information gain.
• It employs probabilistic fusion, reinforcement learning, and optimization techniques to merge heterogeneous data from vision, touch, LiDAR, and RF sources.
• Experimental benchmarks show significant improvements in localization, mapping, and object recognition by efficiently balancing sensor selection under resource constraints.

Multimodal active sensing refers to the principled integration and active control of multiple distinct sensing modalities—such as vision, touch, ranging, proprioception, or radio-frequency probes—to optimize information gathering, perception, and inference in dynamic or uncertain environments. Rather than relying on passive, pre-scheduled data collection, such systems autonomously select sensing actions (including sensor selection, placement, probing parameters, and viewpoint) using task-driven optimization criteria, often maximizing information gain or minimizing estimation uncertainty. This paradigm leverages the complementary strengths of each modality, fuses heterogeneous observations via probabilistic models or neural architectures, and formulates the control of sensing as a constrained, usually sequential, decision process.

1. Foundational Principles and Formalisms

Core to multimodal active sensing is the representation of the sensor-task relationship and its formalization as an optimization problem. For tasks such as inferring latent environmental variables or characterizing physical systems, the system must:

• Represent domain knowledge and observation models through graphical or causal constructs, such as Bayesian networks encoding dependencies among latent states, sensor measurements, and observable proxies (Arora et al., 2017).
• Quantify an objective function for action planning, typically the expected information gain, formulated as the expected reduction in Shannon entropy $I(Z_{seq}) = H(L)-H(L|Z_{seq})$, over latent quantities of interest $L$ given multimodal observation sequences $Z_{seq}$.
• Constrain planned actions under resource budgets, including energy, time, bandwidth, or explicit sensing/communication action limits, often via constraints such as $\sum_{i} \mathrm{cost}(a_i) \le S$ (Arora et al., 2017), or average rate constraints in communication-aware deployments (Zakeri et al., 15 May 2025, Zakeri et al., 3 Nov 2025).

This mechanism yields an interleaving of perception, planning, and control, with active selection and scheduling of the modalities themselves as key decision variables.

2. Sensor Modality Types and Integration Strategies

Multimodal active sensing leverages a heterogeneous suite of sensors, including:

• Vision (RGB, depth, point cloud): High spatial resolution but limited in occluded or visually ambiguous settings. Used in articulated object recognition (Zeng et al., 1 Jul 2024), human mesh recovery (Maeda et al., 2023), and visuotactile end-effectors (Yin et al., 2022).
• Proprioceptive and tactile: Contact-based and/or pressure measurements enhance manipulation, disambiguate grasps and correct for occlusions or calibration drift (Park et al., 2018, 1809.03216, Yin et al., 2022).
• Ranging (LiDAR, ToF, Radar): Provides geometric and distance cues; LiDAR used for body-part localization (Maeda et al., 2023), radar integrated in vision–RF fusion for positioning (Peng et al., 26 Jun 2025).
• RF Sensing/Communication: In mmWave and 6G systems, active RF probing (e.g., beam-sweeping, radar-like) and passive channel estimation are fused for SLAM and beamforming (Yang et al., 2022, Zakeri et al., 15 May 2025, Zakeri et al., 3 Nov 2025, Peng et al., 26 Jun 2025).
• Hybrid configurations: Visuotactile with optical, IR, and pressure combined via custom elastomeric interfaces to sense both proximity and physical contact (Yin et al., 2022).

Integration is achieved by probabilistic fusion—via message passing in Bayesian networks (Arora et al., 2017), variational inference (Maeda et al., 2023), deep neural feature fusion (e.g., transformer-based architectures) (Zeng et al., 1 Jul 2024), or hard gating logic in resource-constrained embedded controllers (Park et al., 2018). Real-time data alignment (spatio-temporal registration) and uncertainty modeling are central components.

3. Action Selection and Active Sensing Policies

Active sensing algorithms plan sensing actions—viewpoint, modality, or probing sequence—based on expected informativeness or uncertainty reduction:

• Monte Carlo Tree Search (MCTS): Used to approximate the optimal policy in environments with large observation and action spaces, balancing exploration and exploitation (Arora et al., 2017). Rollouts simulate the evolution of belief over latent variables under candidate sensor-action sequences, with rewards proportional to normalized information gain.
• MDP/Formal RL formulations: Reinforcement learning agents select informative next viewpoints or sensor configurations based on learned or model-based value functions. In articulated object perception, state $s_t$ consists of multimodal feature representations; actions correspond to discrete permissible viewpoints; rewards are task-driven (perception score improvement, coverage gain) (Zeng et al., 1 Jul 2024, Tran et al., 1 Jul 2024).
• Uncertainty-driven heuristics: In human mesh recovery, the system quantifies predictive variance per body vertex and targets camera or touch/2D-LiDAR actions to maximize reduction of localized uncertainty, subject to kinematic constraints (Maeda et al., 2023).
• Drift-plus-penalty/Lyapunov optimization: In communications-aware systems, action selection is cast as queue-stabilizing optimization, balancing the value of fresh sensing against resource constraints via virtual queues and dynamic per-slot optimization (Zakeri et al., 15 May 2025, Zakeri et al., 3 Nov 2025).

Multi-robot and swarm settings instantiate this as coordinated state-machine models: e.g., fixed-phase switching from initial coverage (exploration) to focused, entropy-driven information gathering (Tran et al., 1 Jul 2024).

