Robots Learn to Feel: AI-Powered Sensing Detects Hidden Structures

by

Author: Denis Avetisyan

A new robotic system combines tactile sensing and reinforcement learning to autonomously locate and characterize embedded objects with greater precision than manual methods.

This work demonstrates a dynamic tactile sensing system coupled with the Soft Actor Critic algorithm for improved inclusion characterization, with potential applications in breast tumor detection and material science.

Accurate characterization of embedded inclusions often relies on manual techniques prone to subjective error and limited precision. This is addressed in ‘Dynamic Tactile Sensing System and Soft Actor Critic Reinforcement Learning for Inclusion Characterization’, which details a robotic system integrating tactile sensing with reinforcement learning-specifically, the Soft Actor Critic algorithm-to autonomously locate and characterize these inclusions. Experimental results demonstrate that this system achieves size estimation errors of 2.61% and 5.29% for soft and hard inclusions, respectively-outperforming expert human operators-and can determine mechanical properties without direct observation. Could this approach pave the way for more reliable and automated solutions in applications like non-invasive breast tumor characterization and industrial quality control?

Beyond Visualization: Sensing the Unseen in Material Integrity

Conventional methods for detecting inclusions within materials frequently depend on imaging technologies such as X-ray computed tomography or ultrasound; however, these techniques can struggle to discern minute variations in mechanical properties at the inclusion’s boundary. While capable of visualizing structural defects, they often lack the sensitivity to characterize subtle changes in stiffness, density, or texture-characteristics crucial for assessing the integrity and potential failure points of a composite material. This limitation is particularly pronounced when inclusions are small, have similar densities to the surrounding matrix, or present gradual transitions in material properties, rendering them difficult to differentiate using purely visual cues. Consequently, a need exists for complementary or alternative detection strategies that directly probe the mechanical response of the material, offering a more nuanced understanding of inclusion characteristics.

Current methods for detecting inclusions within materials often depend on visual inspection, which struggles with subtle variations in mechanical properties and can be time-consuming. Consequently, researchers are increasingly turning to robotic tactile sensing as a powerful alternative. This approach utilizes sensitive robotic “skin” to directly map the physical characteristics of a structure – things like stiffness, texture, and density – without relying on visual data. By systematically exploring a material’s surface, these robotic systems build a detailed tactile profile, allowing for the identification of even minute inclusions or structural anomalies that would otherwise go unnoticed. This direct assessment of physical properties promises a more robust and efficient means of inclusion detection, opening doors to improved quality control and enhanced material characterization across various industries.

Successfully deploying robotic tactile sensing for inclusion detection hinges on overcoming significant computational hurdles related to data interpretation. While a robotic system can gather extensive surface information, pinpointing subtle anomalies indicative of embedded inclusions requires more than simple data acquisition; it demands automated exploration strategies and intelligent algorithms. Researchers are actively developing methods to filter noise, correlate tactile readings with known inclusion characteristics, and efficiently map the inspected volume – essentially teaching robots to ‘feel’ for differences imperceptible to traditional imaging. The ability to autonomously navigate surfaces, adapt to varying material properties, and rapidly analyze complex tactile data streams is crucial for scaling this technology beyond laboratory settings and into real-world applications like non-destructive testing and advanced manufacturing.

Dynamic Sensing: A System for Mapping Material Landscapes

The Dynamic Tactile Sensing System employs a dual-arm robotic platform integrated with a Tactile Imaging Sensor to systematically explore defined areas. This sensor suite captures spatial data regarding contact forces and textures as the robot manipulates and interacts with the region of interest. The dual-arm configuration allows for increased manipulation dexterity and the ability to apply force from multiple points simultaneously, facilitating more comprehensive data acquisition. The robot’s movements are not pre-programmed but are dynamically adjusted based on sensor input, enabling the system to adapt to varying surface geometries and material properties within the exploration space.

Dynamic Interrogation is the core data acquisition process within the system, functioning as a two-stage approach to surface exploration. Initially, Coarse Interrogation is utilized to broadly scan the region of interest, establishing a preliminary understanding of the environment and identifying potential features. Following this initial scan, the system transitions to Fine Interrogation, concentrating on specific areas identified during the coarse phase to achieve precise localization and detailed characterization of surface properties. This staged process optimizes data collection efficiency by prioritizing thorough investigation of relevant areas while minimizing redundant scanning of the overall workspace.

The system’s exploration strategy is optimized through reinforcement learning, utilizing the Soft Actor Critic (SAC) algorithm. SAC is an off-policy actor-critic method designed for continuous action spaces, enabling the robot to learn an optimal policy for tactile exploration. The algorithm maximizes a reward function that balances exploration and exploitation, while also incorporating an entropy term to encourage diverse behaviors and prevent premature convergence on suboptimal solutions. This approach allows the robot to adapt its probing actions – force, velocity, and direction – based on tactile feedback, efficiently mapping the surface of interest and localizing features without explicit programming of movement trajectories. The learned policy is continuously refined through interaction with the environment, improving the robot’s ability to perform dynamic tactile sensing tasks.

