Information theory measures mechanical intelligence in legged robots
Quantifying Mechanical Intelligence in Legged Robots with Information Theory
Robotics
Summary
Robots can use their body’s natural movements to help control themselves, making walking and balancing easier. This paper looks at how to measure how much a robot’s body helps with its control using ideas from information theory, which studies how information is processed and shared. The authors tested their measurements on simple leg models and simulated walking robots to see how different robot parts affect control. Their work helps us understand and improve robot designs by showing how the body itself can do some of the 'thinking' needed for movement.
What this means in practice
- •For robotics engineers: Design legged robots that optimize body parts for less complex control based on quantitative mechanical intelligence measures.
- •For robotic control developers: Develop control policies that better exploit mechanical properties like compliance and transmission types for efficient locomotion.
Authors
Zach J. Patterson
Abstract
Mechanical intelligence, loosely defined as the reduction in control burden afforded by a robot's physical form, has become a prominent concept in robotics, with instantiations in bioinspired robotics, soft robotics, robotic swarms, and many other areas. However, rigorous theoretical understanding and quantitative measures of mechanical intelligence have lagged behind the engineering systems that the community has developed. In this work, using modern legged robots as a benchmark and exemplar, we propose several information-theoretic metrics for quantifying mechanical intelligence. By viewing body dynamics as both a computational process and a communication channel, we show that several prior insights in legged-robot engineering can be described using information theory, and we quantify how bits are processed by mechanical modes and across robot coordinates. Specifically, we examine the trade-off between explicitly incorporating compliance through series-elastic actuation and using so-called proprioceptive, low-gear-ratio transmissions, and we explore how these mechanisms interact with control policies during locomotion. We develop these results on systems of increasing complexity: a simplified linear model of a robot-leg transmission, a nonlinear single-leg simulation, and simulated quadruped robots controlled by a learned policy while navigating challenging terrain. These results lay the groundwork for broader study of robot mechanisms and their role in embodied computation.