Estimating a soft robot's pose and applied forces, also called proprioception, is crucial for safe interaction of the robot with its environment. However, most solutions for soft robot proprioception use dedicated sensors, particularly for external forces, which introduce design trade-offs, rigidity, and risk of failure. This work presents an approach for pose estimation and contact detection for soft robots actuated by shape memory alloy (SMA) artificial muscles, using no dedicated force sensors. Our framework uses the unique material properties of SMAs to self-sense their internal stress, via offboard measurements of their electrical resistance and in-situ temperature readings, in an existing fully-soft limb design. We demonstrate that a simple polynomial regression model on these measurements is sufficient to predict the robot's pose, under no-contact conditions. Then, we show that if an additional measurement of the true pose is available (e.g. from an already-in-place bending sensor), it is possible to predict a binary contact/no-contact using multiple combinations of self-sensing signals. Our hardware tests verify our hypothesis via a contact detection test with a human operator. This proof-of-concept validates that self-sensing signals in soft SMA-actuated soft robots can be used for proprioception and contact detection, and suggests a direction for integrating proprioception into soft robots without design compromises. Future work could employ machine learning for enhanced accuracy.

The present paper is concerned with deep learning techniques applied to detection and localization of damage in a thin aluminum plate. We used data generated on a tabletop apparatus by mounting to the plate four piezoelectric transducers, each of which took turn to generate a Lamb wave that then traversed the region of interest before being received by the remaining three sensors. On training a neural network to analyze time-series data of the material response, which displayed damage-reflective features whenever the plate guided waves interacted with a contact load, we achieved a model that detected with greater than 99% accuracy in addition to a model that localized with $3.14 \pm 0.21$ mm mean distance error and captured more than 60% of test examples within the diffraction limit. For each task, the best-performing model was designed according to the inductive bias that our transducers were both similar and arranged in a square pattern on a nearly uniform plate.

A report examines the potential of materials that can change shape, adapt and repair themselves.

MIT CSAIL's origami robot is packaged in an ingestible ice pill. In 2013, University of Sheffield roboticist Dana Damian was doing postdoctoral research at Harvard Medical School affiliate Boston Children's Hospital when she learned of a procedure called the Foker technique. The surgery, performed on children with a rare congenital lung defect, calls for doctors to attach sutures to part of an infant's esophagus, then tie them off on the baby's back. Over time, the sutures lengthen the esophagus by pulling on it, stimulating tissue growth. Although the technique can be effective, the risk of infection and complication is high, and the baby must remain under sedation for weeks.

The classical view of a robot as a mechanical body with a central "brain" that controls its behavior could soon be on its way out. The authors of a recent article in Science Robotics argue that future robots will have intelligence distributed throughout their bodies. The concept, and the emerging discipline behind it, are variously referred to as "material robotics" or "robotic materials" and are essentially a synthesis of ideas from robotics and materials science. Proponents say advances in both fields are making it possible to create composite materials capable of combining sensing, actuation, computation, and communication and operating independently of a central processing unit. Much of the inspiration for the field comes from nature, with practitioners pointing to the adaptive camouflage of the cuttlefish's skin, the ability of bird wings to morph in response to different maneuvers, or the banyan tree's ability to grow roots above ground to support new branches.

The MIT (Massachusetts Institute of Technology: MIT) is one of the most internationally-renowned academic ecosystems in terms of research, technology and the future. With these credentials, it is not strange that OpenMind has selected it as the perfect setting to ponder: What's the next step? Exponential life, a life marked by the progress of so-called exponential technologies is not science fiction, nor a possible exclusively future scenario because technologies are already among us that transform our lives in very different ways. In this article we have chosen 5 examples, explained by the authors of our latest book, to show why we can say that exponential technologies are already here. Image of the event held at MIT.

The boundaries between smart materials, artificial intelligence, embodiment, biology, and robotics are blurring. Smart materials largely cover the same set of physical properties (stiffness, elasticity, viscosity) as biological tissue and state-of-the-art soft robotic technologies that have the potential to deliver this capability. We can foresee smart skins, assist and medical devices, biodegradable and environmental robots or intelligent soft robots. Ultimately wearable assist devices will make conventional assist devices redundant.

The nineteenth century marked the acceleration and wide adoption of industrial processes. In the twentieth century, technology moved from the laboratory and research institute to the home. We are now at the cusp of a new technological shift of equal significance: the Robotics Revolution. But what is the Robotics Revolution and what will it actually deliver? A "robot" is often defined as a machine that can carry out a complex series of actions automatically, especially one programmable by a computer.