Robot Firmware serves as the central control mechanism for the Dual-Arm Robot, managing the coordinated operation of all hardware components including motors, sensors, and the tactile imaging system. This firmware utilizes real-time operating system (RTOS) principles to ensure deterministic timing and responsiveness critical for dynamic tactile sensing. Specifically, it handles trajectory planning, motor control loops, sensor data acquisition, and communication protocols, synchronizing these functions to enable precise and repeatable movements during both coarse and fine interrogation procedures. The firmware also incorporates error handling and safety features to prevent collisions and ensure stable operation throughout the exploration process, and is designed to interface directly with the reinforcement learning algorithm for action execution.

From Surface Feel to Data Insights: Decoding Tactile Signatures

The Tactile Imaging Sensor utilizes Total Internal Reflection (TIR) to detect subtle changes in surface topography. When light travels from a high refractive index medium (the sensor’s transparent material) to a lower one (the object being scanned), it typically refracts. However, at sufficiently steep angles of incidence, the light undergoes total internal reflection, remaining within the higher index medium. Surface deformations disrupt this TIR, causing variations in the reflected light intensity which are captured by a camera array. These intensity variations directly correlate to the degree of surface deformation, providing the raw pixel intensity data used for subsequent analysis and feature extraction. The system is sensitive to deformations on the order of micrometers, allowing for the detection of small inclusions and surface irregularities.

The Soft Actor Critic (SAC) algorithm utilizes pixel intensity values derived from tactile images as input for reinforcement learning. These pixel intensities represent the visual data captured by the tactile sensor and are processed to determine the robot’s actions. A specifically designed reward function is central to the SAC implementation; it assigns numerical values to different robot behaviors, encouraging exploration of areas with higher potential for inclusion detection. This reward function guides the robot to systematically investigate regions of interest, effectively optimizing its search pattern based on the tactile data received and maximizing the probability of identifying and characterizing inclusions within the target surface.

The Deformation Index (DI) is calculated by the system as a quantitative measure of surface stiffness, directly correlating to the characteristics of inclusions within the scanned material. This index is derived from analyzing the degree of deformation observed in the tactile images when the sensor makes contact with the surface; higher values indicate greater stiffness, suggesting the presence of a hard inclusion, while lower values indicate a softer, more pliable inclusion or no inclusion at all. The DI is not an absolute value of stiffness but rather a relative measurement, normalized against the material properties of the surrounding substance to provide a discernible difference between inclusions and the base material. The calculated DI values are then used as primary inputs for subsequent size estimation and overall inclusion characterization processes.

Size estimation is performed by analyzing the pixel area occupied by identified inclusions within the tactile images, providing a direct measurement of their two-dimensional extent. Overall inclusion characterization combines the size estimation with the previously calculated Deformation Index – a measure of material stiffness – to create a profile for each detected inclusion. This profile includes both dimensional data, expressed in pixel units which can be calibrated to real-world measurements, and a stiffness value indicating the relative hardness or softness of the inclusion compared to the surrounding material. This combined data allows for differentiation between inclusion types and assessment of their impact on the overall material properties.

Towards Clinical Translation: Mapping Risk and Refining Characterization

The Dynamic Tactile Sensing System moves beyond simple inclusion detection by providing detailed characterization, which then fuels the calculation of a quantitative Risk Score. This score isn’t merely a flag for anomalies; it’s a nuanced assessment built upon precise measurements of inclusion size, stiffness, and boundary characteristics. By integrating these parameters, the system offers a means to differentiate between benign tissues – such as cysts or fibroadenomas – and potentially malignant formations. A higher Risk Score indicates a greater likelihood of malignancy, prompting further investigation, while lower scores may suggest a benign condition, potentially reducing the need for invasive biopsies. This capability aims to provide clinicians with an objective, data-driven tool to improve diagnostic accuracy and refine patient management strategies, ultimately leading to earlier and more effective interventions.

The system’s ability to pinpoint and analyze tissue anomalies builds upon established techniques, notably an advancement of Bayesian Embedded Object Detection Mapping. This methodology was refined to improve the precision with which inclusions – potential indicators of disease – are located within the scanned area. By integrating prior knowledge about the expected characteristics of these inclusions with real-time tactile data, the system effectively narrows the search space and enhances the accuracy of object characterization. This iterative process, combining probabilistic modeling and sensor feedback, allows for a more robust and reliable identification of subtle changes in tissue density and stiffness, ultimately improving the system’s diagnostic capabilities and laying the groundwork for clinical application.

