The combination of elasticity and rigidity found within mammalian limbs enables dexterous manipulation, agile, and versatile behavior, yet most modern robots are either primarily soft or rigid. Most mammals have ligaments that connect bone to bone, enabling joints to passively redirect forces and softly constrain the range of motion. Hybrid robots, composed of both soft and rigid parts, promote compliance to external forces while maintaining strength and stability provided by rigid robots.Natural manipulators, such as the human arm, have been shaped by the long-term optimization of evolution. They tend to be extremely versatile, having the dexterity to work with various objects and environments. The hybrid composition of rigid and soft components– including bones, muscles, and connective tissues–yield inherent compliance and flexibility. Biological joints often have passive stability and elasticity that create mechanical feedback that benefits disturbance responses. In addition, recent progress in the mechanical complexity of robots has popularized embedding intelligence within the system. Such robots may inherently dampen motion through elastic components or enable complicated movements emerging from simple actuation, e.g., origami robots.In contrast, traditional robot arms tend to feature rigid components that are susceptible to large moments propagating throughout the entire robot. This means the robot’s structural integrity can be compromised by a large unpredictable disturbance. Robotic manipulators involving rigid joints have strictly defined degrees of freedom resulting from the mechanical design. These joints typically fit within three categories: prismatic joints (linear movement on an axis), revolute joints (rotational movement around an axis), or a combination of the two.Rigid robotic joints are often actuated by motors that change their position directly, allowing straightforward kinematic models to calculate the joint's position. Due to their dynamics, traditional feedback control systems, such as a proportional integral derivative (PID) controller, can solve this problem relatively well, with modifications that can adapt to the influences of gravity. One modification to account for nonlinearities involves feed-forward neural networks with PID input features.While these dynamics are effective within controlled environments, rigid robots pose dangers to both themselves and humans because of their intrinsic inability to deal with external forces. In environments where humans are directly interfacing with robots–such as industrial manufacturing or telepresence -- the robot’s lack of compliance can put workers and civilians at risk of injury. Measures have been taken to increase the safety of these robots, but they are not innately safe. Factors including intrinsic safety, human detection, and control techniques influence the overall safety. The risk that a robot will cause physical harm has also been shown to moderate people’s willingness to work with the robot. Strategies such as safety fences and human-detection increase safety but limit the human-robot collaboration.Flexible robots can mitigate these external forces through structural compliance while maintaining morphological similarities with rigid robots. Systems such as soft robots and tensegrity-based robots with elastic components are inherently compliant. Biologically inspired approaches tend to exemplify this behavior. The motion of legged tensegrity structures has been validated by biological simulations while simplifying the underlying bone-ligament architecture. From a bio-mimicry perspective, a human finger has been functionally recreated through oneshot three-dimensional (3D) printing techniques employing both rigid and elastic components. Soft robots can provide safe human interaction, resulting in safer environments. Soft cable driven exo-suits can be compliant while avoiding obstruction to the user’s range of motion. Intelligent design approaches have even resulted in programmable tensegrities. However, due to the non-linearities within the elastic components, these compliant robots tend to require complex models in order to be controlled properly.Soft robotics made from elastic components increase compliance while often sacrificing stability and precision. An accurate model of the system would enable the use of modern control techniques which can provide optimal solutions. Optimal control finds the proper control values which optimize an objective function based on the system model. A Linear-Quadratic Regulator (LQR) solves the problem of minimizing a quadratic cost matrix (encoding weights of errors, energy use, etc.) over a specified time horizon, however it is expensive and demands accurate models. Model predictive control optimizes a finite time-horizon window that is repeatedly solved at each new time-step, reducing computational cost while enabling anticipation of future events. To create the model, a common method involves system identification, which can estimate the dynamics based on measurements.However, noise in the design process can breed inconsistencies in production, and the non-linear nature of flexible robots further complicates modeling. This emphasizes the need for control methods that can learn from data. One potential solution for controlling this variation in robots involves having precisely adjusted models for each physical instance. But these approaches are cumbersome due to the requirement of constructing precise models. Thus, there is a need for a system and method of controlling soft-rigid hybrid robotic joints that overcome the deficiencies of the conventional control methodologies. 

A patent-pending platform technology designed to work in any gravity, which includes in microgravity environments, able to execute advanced molecular biology workflows; representing a paradigm shift in automation for molecular biology.

