Object Classification Utilizing Neuromorphic Proprioceptive Signals in Active Exploration: Validated on a Soft Anthropomorphic Hand (2505.17738v1)

Abstract: Proprioception, a key sensory modality in haptic perception, plays a vital role in perceiving the 3D structure of objects by providing feedback on the position and movement of body parts. The restoration of proprioceptive sensation is crucial for enabling in-hand manipulation and natural control in the prosthetic hand. Despite its importance, proprioceptive sensation is relatively unexplored in an artificial system. In this work, we introduce a novel platform that integrates a soft anthropomorphic robot hand (QB SoftHand) with flexible proprioceptive sensors and a classifier that utilizes a hybrid spiking neural network with different types of spiking neurons to interpret neuromorphic proprioceptive signals encoded by a biological muscle spindle model. The encoding scheme and the classifier are implemented and tested on the datasets we collected in the active exploration of ten objects from the YCB benchmark. Our results indicate that the classifier achieves more accurate inferences than existing learning approaches, especially in the early stage of the exploration. This system holds the potential for development in the areas of haptic feedback and neural prosthetics.

Insights into Object Classification through Neuromorphic Proprioceptive Signals

The paper "Object Classification Utilizing Neuromorphic Proprioceptive Signals in Active Exploration: Validated on a Soft Anthropomorphic Hand" presents a significant paper in the integration of neuromorphic models and robotic platforms for object classification. Specifically, it focuses on exploiting neuromorphic proprioceptive signals in the domain of active exploration using a soft anthropomorphic hand prototype developed by Fengyi Wang et al., highlighting its application in robotics and prosthetic technology.

The methodology details the pairing of stretch sensors with a QB SoftHand robotic platform. This configuration seeks to simulate human-like proprioceptive sensation, crucial for precise in-hand manipulation. Notably, the paper advances the application of spiking neural networks (SNNs) for processing encoders' spike train outputs, derived from a biological muscle spindle model. This model accommodates the intrinsic properties related to muscle length changes and their temporal derivatives, thus precisely encoding proprioceptive data.

Testing was performed using ten objects from the Yale-CMU-Berkeley (YCB) benchmark to demonstrate the system's effectiveness. The results underscore the superiority of the SNN-based classifier over prevalent machine learning algorithms, notably in the initial phases of data acquisition, where quick feedback is indispensable. Early-stage classification accuracy reached 65% swiftly, showcasing faster and more efficient decision-making capabilities compared to conventional approaches like LSTM and kNN.

Functionally, the hybrid SNN architecture utilized in this paper combines resonator and integrator neurons to improve classification. This design facilitates proficient processing of spike-encoded proprioceptive feedback, thus achieving enhanced inferential outcomes with lower energy requirements—particularly when deployed on neuromorphic hardware platforms like Loihi.

In terms of implications, these advancements underscore a crucial shift in how robotic systems interact with their environments, moving towards more human-like perception and response strategies. This has profound practical implications in prosthetic limb development, where restoring natural proprioceptive function is paramount for users’ integration of the device into daily activities. Moreover, these systems may markedly improve robotic handling and manipulation, fostering advancements in autonomous robotics.

Future work should investigate pose-invariance and augment these proprioceptive perceptions with tactile data for a comprehensive sensorimotor approach. The pursuit of enriched adaptive exploration strategies will also enhance the versatility and precision of robotic systems, ushering in a new era of neuromorphic robotics. Such strides would not only advance technical capabilities but also enhance our understanding of the underlying cognitive mechanisms mimicked in robotic architectures. The integration of these features could unlock broader applications in areas such as assistive technology, automated assembly, and exploratory robotics.