Rigorous validation of tactile sensing systems requires materials that realistically simulate complex biological tissues; therefore, a Polydimethylsiloxane (PDMS) phantom was developed to closely mimic the mechanical properties of breast tissue. This phantom allows for controlled experiments and precise calibration of the Dynamic Tactile Sensing System, enabling researchers to assess its performance in a standardized and repeatable manner. By embedding inclusions of varying stiffness within the PDMS, the system’s ability to detect and characterize these anomalies – representing potential tumors – can be thoroughly tested independently of patient-specific variability. The phantom’s tunability allows for the creation of diverse tissue models, offering a valuable platform for refining algorithms and ensuring the system’s reliability before clinical translation, ultimately improving the accuracy of non-invasive diagnostic tools.

The Dynamic Tactile Sensing System demonstrates a marked advancement in inclusion size estimation accuracy. Rigorous testing revealed the system achieves errors of just 2.61% when assessing soft inclusions and 5.29% for harder ones – a substantial improvement over expert human operators, who averaged errors of 7.84% and 6.87% respectively. This heightened precision suggests the system’s ability to reliably quantify subtle variations in tissue density, potentially enabling earlier and more accurate identification of anomalies. The reduced error rate isn’t merely incremental; it indicates a capability to discern features beyond the scope of manual palpation, offering a promising avenue for improved diagnostic tools and patient care.

The Dynamic Tactile Sensing System exhibits a substantially improved capacity for data interpretation, as evidenced by its Discrimination Index (DI) Ratio of 2.77. This metric quantifies the system’s ability to reliably differentiate between distinct tissue characteristics, significantly exceeding the DI Ratio of 1.04 achieved through manual data assessment. A higher DI Ratio indicates a more robust and precise analytical capability, suggesting the system not only gathers tactile information but also processes it with a level of discernment that surpasses human performance. This enhanced data processing is crucial for accurate inclusion characterization and risk assessment, paving the way for a more objective and potentially earlier detection of breast cancer.

The development of a non-invasive diagnostic approach for breast cancer holds considerable promise for improving patient outcomes through earlier and more accurate detection. Current methods often rely on biopsies, which, while definitive, are invasive and can cause discomfort or complications. This dynamic tactile sensing system offers a potential alternative, utilizing mechanical property mapping to characterize tissue anomalies without the need for surgical intervention. By differentiating between benign and malignant inclusions based on their physical characteristics, the system aims to reduce false positives and unnecessary biopsies, leading to faster diagnosis and treatment. Furthermore, the enhanced data processing capabilities, as evidenced by the improved DI Ratio and size estimation accuracy, suggest a more reliable and objective assessment compared to manual examination, ultimately contributing to more informed clinical decision-making and potentially increasing survival rates.

The system detailed within demonstrates a fascinating emergence of capability. Much like a forest evolves without a forester, yet follows rules of light and water, this robotic sensing platform achieves inclusion characterization through localized interactions – the tactile sensing and reinforcement learning algorithms acting as the governing principles. It’s not about imposing control, but enabling the system to discover mechanical properties, mirroring how nature finds equilibrium. As Immanuel Kant observed, “All our knowledge begins with the senses.” This research doesn’t simply replicate human assessment; it refines it through a systematic, data-driven approach, allowing for more nuanced and accurate detection of inclusions-a testament to how understanding fundamental principles can yield emergent intelligence.

Future Directions

The pursuit of automated inclusion characterization, as demonstrated in this work, inevitably bumps against the limits of pre-programmed directives. The system excels by learning to sense, yet the true complexity of materials – particularly biological tissues – resists complete formalization. Future iterations will likely find greater benefit from embracing this inherent unpredictability. Rather than striving for perfect “knowledge” of inclusion properties, the focus should shift towards algorithms that skillfully navigate uncertainty, maximizing information gain with each tactile interaction.

The current paradigm relies on reinforcement learning to refine sensing strategies. However, complex systems don’t require central control; they thrive on local rules. A more robust approach may involve distributing intelligence across the tactile array itself, allowing for emergent behavior and adaptive exploration without top-down orchestration. This necessitates investigation into bio-inspired sensing modalities and decentralized processing architectures, moving beyond the limitations of current actuator-sensor feedback loops.

Ultimately, the goal isn’t to replicate human expertise, but to augment it. The system’s capacity for precise, repeatable measurements offers a valuable complement to clinical assessment. Further research should explore how these robotic sensing capabilities can be integrated into existing diagnostic workflows, not as a replacement for trained professionals, but as a tool to enhance their judgment and improve patient outcomes. The system’s resilience will be tested not in controlled environments, but in the messy reality of clinical application.

Original article: https://arxiv.org/pdf/2601.16061.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

See also:

2026-01-23 14:21