4. Sensor Fusion, Uncertainty, and Inference Architectures

Fusion architectures are designed to optimally combine diverse data streams:

• Graphical models: Sensor dependencies and causal relationships (e.g., camera, UV, neutron—each weakly or strongly linked to latent geology) encoded in Bayesian networks, with belief updates via message passing or sampling (Arora et al., 2017).
• Deep multimodal encoders: Adaptive weighting and feature fusion of image and point cloud data is achieved via architectures such as ResNet–PointNet backbones plus attention modules and transformers, supporting joint estimation of object/joint parameters, movability, and confidence scores (Zeng et al., 1 Jul 2024).
• Sensor fusion optimization: Measurement likelihoods from camera, touch, and LiDAR are integrated at the level of global offset correction and local pose refinement, with uncertainties propagated, leading to statistically grounded inference (Maeda et al., 2023).
• Semantic communication and multi-agent fusion: In 6G ISAC scenarios, measure-to-semantic-token encoding with multi-head attention fusion (fusion-based ISAC) achieves substantial accuracy gains over single-modality baselines (Peng et al., 26 Jun 2025).

Models account for sensor-specific noise, cross-modal correlations, and priors over scene structure or task-relevant attributes.

5. Computational Complexity and Scalability

The challenge of combinatorial action/observation spaces is addressed via algorithmic innovations:

• Sample-based planning: MCTS and related sampling-based planners have per-iteration complexity $O(H \cdot T_L)$, where $T_L$ is message passing cost per update, independent of the total observation history (Arora et al., 2017).
• Belief propagation: Loopy BP and particle-based message representation for joint estimation of agent pose and environment features (Yang et al., 2022).
• Anytime properties: Many planners are anytime in nature; the resulting policies improve with longer planning windows or more computation, but maintain bounded per-step cost.
• Resource–performance trade-off: Explicit budgeted sensing is engineered via constraints on number of active sensing actions or modality selection rate, with performance (e.g., SNR, reconstruction accuracy) characterized as a function of available resources (Zakeri et al., 15 May 2025, Zakeri et al., 3 Nov 2025, Peng et al., 26 Jun 2025).
• Distributed paradigms: F-MAC, I-MAC, and R-MAC architectures in ISAC yield varied complexity, communication overhead, and network-level robustness (Peng et al., 26 Jun 2025).

6. Experimental Benchmarks and Empirical Outcomes

Extensive simulation and hardware experiments demonstrate the efficacy of multimodal active sensing:

• Field robotics: Mars-analogue exploration with Bayesian-MCTS planning yields statistically significant improvement in information gain and recognition score over random or greedy coverage; hardware rover achieves ~13% more information gain and ~5% better recognition (Arora et al., 2017).
• Multi-robot environmental mapping: Hybrid coverage–active-sensing swarms attain 43% reduced turnaround time, 50% higher estimation accuracy, and 5× lower localization error vs. single-mode or classic active sensing strategies (Tran et al., 1 Jul 2024).
• Articulated object perception: Transformer-based multimodal fusion with RL-driven viewpoint selection achieves up to 65% reduction in orientation error and robust real-world transfer from simulated training, with RL policies delivering ∼50% drop in the hardest joint-state errors (from 100% normalized baseline) (Zeng et al., 1 Jul 2024).
• Human mesh recovery in pHRI: Active camera/touch selection plus fusion reduce mean MPJPE by 20–30% vs. baselines, even with severe occlusion; in real-robot settings, errors decrease from ~572 mm (camera-only) to ~275 mm (camera + active touch + LiDAR) (Maeda et al., 2023).
• Integrated communication–sensing: Drift-plus-penalty control in mmWave systems restricts SNR loss to <8% under halved sensing budgets, while AoI-aware DRL policies in beam prediction raise top-1/top-3 accuracy by 44.16% and 52.96% under strict constraints (Zakeri et al., 15 May 2025, Zakeri et al., 3 Nov 2025).
• Multimodal visuotactile sensing: The fusion of tactile and proximity information under dynamic manipulation scenarios enables accurate pre-, during-, and post-contact inference, with <1 mm depth error at 50 mm for white surfaces and contact detection down to ~0.1 N (Yin et al., 2022).

7. Architectural Paradigms and Open Research Directions

System-level paradigms for multimodal active sensing have been formalized:

• Fusion-based, interaction-based, and relay-based ISAC (F-MAC, I-MAC, R-MAC): Each balances centralization, peer communication, and relay adaptation for resource-efficient multimodal integration in networks (Peng et al., 26 Jun 2025).
• Enabling technologies: Large AI models (transformer-based fusion), semantic communication (proto-symbolic information exchange for reduced bandwidth), and distributed multi-agent orchestration are advancing the field.
• Open challenges: Adaptive modality selection (dynamic sensor scheduling), online edge learning (update of sensor/agent parameters in situ), privacy/security in semantic fusion, and dynamic robustness to sensor dropout remain core active research areas (Peng et al., 26 Jun 2025).

A plausible implication is that continued progress in scalable fusion architectures, uncertainty-driven action selection, and hierarchical resource-aware control will further expand the applicability of multimodal active sensing across field robotics, intelligent transportation, human-robot interaction, and integrated sensing-communication systems.

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References:

(Arora et al., 2017, Yang et al., 2022, Tran et al., 1 Jul 2024, Zeng et al., 1 Jul 2024, Maeda et al., 2023, Zakeri et al., 15 May 2025, Zakeri et al., 3 Nov 2025, 1809.03216, Park et al., 2018, Peng et al., 26 Jun 2025, Yin et al., 2022)