Soft Robotics: Advances, Challenges, and Future Applications in Manipulation, Exploration, and Healthcare
Soft robotics offers transformative potential for safe and adaptable interactions in complex environments, but faces significant challenges in materials, modeling, and control that must be overcome to fully realize its applications in manipulation, exploration, and healthcare
The paper “An Overview of Soft Robotics” published in the Annual Review of Control, Robotics, and Autonomous Systems offers a comprehensive analysis of the advancements, challenges, and potential future developments in the field of soft robotics. As a burgeoning area of research, soft robotics has the potential to revolutionize many industries by enabling robots to interact safely and effectively in complex and unstructured environments. These systems diverge significantly from traditional rigid-bodied robots, offering flexibility, compliance, and the capacity to perform tasks in dynamically changing settings. In addition, soft robotics is at the center of the three most prominent robotics research areas: human collaborative robots, dexterity, and autonomy. This summary encapsulates the key themes and insights presented in the paper, covering soft robotic actuation mechanisms, modeling, control strategies, sensing, and application areas, as well as future directions for research.
Introduction
The field of soft robotics takes its inspiration from natural systems, particularly from biological organisms that demonstrate remarkable adaptability, flexibility, and compliance. Unlike conventional rigid-bodied robots, which are optimized to perform a single task efficiently in a well-structured environment, soft robots are designed to handle multiple tasks in unpredictable settings. Their flexible structures, composed of soft materials, allow for a high degree of freedom (DOF) in movement and enable them to interact with their surroundings in a compliant and safe manner. This flexibility makes soft robots ideal candidates for applications requiring delicate interactions, such as medical devices, exploration robots, and industrial handling of fragile objects.
Despite their promise, soft robots have not yet reached their full potential. Their performance is limited by challenges related to materials, modeling, control, and real-world deployment. Traditional robotic systems, characterized by rigidity and control precision, benefit from well-established mathematical models and control architectures. However, in the case of soft robots, the compliant nature of the materials introduces complexity in both simulation and control. The authors argue that advancing the field requires overcoming these challenges by focusing on material science, computational modeling, and the development of control and sensing architectures suited to the unique nature of soft robotic systems.
Soft Robotic Actuation Modalities
Actuation is a fundamental aspect of robotic systems, and in soft robotics, actuators serve as the artificial muscles that generate motion and deformation. The review covers five primary actuation modalities that have been developed for soft robotics: fluidic, electrostatic, electrochemical, thermal, magnetic, and biohybrid actuation. Each modality has distinct characteristics and potential applications, as well as specific limitations that must be addressed.
Fluidic actuation is the most widely used modality in soft robots. It operates by controlling the pressure of fluids (air or liquid) within inflatable cavities, leading to the deformation of the soft robot’s body. The major advantage of fluidic actuators is their ability to produce a wide range of motion, such as bending, twisting, and elongation, while generating significant forces. However, they suffer from energy inefficiency and are susceptible to puncture or leakage. Moreover, controlling fluidic systems precisely is challenging due to the nonlinear behavior of the soft materials involved.
Electrostatic and electrochemical actuators use electrical energy to induce deformation in the soft robot’s body. Dielectric elastomer actuators (DEAs), for instance, employ electrostatic forces between two stretchable electrodes to deform a dielectric material. These actuators can produce a variety of deformation patterns, including contraction, elongation, and twisting, depending on their configuration. Another variant, ionic polymer-metal composite (IPMC) actuators, use electrochemical principles, where ion migration causes swelling and movement. While these actuators offer significant advantages in terms of responsiveness to electrical inputs, they often require high voltages, which can limit their practical applications and raise safety concerns.
Thermal actuation relies on shape-memory alloys (SMAs), materials that change shape when exposed to specific temperatures. SMAs are often used in medical devices due to their ability to deform under low voltages and operate silently. However, they are inefficient because of their reliance on temperature changes, which introduces delays in operation and limits their energy efficiency. Additionally, thermal actuators experience mechanical fatigue over time, which shortens their operational lifespan.
