The research area is witnessing a significant shift towards leveraging non-Euclidean structures and bio-inspired designs to enhance robotic capabilities. Innovations in distance fields and geodesic flows are being generalized to Riemannian manifolds, enabling more efficient and flexible path planning in complex environments. This approach, facilitated by neural network solutions, promises scalable and high-dimensional applications, particularly in energy-aware motion generation. Additionally, the field is seeing advancements in soft robotics, with a focus on modular, repairable systems and optimization-driven design methodologies that bridge the sim-to-real gap. Gripper designs are also advancing, incorporating strain-limiting layers to improve grip strength and payload capacity. Real-time trajectory generation for soft manipulators is another notable development, offering faster and more reliable motion planning. These trends collectively push the boundaries of robotic adaptability and performance in various applications, from medical to industrial settings.

Noteworthy papers include one that introduces a Riemannian eikonal solver for geodesic distance fields, enabling minimal-energy trajectories in robotics, and another that presents a modular soft actuator system for peristaltic transport, offering a scalable solution for delicate object manipulation.