The use of telemedicine/telehealth increased substantially during the COVID-19 pandemic, leading to its accelerated development, utilization and acceptability. Telehealth momentum with patients, providers, and other stakeholders will likely continue, which will further promote its safe and evidence-based use. Improved healthcare by telehealth has also extended to musculoskeletal care. In a recent study looking at implementation of telehealth physical therapy in response to COVID-19, almost 95% of participants felt satisfied with the outcome they received from the telehealth physical therapy (PT) services, and over 90% expressed willingness to attend another telehealth session. While telehealth has enhanced accessibility by virtual patient visits, certain physical rehabilitation largely depends on physical facility and tools for evaluation and therapy. For example, limb kinematics in PT with respect to the shoulder joint is difficult to evaluate remotely, because the structure of the shoulder allows for tri-planar movement that cannot be estimated by simple single plane joint models. With the emergence of gaming technologies, such as videogames and virtual reality (VR), comes new potential tools for virtual-based physical rehabilitation protocols. Some research has shown digital game environments, and associated peripherals like immersive VR (iVR) headsets, can provide a powerful medium and motivator for physical exercise. And while low-cost motion tracking systems exist to match user movement in the real world to that in the virtual environment, challenges remain in bridging traditional PT tooling and telehealth-friendly physical rehabilitation.

Researchers at the University of California, Davis have developed an automated harvesting system using predictive scheduling for crop-transport robots, reducing manual labor, and increasing harvesting efficiency.

During initial stages of development, the human brain self assembles from a vast network of billions of neurons into a system capable of sophisticated cognitive behaviors. The human brain maintains these capabilities over a lifetime of homeostasis, and neuroscience helps us explore the brain’s capabilities. The pace of progress in neuroscience depends on experimental toolkits available to researchers. New tools are required to explore new forms of experiments and to achieve better statistical certainty.Significant challenges remain in modern neuroscience in terms of unifying processes at the macroscopic and microscopic scale. Recently, brain organoids, three-dimensional neural tissue structures generated from human stem cells, are being used to model neural development and connectivity. Organoids are more realistic than two-dimensional cultures, recapitulating the brain, which is inherently three-dimensional. While progress has been made studying large-scale brain patterns or behaviors, as well as understanding the brain at a cellular level, it’s still unclear how smaller neural interactions (e.g., on the order of 10,000 cells) create meaningful cognition. Furthermore, systems for interrogation, observation, and data acquisition for such in vitro cultures, in addition to streaming data online to link with these analysis infrastructures, remains a challenge.

 Researchers from UC San Diego developed Pointillism, a system that enables radars to overcome the challenges posed by specular reflections, sparsity and noise in the radar point clouds, to provide high-fidelity perception of the scene with 3D bounding boxes. Pointillism consists of multiple low-resolution radars placed in a optimal fashion to maximize the spatial diversity and scene information. Pointillism combines this spatial diversity with novel multi-radar fusion algorithms to tackle the problem of specular reflections, sparsity and noise in radar point clouds. Building upon the hardware and algorithms, Pointillism also introduces a novel data-driven approach that enables the detection of multiple dynamic objects in the scene, with their accurate location, orientation and 3D dimensions. Furthermore, Pointillism enables such perception even in inclement weather, thereby paving a way for radar to be the main-stream sensor for autonomous perception.

Brief description not available

Brief description not available

The inventors have developed a novel 3D printing technique, named Non-Planar Granular 3D Printing (NGP), which selectively deposits a liquid binder into granular particles, enabling rapid fabrication of complex 3-dimensional objects. For this new method, an industrial robotic arm is equipped with a dispenser attached to a long metal needle, called a liquid deposition end-effector, and a container of granular particles, such as sand, beads, or powders. The needle moves freely as it injects the binding liquid into the granular material. Like other 3D printing methods, NGP can use a CAD 3D model and conventional slicing software to produce a robotic toolpath following a desired height and width. However, the advantage of the process lies in its ability to 3D print objects non-planarly, by moving the extruder’s dispensing tip freely within the granular medium. The selective application of the binding liquid causes the particles to bond together, forming parts of the 3D printed object. Meanwhile, the loose particles remaining in the container temporarily support the weight of the wet particles while they cure. This unique approach enables the creation of complex geometric forms without the need for supporting structures that are typical in traditional 3D printing methods, thereby eliminating material waste typically associated with such processes. After the completion of the process, and the binding material has cured, the hard objects can be easily extracted from the container, leaving behind the remaining loose particles, which can be repeatedly re-used.   

Brief description not available

Brief description not available

Contact force is a natural way for humans to interact with the physical world around us. However, most of our interactions with the digital world are largely based on a simple binary sense of touch (contact or no contact). Similarly, when interacting with robots to perform complex tasks, such as surgery, we need to acquire the rich force information and contact location, to aid in the task. To address these issues, researchers at UC San Diego have developed WiForce, which is a ‘wireless’ sensor that can be attached to an object or robot, like a sticker. WiForce’s sensor transduces force magnitude and location into phase changes of an incident RF signal, which is reflected back to enable measurement of force and contact location.