Magnetic actuation leverages the alignment of embedded magnetic particles within soft materials under external magnetic fields. This untethered actuation modality has gained attention for biomedical applications, where soft robots need to navigate delicate environments inside the human body. Magnetic actuation offers precise control and can operate without physical connections. However, the need for large and complex magnetic field setups, as well as interference from nearby metallic objects, presents significant hurdles.
Biohybrid actuation integrates living cells, such as muscle cells, into soft robots to induce movement. These actuators mimic the functions of natural muscles and offer unique advantages, such as self-healing capabilities and energy efficiency from biological fuel sources. The most common biohybrid systems involve skeletal muscle cells and cardiomyocytes, which can contract in response to external stimuli. Despite their potential, biohybrid actuators face challenges in scalability, ethical concerns related to the sourcing of biological materials, and the complexity of integrating living tissues with synthetic materials.
Modeling and Simulation
Accurate modeling and simulation are critical for advancing soft robotics, as they allow researchers and engineers to predict how soft robots will behave in real-world conditions. However, the inherent flexibility and deformation of soft materials complicate the modeling process. Traditional methods used for rigid robots, such as kinematic and dynamic models, do not apply well to soft systems.
The most accurate approach to modeling soft robots involves numerical mesh-based techniques, particularly the finite element method (FEM). FEM divides the soft robot’s geometry into discrete elements and calculates the forces and deformations acting on each element. While FEM is highly accurate, it is computationally expensive and becomes impractical for complex robots with large DOFs.
Simplified models, such as the Cosserat rod theory and the piecewise constant strain (PCS) model, are often used to reduce computational complexity. These models represent soft robots as series of connected rods or segments, each capable of bending, twisting, stretching, and shearing. These simplified models are particularly useful for simulating specific behaviors, such as the bending of soft robotic arms. A further simplification, the piecewise constant curvature (PCC) model, assumes that deformation occurs only through bending, ignoring stretching and twisting. This model is often used in applications where bending is the primary mode of movement.
In addition to physics-based modeling, learning-based approaches are gaining popularity in soft robotics. Machine learning techniques, particularly neural networks, can be trained on experimental data to create models that predict the dynamic behavior of soft robots. These models offer significant advantages in speed and flexibility, as they can adapt to new designs and environments without the need for precise mathematical descriptions. However, learning-based models require extensive training data and are less interpretable than traditional physics-based models.
Control Strategies
Controlling soft robots presents unique challenges due to their continuous deformation and high DOFs. Traditional control architectures for rigid robots, which rely on discrete joint-based models, are not applicable to soft systems. As a result, researchers have developed a range of model-based and data-driven control strategies specifically for soft robots.
Model-based control approaches use a mathematical model of the robot to calculate control inputs. These models are typically derived from the robot’s kinematic and dynamic properties and allow for precise control of movement. Kinematic model-based control focuses on the robot’s posture and trajectory without considering forces, making it suitable for tasks like positioning and reaching. While this approach is computationally simple, it is not effective for tasks that require interaction with the environment or force feedback.
Dynamic model-based control goes a step further by incorporating forces such as inertia, gravity, and external loads into the control equation. This approach allows for more accurate and versatile control, particularly in scenarios where the robot must interact with objects or adapt to external forces. Dynamic control is computationally more demanding than kinematic control, but it offers superior performance in tasks that require both position and force control.
Data-driven control strategies bypass the need for explicit models by learning control policies from experimental data. Reinforcement learning, for instance, has been successfully applied to soft robots, where the robot learns to achieve a desired task through trial and error. One particularly promising technique is the Koopman operator, which linearizes the robot’s nonlinear dynamics, allowing traditional linear control methods to be applied. These data-driven approaches offer flexibility and robustness but require extensive training and are computationally intensive.
Sensing Strategies and Sensor Technologies
Sensing is essential for enabling soft robots to perceive their environment and monitor their own state (proprioception). However, traditional sensors, which are designed for rigid systems, are not well suited for the flexible and deformable bodies of soft robots. To address this, researchers are developing new proprioceptive and tactile sensing technologies that can be embedded into soft materials.
Proprioception refers to the robot’s ability to sense its own movement and deformation. In research settings, this is often achieved using external motion capture systems, which track the robot’s position using infrared cameras and reflective markers. However, these systems are impractical for real-world applications due to their reliance on external equipment. Instead, researchers are developing embedded sensors that can be integrated directly into the robot’s body. These sensors measure deformation, strain, and movement in real time, providing crucial feedback for control.
