Soft Robotics: Engineering Flexible Automation for Complex Environments ^†

Abstract

Soft robotics represents a transformative approach to automation, focusing on the development of robots constructed from flexible, compliant materials that mimic biological systems. Being different from traditional rigid robots, soft robots are engineered to adapt and operate efficiently in complex, unstructured environments, making them highly appropriate for applications that require delicate manipulation, safe human–robot interaction, and mobility on unstable terrain. The key principles, materials, and fabrication techniques of soft robotics are explored in this study, highlighting their versatility in industries such as healthcare, agriculture, and search-and-rescue operations. The essence of soft robotic systems lies in their ability to deform and respond to environmental stimuli. The system enables new paradigms in automation for tasks that demand flexibility, such as handling fragile objects, navigating narrow spaces, or interacting with humans. Emerging materials, such as elastomers, hydrogels, and shape-memory alloys, are driving innovations in actuation and sensing mechanisms, expanding the capabilities of soft robots in applications. We also examine the challenges associated with the control and energy efficiency of soft robots, as well as opportunities for integrating artificial intelligence and advanced sensing to enhance autonomous decision-making. Through case studies and experimental data, the potential of soft robotics is reviewed to revolutionize sectors requiring adaptive automation, ultimately contributing to safer, more efficient, and sustainable technological advancements than present robots.

1. Introduction

Soft robotics has emerged as an innovative solution to the challenges posed by traditional rigid robots in complex environments [1]. Inspired by the pliability of biological organisms, soft robots are composed of materials such as elastomers, polymers, and other compliant materials. Their adaptability allows for safe interactions in unpredictable environments, offering unique opportunities across multiple industries. The success of soft robots depends on the materials selected for their construction and the specific designs that enable flexibility and adaptability [2]. The types of materials and mechanical properties are critical for effective soft robotics and bio-inspired architectural designs.

Soft robots derive their flexibility from materials such as elastomers, hydrogels, and shape-memory alloys [3]. Each of these materials offers unique properties for different applications.

Table 1. Stress–strain characteristics of various soft materials.

Material	Properties	Applications
Silicone elastomers	High flexibility, biocompatible	Medical devices, soft actuators
Hydrogels	Soft and responsive to stimuli	Environmental monitoring, prosthetics
Shape-memory alloys	Deform and return to shape	Wearable robots, exoskeletons

depicts the relative elasticity and stress tolerance of materials used in soft robotics. The success of soft robots hinges largely on the materials chosen for their construction. Commonly used materials include silicone, hydrogels, and shape-memory alloys.

Figure 1 shows a comparison of elasticity and stress tolerance across silicone, hydrogels, and shape-memory alloys. The design principles of soft robotics are inspired by biological organisms, which enables high adaptability to complex environments.

In soft robots, pneumatic actuation is achieved through the inflation or deflation of channels embedded within the material, inspired by the movement of octopuses and other soft-bodied creatures. Hydraulic actuation also involves controlling the movement of the robot using a liquid medium to achieve motion, which is ideal for underwater robots. Figure 2 shows the structure and mechanism of a pneumatic soft actuator used in an underwater robotic arm. Common fabrication techniques for soft robots include the following:

• Through 3D printing, precise and intricate designs can be created using soft materials.
• Molding and casting are used to create soft robots with complex internal channels and structural features.
• Laser cutting is used for the precise customization of material sheets before assembly.

Controlling the movements of soft robots is complex due to the highly deformable nature of the materials involved. Machine learning and advanced sensing mechanisms are often used to overcome these challenges. Figure 3 and

Table 2. Actuators used in soft robots.

Actuator Type	Description	Advantages
Pneumatic actuators	Utilize air pressure for movement	Lightweight, versatile
Hydraulic actuators	Use liquid for high-force applications	Effective in water environments
Electroactive polymers	Respond to electrical stimuli	Fast and responsive control

show pneumatic, hydraulic, and electroactive polymer actuators. The control strategies include open-loop control with predefined actuation sequences for simple and repeatable tasks and closed-loop control with feedback. The closed-loop control incorporates sensors to adapt to movements in real time for complex and unstructured environments.

