Soft Robotics: Nature-Inspired Robot Evolution in Emerging Technology

This section addresses the critical challenge of giving soft robots the ability to sense their own configuration. The speaker describes 3D embedded printing, a method that adds sensors (curvature, inflation, and contact sensors) into silicone molded material while it cures. This creates flexible, elastic sensors that support robot needs. The goal is to create a system where continuous sensors embedded in the robot's body can provide automatic measurements of the robot's configuration—proprioception. The speaker introduces kirigami, a method where sensor response depends on cut patterns, and a data-driven approach using neural networks to associate sensory values with geometric configuration. This creates robots with perceptual skin that also know what configuration they are in, which is essential for control.

Sensor integration in soft robots requires balancing information richness with fabrication complexity. Starting with few sensors provides redundancy for mapping proprioceptive and exteroceptive feedback through signal analysis. Strategic sensor placement can mechanically guide actuator curvature. Acoustic sensing using microphones detects object shape by analyzing how filament contacts change vibration frequencies—identical-weight objects of different spatial densities produce distinguishable signatures. Optical fiber sensors embedded in soft sheets detect deformation and pressure through refractive index differences, with roughened sides providing directional bending sensitivity. For soft robotics, form defines function, meaning design necessarily involves designing the fabrication process itself. Applying fabrication-integrated design yields soft robots that are more durable, reliable, stronger, faster, and scalable in manufacturing. Geometric and material optimization is necessary but not sufficient; similarly, manufacturing alone is insufficient. A comprehensive framework requires multidisciplinary community effort integrating design and fabrication considerations throughout development.

This video presents a low-cost bending sensor developed using commercially available braided optical fibers, where roughening the cladding surface increases light scattering and sensitivity to bending without requiring extreme curvatures; the sensor demonstrates high sensitivity to deformation while being relatively insensitive to normal forces (less than 0.03 change ratio at 120N compared to 0.04 for silicon-based sensors at 50-60N), enabling distributed proprioception in soft robotic fingers through simple optical intensity measurements.

Designing sensors for soft robots is particularly challenging because soft robots continuously deform, making it difficult to create mechanically robust and reliable sensing systems for proprioception (understanding the robot's own state) and exteroception (understanding the external environment). Key challenges include high sensor-to-sensor variations from manual fabrication, difficulty decoupling different types of mechanical deformation (bending, stretching, twisting, pressure), and environmental effects like temperature and humidity that impact sensor performance. Effective solutions involve designing sensors with reduced deformation sets to improve mechanical selectivity, using materials like carbon fiber for better repeatability, and implementing shielding techniques for capacitive sensing to mitigate interference from external objects and electromagnetic fields.

This lecture presents research on designing sensors for soft robots and wearable devices, focusing on overcoming challenges such as cross-sensitivity to multiple mechanical inputs, hysteresis, and delamination. The work demonstrates applications in healthcare monitoring for conditions like edema, using strain and pressure sensors embedded in garments to track limb volume changes, and in soft origami robots where capacitive sensors enable proprioception through fold angle measurement. The key insight is that better fundamental sensor design—considering materials, structure, and modality—can reduce computational processing needs and improve performance for wearable and soft robotic applications.

Soft robotics offers transformative potential for minimally invasive surgery through applications like laser ablation and cardiac catheterization. For laryngeal tumor treatment, soft robots route optical fibers carrying laser energy to precisely targeted locations while MRI monitors heat diffusion and surgical margins in real-time, preserving vocal function. Cardiac catheterization applications leverage soft robotic catheters navigating heart chambers to create controlled scars blocking abnormal electrical signals. Combining MRI guidance with FBG sensing enables simultaneous monitoring of catheter position and temperature during ablation. Despite promising research, translating these innovations to clinical practice requires overcoming significant barriers including high costs, regulatory hurdles, and demonstrating consistent performance across diverse patient populations.

