Haptic Perception via the Dynamics of Flexible Body Inspired by an Ostrich's Neck

TL;DR

This work shows that the dynamics of a bio-inspired flexible musculoskeletal system can serve as a physical reservoir for haptic perception, enabling rapid learning and real-time softness classification without heavy computation. By integrating a tendon-driven neck similar to an ostrich, the system exploits viscoelasticity and morphology to amplify subtle environmental differences while preserving memory across cycles. ESP stability, separability of reaction-force trajectories, and haptic memory emerge under carefully chosen input speeds and body properties, with heterogeneity (via cranial/caudal regions and ligaments) further boosting performance. The approach yields fast, on-line inference and reduces computational load by leveraging the body’s memory, offering a path toward emergent functions in soft robotics through embodied intelligence and morphological computation.

Abstract

In biological systems, both skin sensitivity and body flexibility play crucial roles in haptic perception. Fully soft robots often suffer from structural fragility and delayed sensory processing, limiting their practical functionality. The musculoskeletal system combines the adaptability of soft materials with the durability of rigid-body robots. It also leverages morphological computation, where the morphological structures contribute to information processing, for dynamic and adaptive behaviors. This study focuses on the pecking behaviors of birds, which enables precise haptic perception through the musculoskeletal system of their flexible neck. Physical reservoir computing is applied to flexible structures inspired by an ostrich neck to analyze the relationship between haptic perception and physical characteristics. Experiments with both a physical robot and simulations reveal that, with appropriate viscoelasticity, the flexible structure can discriminate object softness and retain that information through behavior. Drawing on these findings and anatomical insights from the ostrich neck, a haptic perception system is proposed that exhibits both separability and behavioral memory in flexible structures, enabling rapid learning and real-time inference. The results demonstrate that through the dynamics of flexible structures, diverse functions can emerge beyond their original design as manipulators.

Figures (8)

• Figure 1: Overviews haptic perception via the dynamics of bio-inspired flexible manipulator: (a) RobOstrich: A tendon-driven flexible manipulator based on the anatomy of an ostrich's neck (b) haptic perception through physical reservoir computing utilizing the dynamics of RobOstrich (c) dynamic properties of the external environment in physical simulations and actual robot experiments (d) experimental setup in actual robot
• Figure 2: Definition of the accuracy curve and exploration of the distribution of learning performance based on the accuracy curve: (a) data acquisition method (b) time-multiplexing in training and evaluation (c) exploring learning performance with respect to input and physical parameters
• Figure 3: Distribution of learning performance with respect to input and body parameters: (a) relationship between input and body parameters under high performance conditions (b) behaviors and reaction force under high performance conditions
• Figure 4: Behavior that enables learning and the corresponding viscoelasticity of the body: (a) comparison of heat maps for ESP index and learning performance (b) differences in behavior due to variations in initial conditions
• Figure 5: Adaptive behavior that amplifies the differences in reaction forces and the corresponding viscoelasticity of the body: (a) region of Low Learning Performance where ESP holds (White Frame) (b) separation of reaction force time series in response to differences in objects observed after the collision (c) relationship between physical characteristics and the time series of reaction force (d) changes in learning performance due to adjustment of body parameters in actual robot experiments