[Paper Review] Trade-Offs in Exploiting Body Morphology for Control: from Simple Bodies and Model-Based Control to Complex Bodies with Model-Free Distributed Control Schemes

Matej Hoffmann, Vincent C. Müller|arXiv (Cornell University)|Nov 9, 2014

Modular Robots and Swarm Intelligence4 citations

TL;DR

This paper investigates the trade-offs in using body morphology for robotic control, contrasting simple, model-based systems with complex, soft, model-free distributed control approaches. It argues for a dynamical systems perspective, highlighting that while morphology can reduce control complexity, it introduces challenges in modeling, design scalability, and performance guarantees, especially in soft robotics.

ABSTRACT

Tailoring the design of robot bodies for control purposes is implicitly performed by engineers, however, a methodology or set of tools is largely absent and optimization of morphology (shape, material properties of robot bodies, etc.) is lagging behind the development of controllers. This has become even more prominent with the advent of compliant, deformable or "soft" bodies. These carry substantial potential regarding their exploitation for control---sometimes referred to as "morphological computation" in the sense of offloading computation needed for control to the body. Here, we will argue in favor of a dynamical systems rather than computational perspective on the problem. Then, we will look at the pros and cons of simple vs. complex bodies, critically reviewing the attractive notion of "soft" bodies automatically taking over control tasks. We will address another key dimension of the design space---whether model-based control should be used and to what extent it is feasible to develop faithful models for different morphologies.

Motivation & Objective

• To analyze the trade-offs between simple and complex robot morphologies in control performance and design complexity.
• To evaluate the feasibility and limitations of model-based versus model-free control strategies in morphologically complex robots.
• To explore how morphological computation can reduce computational load but introduce challenges in design, modeling, and system predictability.
• To assess the role of dynamical systems theory in redefining control beyond traditional computational frameworks.
• To identify open challenges in designing adaptive, soft robotic systems with distributed, self-organizing control mechanisms.

Proposed method

• Adopts a 'trading spaces' framework from Pfeifer et al. to map control strategies along a spectrum from computational to morphological control.
• Analyzes case studies such as passive dynamic walkers and coffee-balloon grippers to illustrate morphology-based control in simple systems.
• Reviews model-based control techniques (e.g., optimal control, multiscale optimization) for rigid and compliant bodies.
• Examines model-free, distributed control in complex systems, including tensegrity robots and spiking neural networks.
• Evaluates the use of evolutionary robotics and hardware-in-the-loop optimization for navigating high-dimensional design spaces.
• Highlights the 'reality gap' between simulation and real-world testing in soft robotics and the lack of analytical models for performance guarantees.

Experimental results

Research questions

• RQ1 How does the complexity of robot morphology affect the feasibility and performance of model-based control?
• RQ2 To what extent can morphological properties reduce the need for centralized, computational control?
• RQ3 What are the trade-offs between control simplicity and system versatility when relying on body dynamics?
• RQ4 How do model-free, distributed control schemes perform in complex, compliant robotic systems compared to model-based approaches?
• RQ5 What are the limitations of current modeling and optimization techniques for soft, deformable robot bodies?

Key findings

• Simple, compliant bodies like passive dynamic walkers demonstrate that morphology alone can generate stable, functional behavior without active control.
• Morphological computation can reduce computational load but often at the cost of reduced portability and adaptability to new environments.
• Model-based control is limited in complex, nonlinear, or deformable systems due to the difficulty of creating accurate physical models.
• Model-free, distributed control in complex bodies requires new algorithms and is still in early development, though promising in systems like tensegrity robots.
• The absence of analytical models for soft bodies leads to a 'curse of dimensionality' in design, making optimization via simulation and hardware testing costly and heuristic-driven.
• Evolutionary and hardware-based co-design approaches show promise but are hindered by the 'reality gap' between simulation and real-world performance.