Overview
Multimodal tactile sensing technology is an advanced technology that simulates the multimodal perception capability of human skin by integrating various sensors (such as pressure, temperature, texture, vibration sensors, etc.). It enables comprehensive and accurate perception of the physical properties of objects (including hardness, temperature, material, and deformation), and provides environmental interaction and decision support for robots or intelligent systems through signal fusion and intelligent algorithm processing. Its core goal is to allow robots to understand the physical world through "touch" like humans, achieving precise manipulation and adaptation to complex environments.
The realization of multimodal tactile sensing technology relies on three core components: sensor design, multimodal signal fusion, and bionic structure integration. These three components work synergistically to improve perception accuracy and robustness.
1. Multimodal Sensor Design: Simulating the "Perceptual Units" of Human Skin
Human skin achieves multimodal perception through different types of receptors (e.g., Meissner's corpuscles for light touch, Merkel cells for shape perception, and Pacinian corpuscles for vibration detection). Drawing on this mechanism, multimodal tactile sensors integrate multiple sensors for pressure, temperature, texture, and vibration to enable comprehensive detection of object properties:
- Pressure Sensing: Capacitive, piezoresistive, or piezoelectric sensors are adopted to detect normal force (vertical pressure) and tangential force (friction force). Sensitivity (0.212 kPa⁻¹) is enhanced by doping PDMS with carbon nanofibers, enabling accurate measurement of micro-pressure.
- Temperature Sensing: Resistive thermistors (such as serpentine metal thermistors) or thermoelectric materials are used to detect changes in object temperature.
- Texture and Material Sensing: Triboelectric nanogenerators (TENG) or optical sensors (e.g., GelSight) are employed to detect surface texture (e.g., roughness) and material (e.g., plastic, metal) of objects. For instance, TENG identifies materials based on differences in triboelectric signals upon contact, and the ResNet18-1D convolutional neural network can achieve a material classification accuracy of over 90%. GelSight, on the other hand, reconstructs object surface textures using photometric stereo by leveraging the deformation of a soft gel elastomer.
2. Multimodal Signal Fusion: From "Data Fragments" to "Comprehensive Cognition"
Signals from different sensors (e.g., pressure, temperature, texture) suffer from temporal asynchrony and spatial mismatch, requiring signal fusion algorithms to achieve information complementarity. Common fusion strategies include:
- Data-level Fusion: Temporal synchronization and spatial alignment are performed on raw signals (such as voltage values from pressure sensors and current values from TENG). For example, multimodal data are fused using Kalman filtering or deep learning models (e.g., ResNet) to improve perception accuracy.
- Feature-level Fusion: Features of each sensor (e.g., pressure variance, texture frequency spectrum) are extracted and then fused through feature concatenation or attention mechanisms.
- Decision-level Fusion: Decision results from each sensor (e.g., "hard", "soft", "hot") are combined via voting or weighted fusion. For example, a tactile-olfactory integrated manipulator improves recognition accuracy in rescue scenarios by fusing decisions from tactile (material) and olfactory (odor) sensing.
3. Bionic Structure Integration: Making Sensors "Work Like Skin"
The layered structure (epidermis, dermis, subcutaneous tissue) and flexibility of human skin are key to its multimodal perception capability. Multimodal tactile sensors enhance flexibility, stretch resistance, and integration through bionic structural design:
- Layered Structure: A layered design of "substrate - sensitive layer - electrode layer" is adopted.
- Flexibility and Stretchability: Flexible materials (such as PDMS and gelatin-based hydrogels) are used as substrates. For example, the gelatin-based hydrogel skin developed by the University of Cambridge can be melted and reshaped into complex forms (e.g., a human hand) and achieve multimodal perception of pressure and temperature with 1.7 million electrode signals.
- Anti-interference Design: Structural optimization is implemented to reduce environmental interference (e.g., the impact of temperature changes on pressure sensing). For instance, the team led by Yang Lei adopted a 2×2 capacitive array to compensate for in-plane tensile strain, improving the accuracy of pressure measurement.
In recent years, multimodal tactile sensing technology has achieved breakthroughs in high integration, high sensitivity, and multi-scenario adaptability, providing core support for the precise manipulation of robots.
1. High Integration: From "Multi-sensor Stacking" to "Multi-modality with Single Material"
Traditional multimodal sensors require stacking multiple sensors, leading to complex structures and vulnerability to damage. Latest research achieves multimodal perception using a single material, improving integration and durability. For example, simultaneous sensing of pressure and temperature is realized by leveraging changes in electrical conductivity (for pressure) and thermal conductivity (for temperature) of a single material, which can also be repaired by melting and reshaping when damaged.
2. High Sensitivity: From "Macro-perception" to "Micro-signal Detection"
To meet the demand for precise robot manipulation (such as grasping eggs and assembling precision parts), the sensitivity of multimodal tactile sensors has been continuously improved. Some sensors achieve a resolution of 50,000 measurement points per square centimeter (800 times that of human fingers), enabling millimeter-level precise operations (e.g., disassembling a computer case to install a memory module).
