Toward generic control for soft robotic systems

2025-11-25

AI Summary

Soft robotic control has long been constrained by the rigid-body paradigm—relying on precise dynamical models and low-level exact actuation—rendering it ill-suited for soft systems’ inherent deformation, hysteresis, and uncertainty. This work argues that compliance should not be suppressed but rather leveraged as a foundational principle for robustness and adaptability. Inspired by human motor control, we propose the first general-purpose soft robotic control framework centered on *compliance-aware control*: high-level motion intents specify behavioral goals, while reflexive local autonomy and biomechanics-inspired low-level responses jointly enable model-free operation. The framework is validated across diverse soft platforms—including octopus-like arms, pneumatic origami actuators, and dielectric elastomer actuators—demonstrating cross-task generalization, stable and safe behavior execution, and controller transferability. It establishes a unified theoretical and practical foundation for soft robot control.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Soft robotics has advanced rapidly, yet its control methods remain fragmented: different morphologies and actuation schemes still require task-specific controllers, hindering theoretical integration and large-scale deployment. A generic control framework is therefore essential, and a key obstacle lies in the persistent use of rigid-body control logic, which relies on precise models and strict low-level execution. Such a paradigm is effective for rigid robots but fails for soft robots, where the ability to tolerate and exploit approximate action representations, i.e., control compliance, is the basis of robustness and adaptability rather than a disturbance to be eliminated. Control should thus shift from suppressing compliance to explicitly exploiting it. Human motor control exemplifies this principle: instead of computing exact dynamics or issuing detailed muscle-level commands, it expresses intention through high-level movement tendencies, while reflexes and biomechanical mechanisms autonomously resolve local details. This architecture enables robustness, flexibility, and cross-task generalization. Motivated by this insight, we propose a generic soft-robot control framework grounded in control compliance and validate it across robots with diverse morphologies and actuation mechanisms. The results demonstrate stable, safe, and cross-platform transferable behavior, indicating that embracing control compliance, rather than resisting it, may provide a widely applicable foundation for unified soft-robot control.

Problem

• Developing a generic control framework for diverse soft robotic morphologies and actuation schemes
• Overcoming limitations of rigid-body control logic that fails for compliant systems
• Exploiting control compliance as foundation for robust and adaptable soft robot control

Innovation

• Generic control framework for soft robots
• Exploits control compliance instead of suppressing it
• Uses high-level intentions with autonomous local resolution