Figure 4 presents a closed-loop control system for a soft robotic gripper. The system comprises sensors, a control unit, pneumatic actuators, a feedback loop, and a pressure regulator. The following diagram illustrates the adaptive control mechanism.

2. Literature Review

Soft robotics dates back to the 1970s as a way to replicate the flexible, adaptive behaviors of biological organisms [1]. The concept of “soft machines” began to gain traction as researchers studied the movement of soft-bodied animals such as octopuses, worms, and starfish. Early models of compliant actuators attempted to mimic these biological organisms but were restricted by a lack of suitable materials and actuation mechanisms.

Research on flexible actuators using pneumatic and hydraulic systems in the 1980s laid the groundwork for the later development of soft robotic arms and grippers [2]. Advances were made in the application of soft materials for compliant movements. The birth of flexible joints and compliant robotic limbs showed the early potential for robots with softer structures, marking the beginning of the transition away from rigid, metallic designs. In the 1990s and 2000s, significant advancements in materials science began to influence robotics. The development of silicone elastomers, electroactive polymers, and other highly flexible materials provided engineers with materials that bend, twist, and expand [3]. This led to the invention of soft actuators—mechanisms that utilize fluids or electricity to deform soft materials.

In 2011, the introduction of the “octopus-inspired” soft arm by researchers at Harvard University constituted a great advance in the field of soft robotics. This project showed how bio-inspiration influences novel robotic systems that interact safely with humans, effectively starting a new era of biologically inspired robotic designs for compliant mechanisms.

Advances in control and fabrication techniques for soft robotics are fueled by technologies such as 3D printing, soft lithography, and computational modeling. With machine learning, control systems in soft robots have evolved, allowing for predictive movement and adaptability in real time. MIT, Stanford, and various European universities have developed soft robotics, focusing on applications such as medical devices, wearable robots, and underwater exploration. The integration of novel fabrication techniques such as 3D and 4D printing enabled more complex and multi-material designs, expanding the scope of soft robotics applications.

The effectiveness of soft robots largely depends on the selection of materials used for construction. Recent advancements have been made through the use of silicone elastomers, hydrogels, and electroactive polymers to create robots that are capable of significant deformation and complex tasks. Zhao and Huang [3] highlighted the pivotal role of material innovation and the development of shape-memory polymers that allow robots to self-reconfigure, enabling soft robots to handle complex environments.

The most common actuation methods for soft robotics include pneumatic, hydraulic, and electroactive mechanisms. Pneumatic actuators, utilizing pressurized air, are effective in achieving high levels of flexibility but often face limitations in control precision. Hydraulic actuators are fluid-driven and advantageous for underwater applications [3]. Electroactive polymers [4] represent a new form of actuation for applications requiring low weight and high flexibility.

One of the major challenges with soft robots is control. The nonlinearity of flexible materials makes movement prediction difficult. Rus and Tolley [2] presented various methods to control soft robots, from traditional feedback control to advanced machine learning approaches, emphasizing that the integration of sensors and soft artificial intelligence (AI) improves control accuracy. Wang et al. [5] focused on the role of reinforcement learning to improve adaptive behavior in soft robotic arms.

3. Application of Soft Robots

Soft robots are particularly well-suited for complex environments where rigid robots are impractical or unsafe to use as shown in

Table 3. Comparison of soft robots vs. traditional rigid robots.

Feature	Soft Robots	Rigid Robots
Flexibility	High	Low
Impact Resistance	High	Moderate
Navigation Efficiency	Dependent on environment	Effective in structured paths
Cost	Moderate	Varies

. In healthcare, soft robots are applied in minimally invasive surgery, where their flexibility allows for navigation inside the human body [5]. Laschi and Trimmer [6] deployed soft robotic grippers for harvesting delicate crops in agriculture, highlighting the advantage of flexibility in reducing product damage.

