Soft robotics has evolved from an academic curiosity into a strategic technology for fields such as healthcare, rehabilitation and industrial manipulation. Its ability to deform, adapt and operate in changing environments makes it especially valuable. Within this field, modular soft robots (MSRs) multiply the possibilities: because they consist of connected segments, they can be reconfigured to perform complex tasks that a rigid robot could not handle. However, that same structural flexibility poses a huge challenge: precise control of these systems cannot depend on fixed models or manually programmed rules.
Controlling a modular soft robot requires understanding how each segment affects the overall motion. The dynamics of these systems are nonlinear, with hysteresis and viscoelastic behaviour, which adds an additional difficulty to controller design. In a robot with many degrees of freedom, any change in morphology, such as adding or removing a module, completely modifies the dynamics of the system. Classic controllers trained for a specific configuration become invalid when the robot is reconfigured. Until now, the usual solution was to retrain the model from scratch, an expensive, slow and impractical process for real applications.
Inspiration from continual learning offers an alternative path. A control system based on this paradigm learns incrementally, incorporating new robot configurations without losing previously acquired knowledge. In other words, the controller does not forget how to handle earlier configurations when it learns a new one. This principle, known in artificial intelligence as resistance to catastrophic forgetting, is essential for a modular robot to adapt throughout its useful life without constant human intervention.
For this behaviour to be viable, the control framework must carefully manage model memory. Regularization techniques, knowledge distillation and selection of relevant experiences prevent new learning from overwriting previous skills. This architecture not only protects knowledge, but also reduces the amount of data and computational resources needed to update the controller. Instead of retraining from zero, the system makes local adjustments over the existing base, which accelerates development and enables continuous experimentation.
Moreover, when the robot configuration remains fixed, the framework can be deployed in a distributed way. Each module has its own local dynamic model, enabling decentralized and much more precise control. Instead of treating the robot as a single black box, the particularities of each segment are learned and the actions are coordinated in a modular fashion. This strategy reduces computational load, simplifies maintenance and improves fault tolerance: if one module stops responding, the rest can reorganize to complete the task.
Experiments carried out with a tendon-driven simulated robot and with a real three-module pneumatic arm show that the approach is not only theoretically sound, but also viable under working conditions. In closed-loop trajectory tracking tests, the controller achieves precise tracking even when the robot morphology changes during operation. A reaching experiment with a virtual target has also been tested, in which the system activates only the necessary modules, reducing resource consumption and improving efficiency. This ability to actively select modules is key to deploying modular robots in real environments, where computational and energy resources are limited.
The applications of this technology are very broad. In the medical field, a modular robot could move through the human body in a non-invasive way, changing shape according to the needs of the procedure. In search and rescue operations, these robots could adapt to rubble and confined spaces. In industry, modular soft arms can manipulate fragile objects with a dexterity that a rigid actuator cannot offer. In all these cases, the ability to learn new configurations on the fly makes the difference between a laboratory prototype and a practical solution.
For companies seeking to bring these advances to market, the software side is as important as the robot mechanics. An advanced control system requires a technological platform that integrates sensors, machine learning algorithms, secure communications and real-time data visualization. At this point, the experience of a company like Q2BSTUDIO, specialized in software development and technology, becomes a strategic ally. The development of custom software makes it possible to adapt every component of the system to the specific needs of the project, avoiding the limitations of generic solutions.
The integration of artificial intelligence into these systems goes far beyond simple movement automation. AI agents can decide which modules to activate, which trajectory to follow and what force level to apply at each moment. They learn from interaction with the environment and continuously optimize robot performance. With the support of a cloud infrastructure on AWS or Azure, generated data can be stored in a scalable way and processed to train new models. Cybersecurity must be present from the design stage, because a connected robot is always a potential entry point for external attacks. Penetration testing and security audits are recommended practices to protect both the machine and the data it handles. And when information becomes a business asset, Business Intelligence dashboards with Power BI make it possible to visualize fleet performance, detect anomalies and make data-driven decisions.
This software development approach has a direct analogy with continual learning. In the same way that a modular robot learns to reconfigure itself without forgetting, an enterprise platform can evolve with new functionalities without breaking existing processes. Q2BSTUDIO applies this philosophy when creating control systems, telemetry platforms and fleet management tools. It is not only about implementing algorithms, but about making technology adapt to the evolution of the business and the physical environment. Collaboration between robotics engineers and software developers connects the physical and digital worlds, creating solutions that once seemed reserved for research laboratories.
In short, adaptive control of modular soft robots through continual learning represents a major step towards more autonomous and flexible robotic systems. The ability to adapt to morphological changes without complete retraining not only reduces costs, but also opens the door to new applications in unpredictable environments. Combining soft hardware, adaptive algorithms and robust enterprise software is the formula to bring this technology out of laboratories and turn it into a productive tool. Companies like Q2BSTUDIO are in a privileged position to lead this transformation, providing a comprehensive vision that spans from control engineering to data, security and artificial intelligence.
