Mechanosensory Control of Locomotion in Animals and Robots: Moving Forward

Abstract

While animals swim, crawl, walk, and fly with apparent ease, building robots capable of robust locomotion remains a significant challenge. In this review, we draw attention to mechanosensation-the sensing of mechanical forces generated within and outside the body-as a key sense that enables robust locomotion in animals. We discuss differences between mechanosensation in animals and current robots with respect to (1) the encoding properties and distribution of mechanosensors and (2) the integration and regulation of mechanosensory feedback. We argue that robotics would benefit greatly from a detailed understanding of these aspects in animals. To that end, we highlight promising experimental and engineering approaches to study mechanosensation, emphasizing the mutual benefits for biologists and engineers that emerge from moving forward together.

Fig. 1

Mechanosensory control of locomotion in animals. (A) Animal mechanosensors come in many shapes and forms. Embedded deep within the musculoskeletal system, they detect external and internal forces important for controlling locomotion. Top left: cerebrospinal fluid contacting neuron in the central canal of a larval zebrafish encoding axial bending in the spinal cord through coupling to an internal acellular thread called the Reissner fiber (top view, adapted from Wu et al. 2021). Bottom left: chordotonal neurons in a Drosophila larva encoding muscle stretch (adapted from Agrawal and Tuthill 2022). Top right: muscle spindle in a mouse leg encoding muscle stretch. Bottom right: campaniform sensillum on a Drosophila wing encoding aerodynamic forces. (B) Across species and types of locomotion, mechanosensory feedback informs locomotor circuits in the central nervous system at multiple levels and timescales. Mechanosensory feedback mediates reflexes, shapes internal locomotor rhythms, and contributes to action selection and state estimation. Feedback is flexibly regulated to meet behavioral demands. Control diagram adapted from Dickinson et al. (2000).