Underwater exploration is another major application area, as soft robots are capable of navigating difficult terrain and interacting with marine life without causing harm. Soft hydraulic actuators have been particularly effective for sub-sea exploration due to their ability to resist the pressures of deep-sea environments.

Soft robots are especially well-suited for environments that are challenging or hazardous for rigid robots [7,8]. The unique properties of these robots make them applicable across a range of industries.

• Surgical assistance: Soft robots are developed for minimally invasive surgical procedures [9]. Their flexibility allows them to reach difficult areas with minimal tissue damage [10,11].
• Rehabilitation devices: Wearable soft robotics, such as gloves and braces, are used in rehabilitation for stroke survivors to assist with movement recovery.
• Agricultural applications: Soft robotic grippers are used in agricultural settings to handle delicate fruits, minimizing damage during harvesting [12,13,14,15].
• Underwater exploration: Hydraulic soft robots are designed for exploring fragile underwater ecosystems without causing harm.

4. Case Study on Soft Robotics in Agricultural Produce Handling

In the case study, we examine the deployment of a soft robotic gripper in an agricultural setting, specifically focusing on handling delicate produce such as tomatoes and strawberries. We assess how effectively a soft robotic system can handle fragile items without causing damage compared to traditional rigid robotic systems.

Soft robotics is an advanced solution to handle delicate items in agriculture. Traditional rigid robotics lacks the flexibility to handle soft and easily damageable produce, often resulting in much product waste. Soft robotics, with its bio-inspired flexible design, offers an adaptable approach to handling fragile fruits and vegetables, providing both safety and precision. In this study, the application of a soft robotic gripper for harvesting and handling agricultural produce is also explored. We examine the design, materials used, and control systems used, comparing the results against a traditional rigid robotic gripper. Figure 5 displays the soft robotic gripper attached to an industrial robotic arm, gently picking tomatoes from a vine.

Design and Fabrication of a Soft Robotic Gripper

For the soft gripper, the silicone elastomer is chosen for its high flexibility, biocompatibility, and ability to deform without damage. Reinforced areas are also embedded to add strength and durability to high-load regions. The gripper is fabricated using a combination of soft lithography and 3D printing. Soft lithography enables the formation of pneumatic chambers, while 3D printing is used to integrate rigid components that connect to the robotic arm. Figure 6 depicts the internal pneumatic chambers, finger-like structures, and the connection interface to the robotic arm. The soft robotic gripper is actuated using pneumatic systems, which control the inflation and deflation of chambers to create a grip that adapts to the shape of the produce. This helps in reducing pressure points and prevents damage.

A closed-loop feedback control system uses pressure sensors to maintain an optimal grip force, automatically adjusting for variations in produce size and weight. Figure 7 shows how the pneumatic system is used to control the inflation of the gripper and the integration of feedback sensors for adaptive control.

5. Experimental Setup and Methodology

The soft robotic gripper was tested in a controlled environment for harvesting and handling two types of agricultural produce: strawberries and tomatoes. The performance metrics included produce damage rate, efficiency in harvesting, and energy consumption.

Table 4. Experimental setup and parameters.

Parameter	Soft Robotic Gripper	Rigid Robotic Gripper
Produce Type	Strawberries, tomatoes	Strawberries, tomatoes
Average Grip Pressure	0.3 MPa	1.5 MPa
Control Type	Closed-loop feedback	Pre-programmed force
Material Used	Silicone elastomer	Stainless steel

and the following show the setup of the experiment.

• Controlled environment: Greenhouse
• Robots: Soft robotic gripper and a traditional rigid robotic gripper.
• Sample size: 100 strawberries and 100 tomatoes for each gripper type.

6. Results and Comparisons

The soft robotic gripper resulted in a significantly lower rate of product damage than the rigid robotic gripper. A damage rate of 8% for strawberries and 6% for tomatoes with the soft gripper was obtained, compared with 30% and 28% for the rigid gripper (Figure 8).

The harvesting efficiency of the soft robotic gripper was evaluated in terms of speed and energy usage. The soft robotic gripper was slower but more energy-efficient than the rigid robotic system. Figure 9 and

Table 5. Performance metrics comparison.

