Multi-Modal Sensing for Robotic Insertion

Updated 6 August 2025

Multi-modal sensing is the integration of diverse sensors (tactile, force, vision, proprioceptive, acoustic, and proximity) to deliver robust perception and adaptive control during insertion tasks.
Sensor fusion techniques, including deep learning, self-attention, and probabilistic models, enable real-time integration of heterogeneous signals for enhanced accuracy and error recovery.
Applications span industrial assembly, minimally invasive surgery, and laboratory automation, demonstrating significant improvements in precision and efficiency even in unstructured environments.

Multi-modal sensing for robotic insertion refers to the integration of multiple physical sensing modalities—such as tactile, force/torque, proprioceptive, visual, acoustic, and proximity sensing—for robust perception and real-time feedback during insertion tasks. This multi-channel approach addresses the inherent uncertainties, partial observability, and contact-rich dynamics in insertion, ranging from industrial assembly and laboratory automation to minimally invasive surgical procedures. Multi-modal architectures, sensor fusion algorithms, and deep learning enable robots to achieve adaptive, high-precision insertion performance even in unstructured or variable environments.

1. Sensor Modalities and Architectures

Multi-modal insertion systems deploy a heterogeneous mix of sensors, combining various physical principles for comprehensive situational awareness:

Vision: External (third-person) or wrist/fingertip-mounted cameras provide global spatial context and target localization (Liu et al., 2020, Li et al., 2022, Butterworth et al., 2023). Stereo or compound-eye imaging extends visual feedback to 3D pre-contact alignment and near-field depth mapping (Spector et al., 2022, Luo et al., 2023).
Tactile: Vision-based tactile sensors (VBTSs), such as OmniTact’s multi-camera hemispherical module or mini-MagicTac’s layered grid, capture multi-directional surface deformation for local contact geometry and force estimation (Padmanabha et al., 2020, Fan et al., 30 May 2025, Yin et al., 2022).
Force/Torque Sensors: 6-DoF FT sensors at the wrist or proximal end complement tactile readings with absolute force/torque, enabling robust force-controlled insertion, contact event detection, and compliance (Liu et al., 2020, Jin et al., 2022, Liu et al., 2023).
Proprioception: High-rate encoder data, joint positions, and kinematics inform the robot’s intrinsic configuration and enable predictive models for body deformation in compliant or soft robots (Liu et al., 2020, Jin et al., 2022, Donato et al., 5 Apr 2024).
Proximity Sensing: Time-of-Flight (ToF) or IR-proximity sensors deliver pre-contact awareness and enable smooth handovers between visual guidance and contact-control modes (Yin et al., 2022, Fan et al., 30 May 2025).
Auditory and Acoustic Sensing: Contact microphones or spectrogram analysis identify micro-events in manipulation, such as initial touches, material transitions, or tool-workpiece friction (Li et al., 2022).
Specialty Imaging: Multi-modality medical insertion workflows employ robotic ultrasound (US), cone-beam CT (CBCT), and Doppler analysis for fusing anatomical and flow field information (Li et al., 17 Feb 2025).

Centrally, multi-modal sensor architectures seek to maximize spatial coverage (circumferential, hemispherical, or omnidirectional tactile), dynamic range (from occlusion-resistant vision to sub-Newton force), and minimize dead zones or sensor stacking biases, often through single-unit integration (Padmanabha et al., 2020, Fan et al., 30 May 2025, Luo et al., 2023).

2. Sensor Fusion and Deep Learning

Modern insertion platforms apply a variety of fusion strategies—feature-level, decision-level, and hybrid—to integrate signals from these diverse modalities. Common fusion mechanisms and learning architectures include:

Deep Convolutional Neural Networks (CNNs): Directly process high-dimensional images from tactile (and/or visual) sensors, generating embeddings that capture multi-modal features (e.g., deformations, object contours, contact states) (Padmanabha et al., 2020, Spector et al., 2022, Li et al., 2022).
Self-Attention and Transformer Models: Temporal and cross-modal fusion is obtained by dynamically reweighting features over time and across modalities (e.g., MulSA for vision/audio/tactile; Transformer policies for visuotactile insertion) (Li et al., 2022, Azulay et al., 10 Nov 2024).
Behavioral Cloning and Reinforcement Learning: Multi-modal observations serve as input to end-to-end or hierarchical RL agents, which learn contact-rich, adaptive control policies through mixed imitation and policy optimization (e.g., Dreamer-v3, iLQG-MDGPS) (Palenicek et al., 1 May 2024, Lenz et al., 31 Oct 2024, Jin et al., 2022).
Probabilistic Estimation and Factor Graphs: Sensory cues (tactile, proprioceptive) are explicitly fused with prior models via probabilistic factor graphs to infer latent contact configurations, as in active extrinsic contact sensing (Kim et al., 2021).
Physics-Inspired and Information-Theoretic Fusion: Algorithms use filtering (low-pass on F/T), entropy/correlation measures on image channels, and analytic kinematic/force models to reinforce robustness and interpretability in real time (Jin et al., 2022, Fan et al., 30 May 2025, Yin et al., 2022).

A key technical distinction is the choice between early fusion (commingling sensory features before downstream processing) and late fusion (separate processing with decision-level blending)—with advanced architectures often adopting hybrid, multi-level fusion (see (Mohd et al., 2022)).