3. Multi-scenario Adaptability: From "Laboratory" to "Real World"
The application scenarios of multimodal tactile sensing technology have expanded from industrial automation to medical rehabilitation, post-disaster rescue, service robots, and other fields:
- Industrial Automation: High-precision multi-dimensional tactile sensors enable complex tasks such as grasping light bulbs and operating precision instruments, improving the manipulation accuracy of industrial robots.
- Medical Rehabilitation: For example, tactile-olfactory integrated manipulators achieve 96.9% accuracy in human body identification in buried scenarios through the fusion of tactile (material) and olfactory (odor) sensing, enhancing rescue efficiency.
- Service Robots: By collecting tactile data from over 1,000 real-world objects (such as soft materials and medicine bottles), robots are trained to perform human-like grasping and placing operations.
The application scenarios of multimodal tactile sensing technology are expanding rapidly. Its core value lies in enabling robots to "understand" the physical world and achieve more natural human-machine interaction.
1. Industrial Automation: Core Support for Precision Manipulation
Traditional operations of industrial robots rely on vision (e.g., positioning) and force control (e.g., grasping). However, vision cannot perceive the material hardness of objects (e.g., the difference between glass and plastic), and force control cannot detect micro-vibrations (e.g., loosening during assembly). Through the fusion of pressure (grasping force), temperature (object temperature), and texture (surface roughness), multimodal tactile sensing technology enables:
- Precision Assembly: For example, when assembling a mobile phone screen, pressure sensors control the grasping force (to avoid crushing), and texture sensors detect the curvature of the screen edge (to adjust posture), improving assembly yield.
- Hazardous Environment Operations: In environments such as nuclear radiation and high temperatures, robots judge the state of objects through tactile perception (e.g., temperature, vibration), avoiding human exposure to dangers.
2. Medical Rehabilitation: A Key Tool for Precise Operations
Medical robots (such as surgical robots and rehabilitation robots) require high-precision tactile feedback to perform minimally invasive surgeries (e.g., laparoscopic surgery) or precise rehabilitation (e.g., prosthetic control):
- Surgical Robots: For example, the da Vinci Surgical System uses tactile sensors to perceive tissue hardness (e.g., the difference between tumors and normal tissues), helping doctors adjust surgical operations (such as cutting force) and reducing damage to surrounding tissues.
- Rehabilitation Robots: For instance, tactile feedback in prosthetics uses pressure sensors to perceive the contact force between the prosthetic and the ground (to adjust gait) and temperature sensors to detect ambient temperature (to remind users to keep warm), improving the comfort and practicality of prosthetics.
3. Service Robots: A Bridge for Natural Interaction
Service robots (such as household robots and restaurant robots) need to understand human needs and achieve more natural human-machine interaction through tactile perception:
- Household Robots: When grasping a glass, for example, pressure sensors control the grasping force (to prevent slipping), texture sensors detect the material of the glass (e.g., the difference between ceramic and plastic), and temperature sensors perceive the temperature of the glass (to remind users to be careful of burns), achieving human-like services.
- Restaurant Robots: When delivering meals, tactile sensors perceive the temperature of the dinner plate (to avoid scalding customers) and pressure sensors detect the weight of the plate (to adjust walking speed), improving service efficiency and safety.
Despite significant progress, multimodal tactile sensing technology still faces challenges such as signal crosstalk, system integration, and intelligent algorithms. Future development should focus on higher integration, stronger intelligence, and broader application scenarios.
1. Current Challenges
- Signal Crosstalk: Signals from different sensors (e.g., pressure and temperature) may interfere with each other. For example, temperature changes can affect the capacitance value of pressure sensors, leading to measurement errors.
- System Integration: The integration of multimodal sensors needs to address issues of physical space (e.g., sensor layout) and signal processing (e.g., fusion of multi-source data), increasing system complexity.
- Intelligent Algorithms: Existing algorithms mostly rely on supervised learning, which requires a large amount of labeled data (e.g., the correspondence between tactile data and object properties), limiting the generalization ability of the algorithms.
2. Future Development Directions
- Higher Integration: Achieve "skin-like" sensors through single-material multimodality (e.g., gelatin-based hydrogels) or flexible electronic technologies (e.g., printed electronics), improving integration and durability.
- Stronger Intelligence: Reduce reliance on labeled data and improve algorithm generalization ability through unsupervised learning (e.g., autoencoders) or reinforcement learning (e.g., robots learning tactile feedback through trial and error).
- Broader Application Scenarios: Expand from industrial applications to medical, service, post-disaster rescue, and other fields, such as developing wearable tactile sensors (e.g., tactile feedback for prosthetics) or environmental tactile sensors (e.g., monitoring building structures after earthquakes).
Multimodal tactile sensing technology is the key to enabling robots to achieve "human-like intelligence". Through multimodal sensor design, signal fusion, and bionic structure integration, it realizes comprehensive perception of object properties. With continuous technological breakthroughs (e.g., high integration, high sensitivity), its application scenarios will expand from industrial automation to medical rehabilitation, service robots, and other fields, bringing more efficient and safer human-machine interaction experiences to human society. In the future, it is necessary to address challenges such as signal crosstalk and system integration to promote the development of the technology toward greater intelligence and popularization.