Metric	Soft Robotic Gripper	Rigid Robotic Gripper
Average Handling Time	4 s/item	3 s/item
Damage Rate: Strawberries	8%	30%
Damage Rate: Tomatoes	6%	28%
Energy Consumption	Low	High

demonstrate the condition of strawberries harvested by the soft gripper (left) compared to those handled by the rigid gripper (right). The soft robotic gripper demonstrated higher adaptability when handling different sizes and weights of produce, with fewer adjustments needed compared to the rigid system, which required manual recalibration for different item sizes.

7. Challenges and Limitations

Soft robotics involves creating robots with flexible, compliant, and often biomimetic materials to address the challenges of complex, dynamic environments [16,17]. While promising, soft robotics faces challenges and limitations in engineering flexible automation [18].

Soft robotics requires materials that can bend, stretch, and withstand wear and tear in unpredictable environments. However, developing materials that balance flexibility with durability, responsiveness, and resistance to environmental factors (temperature and chemical exposure) is challenging [19,20]. Many soft materials degrade quickly or lack the necessary strength for long-term use. Controlling flexible and deformable structures requires sophisticated models and algorithms that can handle high degrees of freedom. Traditional control approaches cannot be used due to the non-linear, highly dynamic properties of soft robots, making it challenging to achieve precise movements and actions.

Soft robots need portable power sources, but soft materials are not conducive to housing rigid batteries or traditional power components. Designing energy-efficient actuators using pneumatics or hydraulics [20,21], is still a challenge, especially for applications requiring autonomy and long operational periods. Embedding sensors in soft structures to monitor stress, position, or environmental factors is difficult without impacting the robot’s flexibility. Additionally, collecting and interpreting data from these sensors for real-time feedback is challenging due to the complex deformation patterns and signal noise in soft materials [22,23].

Soft robots require more intricate and customized manufacturing processes than traditional robots, which are often made from rigid components and mass-produced [24]. Fabrication methods, such as 3D printing or molding for soft robotics, are evolving, but creating reliable, scalable designs remains a challenge, particularly for mass production [25].

Soft robots are often touted for their adaptability to different environments, yet they remain vulnerable to extreme conditions such as high or low temperatures, sharp objects, or corrosive chemicals. Ensuring these robots can function reliably across diverse environments without frequent maintenance or failure remains a limitation [26]. Modeling the behavior of soft materials is computationally intensive, as they exhibit complex behaviors such as viscoelasticity, non-linearity, and hysteresis. Current models are computationally costly and limited in accuracy, hindering the prediction and planning for efficient automation. Implementing soft robots in safety-critical applications (e.g., in healthcare or human–robot interactions) requires rigorous testing and regulatory approvals. Unlike rigid robots with predictable responses, soft robots’ dynamic responses and interactions introduce complexities in ensuring consistent safety standards.

The high costs associated with research, development, and manufacturing make soft robotics economically less viable for several applications. Limited market readiness and demand are also challenges, along with the scaling and widespread adoption of soft robots. Soft robots in medical and caregiving roles, for example, raise ethical considerations around dependency on automation, job displacement, and the potential reduction in human interaction. Addressing these concerns is essential for the responsible deployment of soft robotics. Overcoming these challenges requires interdisciplinary research across material science, control engineering, computer modeling, and industry-specific testing to unlock the full potential of soft robotics for flexible automation in complex environments.

8. Conclusions

The results of this study highlight the value of soft robotics in handling delicate produce within a complex agricultural environment. The soft robotic gripper reduces damage rates compared with a traditional rigid robotic system, although improvements are needed in terms of operational speed. The adaptability and safety offered by soft robotics make it a promising technology in agriculture and other fields where delicate handling is crucial.

Future research is necessary to enhance the speed of operation, optimize the pneumatic actuation process, and incorporate machine learning for predictive force control. Exploring durable materials for prolonged use in harsh agricultural conditions is essential to address current limitations. Soft robotic actuators with vision systems further enhance adaptability and automation efficiency.