3. Control Policies and Feedback Integration

Multi-modal sensing is tightly coupled with the generation of real-time feedback and closed-loop control during insertion:

Hybrid Motion/Force Controllers: Controllers combine low-gain joint-level position feedback with operational-space force commands (computed from multi-modal networks) to balance free-space precision and compliant contact (see the hybrid controller formula in (Jin et al., 2022)).
Contact-State Estimation: Fused tactile and proprioceptive signals are used to estimate contact lines, orientation angles, or state variables relevant for insertion (e.g., contact angle error of 1.98° achieved by OmniTact in angled contact estimation) (Padmanabha et al., 2020, Kim et al., 2021).
Multi-Step-Ahead Losses: Behavioral cloning with multi-step-ahead loss functions ensures robust long-horizon trajectory following despite distribution shift, especially when handling uncertainties in target pose (Liu et al., 2020).
Active Sensing and Search: Active tactile or force exploration (rocking, pivoting, oscillatory exploration) is implemented to trigger informative observations, refine pose/confidence estimates, and enable recovery from initial misalignment or occlusion (Kim et al., 2021, Butterworth et al., 2023).
Recovery and Error Correction: Algorithms monitor both contact signals (e.g., drop in Structural Similarity Index) and spatial alignment for prompt initiation of corrective actions, bounded search, or re-grasping (Butterworth et al., 2023, Fan et al., 30 May 2025).

In all modes, tactile and force/proximity signals become critical as vision degrades—e.g., during occlusion by the gripper, poor depth cues, or non-visualized jamming events (Lenz et al., 31 Oct 2024, Fan et al., 30 May 2025).

4. Representative Systems and Empirical Performance

Multi-modal sensing has delivered substantial benefits across diverse robotic insertion contexts, as supported by quantitative results:

System	Modalities	Task/Metric	Performance
OmniTact (Padmanabha et al., 2020)	Multi-camera touch	Connector insertion	80% (top+side cam), 17% (OptoForce)
InsertionNet 2.0 (Spector et al., 2022)	Stereo vision + force	Multi-shape real insertions	>97.5% on 200 trials
MagicGripper (Fan et al., 30 May 2025)	Tactile+proximity+visual	Teleop alignment, autonomous grasp	100% misalignment ID w/ tactile
Multimodal proximity+visuotactile (Yin et al., 2022)	Tactile + ToF proximity	Depth/force tracking, approach	Depth RMSE/task-adaptivity
RL vision+tactile (Lenz et al., 31 Oct 2024)	Vision+touch	Tight-tolerance peg-in-hole	Success preserved under tilt/occl.
CBCT–Ultrasound (Li et al., 17 Feb 2025)	Medical: US/CT	Clinical needle insertion	+50% accuracy/time vs. single-mode

These platforms demonstrate robust generalization to unstructured settings, improved insertion efficiency, and error resilience that is either unattainable or unreliable with single-modal (e.g., vision-only) architectures.

5. Application Domains and Broader Impact

The adoption of multi-modal sensing for robotic insertion addresses challenges in:

Industrial automation: Peg-in-hole, screw, connector, or gear insertions, laboratory vial placement, and precise assembly (Liu et al., 2020, Butterworth et al., 2023, Fan et al., 30 May 2025).
Medical robotics: Needle insertions, tissue manipulation, and autonomous minimally invasive surgery using dual-modality imaging (CBCT-US) and haptic-enabled surgical tools (Liu et al., 2023, Li et al., 17 Feb 2025).
Laboratory automation: Reliable handling and sample transfer using force/tactile/vision fusion in liquid and solid handling tasks (Butterworth et al., 2023).
Service robotics and articulated manipulation: Object re-orientation, joint parameter estimation, and dynamic grip adaptation (Zeng et al., 1 Jul 2024).
Soft robotics: State compression and predictive insertion/planning via generative models, exploiting visual and proprioceptive cues for compliant interactions (Donato et al., 5 Apr 2024, Azulay et al., 10 Nov 2024).

A notable trend is leveraging domain randomization and policy distillation for sim-to-real transfer, enabled by multimodal augmentation and robust preprocessing pipelines (Azulay et al., 10 Nov 2024).

6. Challenges, Limitations, and Future Directions

Challenges persist in synchronization (temporal alignment), handling data heterogeneity and noise, real-time fusion under computational constraints, and scaling to high-dimensional, dynamic tasks (Mohd et al., 2022). Additionally, the cost and compactness of sensor integration, blind spot minimization (e.g., in multi-camera tactile), drift under elastomer creep, and cross-modal domain adaptation for robust sim-to-real transfer remain active research areas.

Future directions include:

Extending to deeper multimodal integration (e.g., audio, electromyography, advanced force/torque arrays) (Li et al., 2022).
Employing generative and self-supervised models for more robust cross-modal completion and missing data recovery (Donato et al., 5 Apr 2024).
Tuning metamorphic testing and physical-aware simulation for reliable benchmarking of multi-modal perception systems (Gao et al., 25 Jan 2024).
Evolving adaptive policies capable of switching sensor/feedback priorities contingent on real-time reliability and occlusion (Lenz et al., 31 Oct 2024, Fan et al., 30 May 2025).

7. Summary

Multi-modal sensing for robotic insertion has enabled substantial advances in the precision, adaptability, and robustness of automation ranging from fine assembly and medical intervention to laboratory and service robotics. By strategically integrating tactile, force, vision, proximity, and ancillary modalities through modern learning and fusion architectures, these systems achieve performance and generalization unattainable by single-modal designs, particularly under uncertainty and contact-rich dynamics. Ongoing challenges focus on deeper sensor integration, domain-adaptive learning, and real-time fusion strategies to further push the boundaries of autonomous robotic manipulation.