Feedback Controller Ensures Safety in Soft Robots for Environmental Interaction

On April 20, 2025, researchers published Safe Autonomous Environmental Contact for Soft Robots using Control Barrier Functions, detailing a novel approach to ensure soft robots maintain safe environmental interactions. Their work introduces a feedback controller utilizing Control Barrier Functions, successfully demonstrated through hardware experiments, ensuring formal safety specifications in delicate settings.

The study introduces a feedback controller for soft manipulators ensuring safe environmental contact by maintaining force bounds. Using Control Barrier Functions, the controller maps force limits to safe tip positions based on predicted environment deformation. Hardware experiments with a multi-segment soft pneumatic demonstrate successful constraint of contact forces. This framework represents a fundamental shift in control logic for soft robots, enabling formal verification of pose and contact safety specifications.

Soft robotics is an emerging field that seeks to create robots with soft, adaptable materials, mimicking the flexibility and resilience of living organisms. Unlike traditional rigid robots, soft robots can navigate complex environments, interact safely with humans, and perform delicate tasks. However, controlling these systems presents unique challenges due to their inherent complexity and non-linear dynamics.

Soft robots are built from materials like elastomers, gels, or textiles, which allow them to deform, stretch, and adapt to their surroundings. This flexibility is a significant advantage over rigid robots but introduces complexity. Unlike traditional robots with predictable mechanical behaviors, soft robots exhibit non-linear dynamics that make modeling and control difficult.

For years, researchers relied on model-based approaches—simplified mathematical representations of the robot’s behavior—to design controllers. However, these models often fail to capture the full range of a soft robot’s capabilities or account for real-world uncertainties like environmental changes or material degradation. This limitation has hindered the development of robust control strategies that can ensure reliable performance in dynamic environments.

Recent research has shifted toward hybrid approaches that combine model-based methods with machine learning techniques. For example, model-based reinforcement learning (MBRL) uses a simplified model to guide the robot’s actions while allowing it to learn from real-world interactions. This approach balances computational efficiency with adaptability, enabling soft robots to perform complex tasks without relying solely on pre-programmed behaviors.

One promising development is the use of reduced-order models—simplified representations that capture the essential dynamics of a system. These models reduce computational overhead while maintaining sufficient accuracy for control purposes. By integrating these models with reinforcement learning algorithms, researchers have demonstrated improved performance in trajectory optimization and dynamic control of soft robotic manipulators.

As soft robots move closer to real-world applications such as medical robotics, disaster response, or industrial automation, the need for safety-critical systems becomes increasingly urgent. Unlike traditional robots, which operate in controlled environments, soft robots are often deployed in unpredictable settings where human interaction is possible. Ensuring their safe operation requires rigorous testing and validation.

Recent advancements in provably safe reinforcement learning aim to address these concerns. Researchers can guarantee that the robot’s actions remain within predefined safety constraints by incorporating formal verification techniques into the learning process. This approach combines the flexibility of machine learning with the reliability of traditional control systems, creating a framework for robots that can adapt to new situations while avoiding dangerous behaviors.