Soft material robotics offers transformative potential for minimally invasive surgery by combining compliant, safe materials with integrated sensing and actuation capabilities; these soft surgical robots—including soft continuum robots for lung cancer navigation, soft reactive skins for colonoscopy force distribution, and hybrid soft-rigid origami mechanisms for neurosurgery—address critical challenges in surgical navigation, manipulation, and tissue interaction while enabling new therapeutic possibilities that rigid robotic systems cannot achieve.

Soft robotics emerged from 1990s research but gained prominence through bioinspiration from nature. Calibration-based modeling addresses the limitation of assuming constant curvature in soft robots. Stiffness modulation through backbone insertion achieves 350% stiffness increase, combining soft safety with rigid performance. For pancreatic cancer treatment (less than 10% survival), soft robotic systems integrate micro-cameras, shape-controllable tips, and laser fibers for photodynamic therapy. Sub-millimeter accuracy and significant tumor reduction in animal trials demonstrate clinical viability for delivering therapies to confined anatomical regions while preserving healthy tissue.

Soft robotics has transformative applications in biomedicine. The Skiff Flop is a flexible endoscopic surgical robot enabling minimally invasive operations in complex conditions, developed through collaboration between Sant'Anna Institute of Pisa and the Italian Institute of Technology. Soft prosthetics, 3D printed and completely flexible, connect to the forearm using electrodes and can adapt force output for delicate or strong tasks. Hydrogel, a water-based biomaterial, offers exceptional biocompatibility for internal tissue contact, enabling microchirurgia applications. These advances demonstrate how soft robotics can address human needs in ways rigid robots cannot, from surgical precision to adaptive prosthetic functionality.

Bio-hybrid robotics integrates living biological materials, such as engineered muscles and insect proteins, with artificial materials to create devices that achieve biological functions like highly sensitive detection, self-healing, and energy-efficient motion—functions that purely artificial systems cannot replicate; for example, researchers have developed a bio-hybrid finger using tissue-engineered muscles and 3D-printed structures that can contract and move when electrically stimulated, and created sensors using mosquito proteins that can detect human sweat to help locate survivors in disaster scenarios.

Researchers led by Rashid Bachir at the University of Illinois created a new type of biohybrid robot combining living tissue with an artificial soft skeleton. The team seeded this skeleton with optogenetically cultured muscle cells, the first time such cells were cultivated in mice. Using laser cutting, they fabricated an intricate optoelectronic circuit from a stack of copper sheets. This circuit captures radio commands through a receiver coil and regulates energy to power microscopic LEDs, creating a completely wireless, battery-free configuration. This technology has significant medical applications, potentially enabling minimally invasive surgeries and cancer detection by identifying characteristic chemical signatures.

Bio-hybrid robotics combines living cells (such as neurons, muscle tissue, or heart cells) with artificial mechanical systems to create robots that demonstrate nature's high level of adaptation, with applications ranging from enhanced mobility and sensory capabilities to potential medical uses like artificial hearts, though this technology raises significant ethical questions about sentience, self-awareness, and the potential for synthetic systems to override biological will.

Biohybrid robots are miniature machines that combine 3D printed skeletal structures with living muscle tissues, where applying voltage to the muscle tissues causes contractions that move the printed finger-like bones; these robots have dual applications in creating artificial robots powered by living materials and in pharmaceutical studies where 3D engineered tissues can replace animal testing for drug development.

A research group has developed proof-of-concept instructions for creating bio-hybrid robots using human biological components. The system includes artificial skin made from human stem cells grown into real skin, which could clothe robots and provide healing and growth capabilities. The skin attaches to fabricated muscles, with individual brain organoids (like ganglia) distributed along the body rather than a central brain. The robot would have a mouth for gas exchange and a glucose drip system for nutrition, with waste elimination capabilities. Human tissue is used specifically because most research and fabrication is done with human tissue to ensure applicability to human medicine, avoiding the significant barrier of transitioning from mouse tissue to human tissue.