Sensory feedback control


Earlier model-based control studies of octopus arm movement typically lack the integration of sensory information into the motor control. How octopuses use their sensing capabilities to control their arms remains an open question.

We propose a novel sensory feedback control law for octopus arms. The control law is inspired by several behavioral observations and biophysical experiments:

Sucker sensing

Bend propagation

Muscular arm with sensor model

The arm is actuated by its intricate internal musculature. For simplicity, we consider a planar, inextensible, and unshearable arm. Then, the muscle actuation only requires two longitudinal muscles -- one on the top, and another on the bottom. When these muscles contract, they locally bend the arm.

The sensory information includes the bearing angle $\alpha$ between the target vector $\boldsymbol{\rho}$ and the tangent vector $\mathsf{a}$, and the arc-length $\bar{s}$ of the closest point to the target.

Schematic of the sensorimotor control

Theoretical contribution

Simulation Results


This control law can only track a moving target instead of catching it due to the property of asymptotic stability


Regular session TuBT11 - Distributed Parameter System II from 2022 Conference on Decision and Control - Cancun, Mexico, Dec. 6-9, 2022.


T. Wang, U. Halder, E. Gribkova, M. Gazzola, and P.G., Mehta, "A sensory feedback control law for octopus arm movements," in 2022 Conference on Decision and Control (CDC). IEEE, 2022, pp. 1059-1066 . 


Financial support from ONR MURI N00014-19-1-2373, NSF/USDA #2019-67021-28989, and NSF EFRI C3 SoRo #1830881.