Researchers at the University of California, Davis have developed a system consisting of cameras and multi-wavelength lasers that is capable of precisely locating and inspecting items.

Researchers at the University of California, Davis have developed a programmable machine that shakes and repeatedly inverts large numbers of small containers - such as vials and flasks – in order to mix high-viscosity fluids.

3D printing of concrete structures is a highly efficient, cheap process. However, imperfections are difficult to detect and can compromise the performance of these structures. UCI researchers have developed a method in which a current sent through the printed structure produces a “fingerprint” that allows the real-time detection of flaws in the concrete structure.

Adhesion involves highly complex and hierarchical structures in nature, and by understanding the biological intricacies of such adhesive structures, one can improve engineered adhesives. The role of reversible adhesion in both the natural world and in engineering is to temporarily bind to a surface, providing the opportunity to detach and re-attach as needed. In nature, animals use attachment to enhance their fitness.  In robotics, reversible adhesion enables improved manipulation and locomotion by managing contact at the interface between the robot and its environment.

Researchers at the University of California, Irvine have developed a guided electrokinetic assembly technique that utilizes dielectrophoretic and electroosmotic forces for micro- and nanomanufacturing. This technique provides a new way for assembling microelectronics and living cells for tissue engineering applications.

Vacuum grippers are widely used in industry to handle objects via suction pressure. Unicontact suction cups are commonly used for gripping because they are simple to operate and can handle a variety of items, including those that are delicate, large, or inaccessible to jaw grippers. However, suction cup grippers have challenges such as planning a contact location and inertial force-induced grasping failure. To address these challenges, UC Berkeley researchers developed a tactile sensing technology for smart suction cups. This Berkeley sensing technology can detect suction contact and prevent suction cup grasp failures. It can perform tactile sensing of object properties such as roughness or porosity that might lead to grasping failures before they happen. If a grasp failure does happen, the technology gains additional information about why and how the failure occurred　to prevent similar failures in future attempts. Sensing occurs quickly, such that robot behavior can remain fast while increasing performance, efficiency and reliability.　 As compared with other robotic grasping sensing technologies, this smart suction cup technology is affordable, resilient and easy to service. The cup is manufactured using the same process as other suction cups, and electronics are simple and located away from the point-of-contact and protected from damage or hazardous exposure.

Prof. Konstantinos Karydis’ lab at the University of California, Riverside has developed a new active resilient quadrotor (ARQ), which incorporates passive springs within its frame to absorb shocks and survive collisions.  Each arm of the quadrotor is equipped with sensors to accurately and rapidly detect the location (in the drone’s frame) and intensity of a collision.  In addition, a recovery controller that enables the drone to sustain flight after collision with objects like wall, poles, or moving objects. The technology has been proven on the quadrotor however it may be applied to drones with more than four arms. Fig 1: Instances of the novel ARQ drone detecting and recovering from colllisions in (a) and (b) and from collision with a wall (c) and (d). Fig 2: shows ARQ detecting and recovering from a passive collision. (a) ARQ hovers. (b) Collision starts and the ARQ arm absorbs the shock. (c) recovery control starts and there is a body interfering with the ARQ’s flight path. (d) ARQ is stabilized and hovering again.  

The invention is a smart non-intrusive workflow assessment platform for monitoring and optimizing manufacturing environments. The platform monitors environmental and energy metrics, and provides learning models to classify workers’ activities and relate them to the equipment utilization and performance. Correlating both stream of data enables both workers and supervisors to improve the efficiency of the whole manufacturing process and at an affordable price.

Prof. Konstantinos Karydis’ lab at the University of California, Riverside has developed a soft hexapedal robot (SoRx) that may serve as a new tool to applications where operation over rough and/or unstructured terrain is required.  For example when looking for survivors in the aftermath of an earthquake this soft legged robot may be easily deployed. Operation in such terrains still challenges more rigid legged robots; instead, soft legged robots could squeeze and bend to overcome obstacles and fit into crevices to explore their environment. Other uses of SoRX may include educational and recreational applications.       Fig 1: shows that SoRX maintains stable locomotion on an unstable platform that is oscillating in the X-Y plane at speeds comparable to the robot’s forward speed,

UCLA researchers in the Department of Bioengineering have developed a novel machine learning assisted wearable sensor system for the direct translation of sign language into voice with high performance.

Researchers at the University of California, Davis have developed a robotic dispensing interface that uses a microfluidic-embedded container cap – often referred to as a microfluidic Cap-to-Dispense or μCD - to seamlessly integrate robotic operations into precision liquids handling.

UCLA researchers in the Departments of Bioengineering and Head & Neck Surgery have developed a novel robotic platform for the training of transoral surgery.

Researchers at the University of California, Davis have developed a controller that applies environmental data to optimizing operations of livestock cooling equipment.