Hierarchical Control of Visually-Guided Movements in a 3D-Printed Robot Arm

Abstract

The control architecture guiding simple movements such as reaching toward a visual target remains an open problem. The nervous system needs to integrate different sensory modalities and coordinate multiple degrees of freedom in the human arm to achieve that goal. The challenge increases due to noise and transport delays in neural signals, non-linear and fatigable muscles as actuators, and unpredictable environmental disturbances. Here we examined the capabilities of hierarchical feedback control models proposed by W. T. Powers, so far only tested in silico. We built a robot arm system with four degrees of freedom, including a visual system for locating the planar position of the hand, joint angle proprioception, and pressure sensing in one point of contact. We subjected the robot to various human-inspired reaching and tracking tasks and found features of biological movement, such as isochrony and bell-shaped velocity profiles in straight-line movements, and the speed-curvature power law in curved movements. These behavioral properties emerge without trajectory planning or explicit optimization algorithms. We then applied static structural perturbations to the robot: we blocked the wrist joint, tilted the writing surface, extended the hand with a tool, and rotated the visual system. For all of them, we found that the arm in machina adapts its behavior without being reprogrammed. In sum, while limited in speed and precision (by the nature of the do-it-yourself inexpensive components we used to build the robot from scratch), when faced with the noise, delays, non-linearities, and unpredictable disturbances of the real world, the embodied control architecture shown here balances biological realism with design simplicity.

Visuomotor phase-locked loop reproduces elliptic hand trajectories across different rhythms

“…The difficulty with this explanation is that low-pass filters distort the input signal by introducing amplitude modulation and phase lags – filtered trajectories in Schaal and Sternad (2001), Gribble and Ostry (2003) and Matic et al (2021) all show that output ellipses have a different size than input ellipses. Participants in experiment 1 generally maintained the size of the cursor trajectory equal to that of the target’s trajectory, and followed the targets without phase lags – similar results were found by Viviani and Mounoud (1990) when tracking ellipses and by Parker (2020) when tracking one-dimensional sine waves.…”

“…Schaal and Sternad (2001) found that even a simple Butterworth filter with an appropriate cutoff frequency can produce power-law trajectories out of constant-speed trajectories. Recently, we have shown that a robot arm following a constant-speed visual target can also produce power law trajectories because it behaves as second order system with delay, also a low-pass filter (Matić et al, 2021). Similarly, as recognized by Lacquanity et al (1983), an ellipse composed of pure sinewave components, without any harmonics, will conform to the −1/3 speed-curvature power law.…”

An integrative control theory perspective on consciousness.

“…This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. (Barter & Yin, 2021;Matić et al, 2021;Young, 2017). Importantly, the main tenets of PCT have received wide empirical support (for a review, see Mansell, 2020).…”

“…A series of studies involving PCT models that control perceptual variables—retinal linear velocity and lateral displacement—closely match real-world movement data during object interception without requiring prediction (Marken, 2021). Furthermore, software that implements perceptual control systems in robots permits effective navigation and obstacle avoidance (Young, 2017), balance (Johnson et al, 2020), locomotion on four legs (Barter & Yin, 2021), and arm coordination (Matić et al, 2021). These robots exhibit simple, naturalistic, purposive behavior through the real-time control of perceptual input, without the need for prediction, or consciousness.…”

“…PCT explains how an agent can be agile to deal with multiple, dynamic disturbances that cannot be predicted ahead of time, and so would not be addressed by predictive computational models (Barter & Yin, 2021;Matić et al, 2021;Young, 2017). Based on This document is copyrighted by the American Psychological Association or one of its allied publishers.…”