Tactile sensing involves detecting contact with external objects, allowing soft robots to interact safely with their environment. A variety of sensor technologies can be used for tactile sensing, including resistive, capacitive, and magnetic sensors. Resistive sensors measure changes in electrical resistance due to deformation, while capacitive sensors detect changes in electric fields when the robot comes into contact with an object. Magnetic sensors, which measure changes in magnetic flux, offer high accuracy and are particularly useful for tracking movement in soft robots. However, each sensing technology has its limitations, including issues with hysteresis, nonlinearity, and susceptibility to interference from external forces.
Optical fiber sensors, based on fiber Bragg grating, are another promising technology for measuring deformation in soft robots. These sensors use light to detect strain along the length of an optical fiber, providing precise measurements of deformation. Optical fiber sensors are thin and can be embedded into the robot’s body, but they are expensive and require bulky readout equipment, limiting their use in mobile applications.
Application Areas
The paper highlights three major application areas where soft robots are already making an impact: manipulation, exploration, and healthcare.
Manipulation is one of the most immediate applications of soft robotics, particularly in industries where robots are required to handle delicate objects. The flexibility and compliance of soft robots allow them to safely manipulate fragile items without causing damage. Soft robots can manipulate objects using actuation, adhesion, or stiffness modulation. For example, soft grippers can be equipped with adhesive surfaces inspired by gecko feet, allowing them to grip objects without exerting force. In addition, soft robots can adjust their stiffness to accommodate objects of different weights and sizes, making them more versatile in industrial settings.
Exploration is another promising area for soft robotics. Soft robots, particularly those designed with bioinspired features, can navigate and interact with unstructured environments, such as underwater habitats or disaster zones. Their flexibility allows them to move through tight spaces and adapt to unpredictable conditions. However, many exploration robots remain tethered to external power sources, limiting their range. Ongoing research is focused on developing untethered soft robots that can operate autonomously using onboard power sources, such as batteries or fuel cells.
In healthcare, soft robots have the potential to revolutionize areas such as rehabilitation, prosthetics, and minimally invasive surgery. The inherent softness and flexibility of these robots allow them to conform to the human body, making them ideal for assisting patients with mobility impairments or for performing delicate medical procedures. For example, soft robots can be used to guide surgical instruments through the body or to deliver drugs directly to specific tissues. The integration of biohybrid systems into healthcare applications is particularly exciting, as it opens up the possibility of creating robots that can repair themselves or grow new tissues in response to damage.
Future Perspectives
The paper concludes by identifying key challenges and opportunities for the future of soft robotics. To fully realize the potential of soft robots, several important issues must be addressed. First, there is a need for the development of new materials that combine the flexibility of soft robots with the strength and durability of rigid systems. These materials should also be capable of self-healing and reconfiguring themselves in response to damage, mimicking biological systems.
Second, control architectures need to be improved to handle the complex dynamics of soft robots. The high DOFs and continuous deformation of soft systems make control particularly challenging, and existing control strategies are often too computationally expensive for real-time operation. Advances in machine learning and data-driven control could provide a solution, but these methods require further development to ensure they are robust and reliable in real-world scenarios.
Finally, sensor integration is a critical area of research. Current sensor technologies, while promising, are often limited by issues such as hysteresis, nonlinearity, and interference from external forces. New sensor designs that can be seamlessly integrated into soft materials and provide accurate, real-time feedback are essential for enabling autonomous soft robots.
In addition to these technical challenges, the authors highlight the need for further research into biohybrid systems, which integrate living cells into soft robots. These systems offer exciting possibilities for creating robots that can grow, heal, and adapt in ways that are currently impossible with synthetic materials alone. However, ethical concerns related to the use of biological materials must also be addressed.
In all, “An Overview of Soft Robotics” provides a thorough examination of the current state of the field and highlights the significant challenges and opportunities that lie ahead. The paper serves as a valuable resource for researchers and practitioners seeking to understand the complexities of designing, modeling, and controlling soft robots and offers a roadmap for future advancements in this exciting area of robotics.