Current Trends in Soft Robotics Research

The field of soft robotics is witnessing significant advancements, particularly in the areas of underactuation, learning-based control, and proprioceptive sensing. Researchers are increasingly focusing on developing modules that can dynamically alter their properties, such as stiffness and radius, to enhance the versatility and functionality of soft robots. These underactuated geometric compliant (UGC) modules are being designed to maintain structural integrity while adapting to various operational demands, offering new possibilities for robotic applications.

In parallel, learning-based control strategies are gaining traction as a solution to the complexities posed by soft robot dynamics. Recurrent neural networks (RNNs) are being employed to model and predict the behavior of soft robots, capturing nonlinearities such as hysteresis that are inherent in these systems. These models are then integrated into nonlinear model predictive control (NMPC) frameworks, enabling accurate trajectory tracking and improved control performance.

Another notable trend is the use of physical reservoir computing for proprioceptive and exteroceptive information perception. By leveraging distributed pressure data within soft robotic arms, researchers are developing methods to predict kinematic postures and payload status with minimal training data. This approach not only enhances the robot's sensing capabilities but also reduces the reliance on specialized sensors, making soft robots more portable and cost-effective.

For large-scale soft robots, data-efficient learning methods are being explored to handle dynamic tasks with minimal trials. Bayesian optimization is proving to be a powerful tool for optimizing control policies directly from commanded pressures, bypassing the need for complex kinematic and dynamic models. This approach is particularly promising for tasks requiring fast, dynamic motion, expanding the practical applications of large-scale soft robots.

Noteworthy Developments

• Underactuated Geometric Compliant (UGC) Modules: Prototypes that can dynamically alter their radius while maintaining structural integrity.
• Learning-based Nonlinear Model Predictive Control: RNN-based models for soft robots, showing improved accuracy over traditional LSTM networks.
• Physical Reservoir Computing: Simultaneous prediction of kinematic posture and payload status using distributed pressure data.
• Data-efficient Learning for Large-scale Soft Robots: Bayesian optimization for dynamic tasks, demonstrated through both simulation and real-world experiments.