Soft robots have been increasingly utilized as sophisticated tools in physical rehabilitation, particularly for assisting patients with neuromotor impairments. However, many soft robotics for rehabilitation applications are characterized by limitations such as slow response times, restricted range of motion, and low output force. There are also limited studies on the precise position and force control of wearable soft actuators. Furthermore, not many studies articulate how bellow-structured actuator designs quantitatively contribute to the robots' capability. This study introduces a paradigm of upper limb soft actuator design. This paradigm comprises two actuators: the Lobster-Inspired Silicone Pneumatic Robot (LISPER) for the elbow and the Scallop-Shaped Pneumatic Robot (SCASPER) for the shoulder. LISPER is characterized by higher bandwidth, increased output force/torque, and high linearity. SCASPER is characterized by high output force/torque and simplified fabrication processes. Comprehensive analytical models that describe the relationship between pressure, bending angles, and output force for both actuators were presented so the geometric configuration of the actuators can be set to modify the range of motion and output forces. The preliminary test on a dummy arm is conducted to test the capability of the actuators.

Torque and continuous rotation are fundamental methods of actuation and manipulation in rigid robots. Soft robot arms use soft materials and structures to mimic the passive compliance of biological arms that bend and extend. This use of compliance prevents soft arms from continuously transmitting and exerting torques to interact with their environment. Here, we show how relying on patterning structures instead of inherent material properties allows soft robotic arms to remain compliant while continuously transmitting torque to their environment. We demonstrate a soft robotic arm made from a pair of mechanical metamaterials that act as compliant constant-velocity joints. The joints are up to 52 times stiffer in torsion than bending and can bend up to 45{\deg}. This robot arm can continuously transmit torque while deforming in all other directions. The arm's mechanical design achieves high motion repeatability (0.4 mm and 0.1{\deg}) when tracking trajectories. We then trained a neural network to learn the inverse kinematics, enabling us to program the arm to complete tasks that are challenging for existing soft robots such as installing light bulbs, fastening bolts, and turning valves. The arm's passive compliance makes it safe around humans and provides a source of mechanical intelligence, enabling it to adapt to misalignment when manipulating objects. This work will bridge the gap between hard and soft robotics with applications in human assistance, warehouse automation, and extreme environments.

This paper introduces a size-adaptable robotic endoscope design, which aims to improve the efficiency and comfort of colonoscopy. The robotic endoscope proposed in this paper combines the expansion mechanism and the external drive system, which can adjust the shape according to the different pipe diameters, thus improving the stability and propulsion force during propulsion. As an actuator in the expansion mechanism, flexible bellows can provide a normal force of 3.89 N and an axial deformation of nearly 10mm at the maximum pressure, with a 53% expansion rate in the size of expandable tip. In the test of the locomotion performance of the prototype, we obtained the relationship with the propelling of the prototype by changing the friction coefficient of the pipe and the motor angular velocity. In the experiment with artificial bowel tissues, the prototype can generate a propelling force of 2.83 N, and the maximum linear speed is 29.29 m/s in average, and could produce effective propulsion when it passes through different pipe sizes. The results show that the prototype can realize the ability of shape adaptation in order to obtain more propulsion. The relationship between propelling force and traction force, structural optimization and miniaturization still need further exploration.

Soft-robot designs are manifold, but only a few are publicly available. Often, these are only briefly described in their publications. This complicates reproduction, and hinders the reproducibility and comparability of research results. If the designs were uniform and open source, validating researched methods on real benchmark systems would be possible. To address this, we present two variants of a soft pneumatic robot with antagonistic bellows as open source. Starting from a semi-modular design with multiple cables and tubes routed through the robot body, the transition to a fully modular robot with integrated microvalves and serial communication is highlighted. Modularity in terms of stackability, actuation, and communication is achieved, which is the crucial requirement for building soft robots with many degrees of freedom and high dexterity for real-world tasks. Both systems are compared regarding their respective advantages and disadvantages. The robots' functionality is demonstrated in experiments on airtightness, gravitational influence, position control with mean tracking errors of <3 deg, and long-term operation of cast and printed bellows. All soft- and hardware files required for reproduction are provided.

Redundant cascade manipulators actuated by pneumatic bellows actuators are passively compliant, rugged and dexterous which are qualities making them exceptionally well suited for applications in agriculture. Unfortunately bellows actuators are notoriously difficult to precisely position. This paper presents a novel control algorithm for the control of a parallel platform actuated by pneumatic bellows actuators, which is serving as one module of a cascade manipulator. The algorithm combines a feed-forward controller and a variable gain I-controller. The feed-forward controller was designed using experimental data and two regression steps to create a mathematical representation of the data. The gain of the I-controller depends linearly on the total reference error, which allows the I-controller to work in concert with the feed-forward part of the controller. The presented algorithm was experimentally verified and its performance was compared with two controllers, an ANFIS controller and a constant gain PID controller, to satisfactory results. The controller was also tested under dynamic loading conditions showing promising results.

Variable stiffness actuator (VSA) designs are manifold. Conventional model-based control of these nonlinear systems is associated with high effort and design-dependent assumptions. In contrast, machine learning offers a promising alternative as models are trained on real measured data and nonlinearities are inherently taken into account. Our work presents a universal, learning-based approach for position and stiffness control of soft actuators. After introducing a soft pneumatic VSA, the model is learned with input-output data. For this purpose, a test bench was set up which enables automated measurement of the variable joint stiffness. During control, Gaussian processes are used to predict pressures for achieving desired position and stiffness. The feedforward error is on average 11.5% of the total pressure range and is compensated by feedback control. Experiments with the soft actuator show that the learning-based approach allows continuous adjustment of position and stiffness without model knowledge.

YOLO is a state of the art, real-time object detection algorithm created by Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi in 2015 and was pre-trained on the COCO dataset. It uses a single neural network to process an entire image. The image is divided into regions and the algorithm predicts probabilities and bounding boxes for each region. YOLO is well-known for its speed and accuracy and it has been used in many applications like: healthcare, security surveillance and self-driving cars. Since 2015 the Ultralytics team has been working on improving this model and many versions since then have been released.

An expert on voice recognition and speech technologies responds to Ysabelle Cheung's "Galatea." When Joseph Faber invented the Euphonia, a mid-19th century analog voice synthesizer, people weren't impressed. They found Faber's invention to be a strange device with little to no purpose. In an attempt to create a machine that could mimic human speech, Faber was physically tethered to his invention, manipulating its bellows, gears, and hardware to produce human-like utterances--from short speeches to ghostly renditions of "God Save the Queen"--with a flat affect. One version of the machine was designed with a feminine face attached to its bellows, hair in ringlets and fair, smooth-looking skin.

Hope you are doing well. We're in December and is time to review the year that passed and evaluate how well we performed. And of course as Machine Learning Engineers we would not be able to do that without also evaluating our classification algorithms! So... Grab a cup of coffee because we are going to talk about four important metrics for evaluating our models in this article. Suppose we are a classification algorithm that is predicting whether or not is going to rain.

Last week, OpenAI removed the waitlist for the application programming interface to GPT-3, its flagship language model. Now, any developer who meets the conditions for using the OpenAI API can apply and start integrating GPT-3 into their applications. Since the beta release of GPT-3, developers have built hundreds of applications on top of the language model. But building successful GPT-3 products presents unique challenges. You must find a way to leverage the power of OpenAI's advanced deep learning models to provide the best value to your users while keeping your operations scalable and cost-efficient.