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Soft Robotics and AI Control: Engineering Research Directions for 2026
Soft robotics is moving from material experimentation toward intelligent systems that can sense, adapt and interact safely. The combination of compliant mechanics, embedded sensing and AI control creates a rich multidisciplinary research space.
Mechanical modelling
Soft actuators require nonlinear material models, contact and large-deformation mechanics. FEA can be used to study geometry, pressure and material stiffness.
Sensing and state estimation
Vision, pressure, strain, IMU and force sensing can be fused to estimate configuration and interaction forces.
Control research
Model-based control, adaptive control and reinforcement learning can be compared for trajectory tracking, grasping or interaction tasks.
Research metrics
Tracking error, energy consumption, robustness, force regulation, generalization and safe interaction are useful performance measures.
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Topic FAQs
Frequently asked questions
ANSYS/COMSOL can model mechanics, while MATLAB/Simulink, Python or ROS-oriented workflows can support control and learning.
A novel actuator, sensing strategy, adaptive controller, safe learning method or integrated digital twin can create a clear contribution.
