Learning-based Optoelectronically Innervated Tactile Finger for Rigid-Soft Interactive Grasping (2101.12379v1)

Abstract: This paper presents a novel design of a soft tactile finger with omni-directional adaptation using multi-channel optical fibers for rigid-soft interactive grasping. Machine learning methods are used to train a model for real-time prediction of force, torque, and contact using the tactile data collected. We further integrated such fingers in a reconfigurable gripper design with three fingers so that the finger arrangement can be actively adjusted in real-time based on the tactile data collected during grasping, achieving the process of rigid-soft interactive grasping. Detailed sensor calibration and experimental results are also included to further validate the proposed design for enhanced grasping robustness.

• The paper proposes a novel soft robotic finger design that integrates multi-channel optical fibers for real-time tactile sensing.
• It employs machine learning techniques to accurately predict force, torque, and contact points during grasping tasks.
• The integration with a reconfigurable gripper enhances adaptability, enabling robust rigid-soft interactive manipulation.

Insights into Learning-based Optoelectronically Innervated Tactile Fingers for Rigid-Soft Interactive Grasping

This paper presents advancements in the design and application of a soft tactile finger that utilizes multi-channel optical fibers and machine learning to enhance grasping interactions between rigid and soft materials. The paper details the development of a soft robotic finger with omni-directional adaptation capabilities, integrating optoelectronic sensors to measure tactile information critical for robust manipulation tasks.

Overview of the Research Contributions

The authors propose a design for a soft robotic finger, employing multi-channel optical fibers to provide tactile data critical for predicting force, torque, and contact points in real time. This novel design integrates the soft fingers into a reconfigurable gripper, enabling dynamic adjustment during grasping tasks, thereby enhancing interaction capabilities between rigid and soft objects. Four key contributions underline the work:

1. Design Integration: A novel design of omni-adaptive soft fingers is introduced, featuring enhanced finger surfaces and optical fibers for tactile sensing, complementing the gripper’s adjustability in real-time grasping contexts.
2. Calibration and Characterization: Detailed calibration techniques and the characterization of the tactile fingers are explored, focusing on machine learning methodologies for accurate tactile data interpretation.
3. Real-time Tactile Sensing: Robust real-time sensing capabilities allow estimation of tactile data such as normal force, torque, and contact positions, which are crucial for manipulation tasks.
4. Grasping Strategies: The integration of tactile fingers with a reconfigurable gripper shows advancements in rigid-soft interactive grasping, permitting the system to adapt finger configurations in real-time based on sensory input, leading to increased grasp robustness and efficiency.

Sensor Design and Methodological Approach

The paper explores the architecture of the tactile finger, where optical fibers are embedded to achieve tactile sensing that informs on the force, torque, and position data. This sensing mechanism supports the real-time optimization of grasping strategies using a learning-based approach, allowing the system to adapt to different grasping scenarios. Machine learning models are leveraged for sensor calibration and prediction tasks, showing significant precision, as demonstrated by low RMSE values for force and torque predictions. The application of optical fibers facilitates detectable changes in luminous intensity correlated with tactile interactions, enabling precise sensoric assessment crucial for interacting with diverse object geometries.

Experimental Results and Implications

Experimental validations reinforce the effectiveness of the proposed tactile sensing approach in both static calibration and dynamic grasping scenarios. The results reveal that the implemented machine learning models provide efficient and low-latency predictions of tactile information, enhancing the system's adaptability to various objects. These findings suggest substantial improvements in manipulation tasks, where traditional rigid-robot systems struggle, particularly in unstructured environments with a spectrum of object shapes and material properties.

Future Speculations and Implications

The paper illuminates significant implications for robotic manipulation, particularly in fields demanding delicate, adaptable interactions, such as in healthcare or precision manufacturing. Future developments may involve further miniaturization of sensor components, refined machine learning algorithms for better tactile prediction, and expanded adaptability for diverse manipulation tasks. The insights gained from this research contribute to the broader discourse on enhancing robotic systems with sensory capabilities akin to biological systems, thereby progressing towards more versatile and capable robotic systems.

This work stands as a pivotal step in shaping how interactive grasping can be realized in soft robotic systems, advocating for continued research into multimodal sensory integration for achieving dexterous, human-like manipulation capabilities.