Introducing the Robi Humanoid Robot: Revolutionizing Industrial and Healthcare Applications
The Robi Humanoid Robot, designed by Oversonic Robotics, is a groundbreaking technology that is set to revolutionize the industrial and healthcare sectors. The robot stands at approximately five and a half feet tall and weighs around 70 kilograms. Equipped with artificial intelligence, it is capable of performing a wide range of functions, making it highly useful in both industrial and social contexts. In this article, we will explore the key features of the Robi Humanoid Robot and its applications in various industries.
Design and Build
The Robi Humanoid Robot was developed over a period of more than two years and is one of the very first humanoid robots made in Italy with an aim towards industrial automation. The robot’s mechanical structure mimics the human body thanks to 40 moving joints and a set of sensors and cameras that allow it to monitor surrounding spaces, even if they are crowded. Equipped with arms and gripping joints such as robotic hand grippers for pick-and-place tasks, the robot can work for eight hours on its own battery power and is guided by three on-board computers.
Functionality
The Robi Humanoid Robot has a range of features that make it highly versatile in a variety of industry sectors. It is capable of performing heavy or repetitive tasks in risky or unsanitary conditions, reducing the risk of injury or illness for human workers. Additionally, it can communicate with humans by voice, making it easy to operate and collaborate with.
Cloud-Based Ecosystem
The Robi Humanoid Robot’s artificial intelligence system allows different functions to be activated depending on the people with whom it interacts. Moreover, a cloud-based ecosystem makes it possible to monitor the functions of the various components, optimizing maintenance to avoid sudden shutdowns or malfunctions. This ensures that the robot is always functioning at its best, reducing downtime and increasing productivity.
Cost and Availability
After a period of testing with some partner companies, Oversonic Robotics plans to subject the Robi Humanoid Robot to the necessary certifications and begin selling this year. 50 sales are planned for 2022 with purchase cost of 120,000 euros and an annual management fee of 20,000 euros. The devices plan to be mass-produced in different versions, even customized according to needs, and there are already pre-orders.
Applications
In addition to heavier tasks in industry, the Robi Humanoid Robot will lend itself to functions in business, healthcare, and hospitality. It can even autonomously remove its gripper device to replace it with another more suitable device depending on the task it is working on at the time. Its battery system also includes an induction charging function to enable wireless charging. This humanoid robot is capable of combining a strategic cloud-based cognitive platform and industrial-scale operation to balance itself between an industry 5.0 approach while following the most innovative trends in social robotics.
Conclusion
In conclusion, the Robi Humanoid Robot is a promising technology that has the potential to revolutionize the industrial and healthcare sectors. With its advanced features, versatility, and cloud-based ecosystem, it paves the way for increased productivity, efficiency, and safety in various industries. It is no doubt that this state-of-the-art technology will change the way things are done in factories, hospitals, and other workplaces where labor-intensive tasks are the norm.
Learning to Walk in One Hour: The Robot Dog Experiment
Researchers at the Max Planck Institute for Intelligent Systems (MPI) in Stuttgart conducted a research study to find out how animals learn to walk and learn from stumbling. They built a four-legged dog-sized robot that helps them figure out the details. After learning to walk in just one hour, the robot dog makes good use of its complex leg mechanics. In this section, we’ll explore the details of this experiment and its implications for robotics.
Mechanics and Optimization
A beige and optimization algorithm guides the learning by measuring foot sensor information to match it with target data from the modeled virtual spinal cord running as a program in the robot’s computer. The robot learns to walk by continuously comparing sent and expected sensor information, running reflex loops, and adapting its motor control patterns. The learning algorithm adapts control parameters of a central pattern generator. The central pattern generator networks aid the generation of rhythmic tasks such as walking, blinking, or digestion. Moreover, reflexes are involuntary motor control actions triggered by hard-coded neural pathways that connect sensors in the leg with the spinal cord.
Adapting to Terrain
As long as the young animal walks over a perfectly flat surface, the central pattern generator can be sufficient to control the movement signals from the spinal cord. A small bump on the ground, however, changes the walk, reflexes kick in, and adjust the movement patterns to keep the animal from falling. These momentary changes in the movement signals are reversible or elastic, and the movement patterns return to their original configuration after the disturbance. But if the animal does not stop stumbling over many cycles of movement, despite active reflexes, then the movement patterns must be relearned.
The Learning Process
In the newborn animal, central pattern generators are initially not yet adjusted well enough, and the animal stumbles around both on even or uneven terrain. But the animal rapidly learns how its central pattern generators and reflexes control leg muscles and tendons. The same holds for the labrador-sized robot dog named Morty. Furthermore, the robot optimizes its movement patterns faster than an animal, requiring only one hour. Morty’s central pattern generator is simulated on a small and lightweight computer that controls the motion of the robot’s legs. This virtual spinal cord is placed on the quadruped robot’s back, where the head would be. During the hour, it takes for the robot to walk smoothly, sensor data from the robot’s feet are continuously compared with the expected touchdown predicted by the robot central pattern generator. If the robot stumbles, the learning algorithm changes how far the legs swing back and forth, how fast the legs swing, and how long the leg is on the ground. The adjusted motion also affects how well the robot can utilize its compliant leg mechanics.
Energy Consumption
Comparing with industrial quadruped robots from prominent manufacturers, which have learned to run with the help of complex controllers, Morty is much more energy-efficient. Their controllers are coded with the knowledge of the robot’s exact mass and body geometry, which use a model of the robot. They typically draw several tens up to several hundred watts of power. Both robot types run dynamically and efficiently, but the computational energy consumption is far lower in the Stuttgart model. It also provides important insights into animal anatomy.
Artificial Intelligence Software that Can Create Proteins
Scientists have developed artificial intelligence software that can create proteins that may be useful as vaccines, cancer treatments, or even tools for pulling carbon pollution out of the air. The research from the journal Science was led by the University of Washington School of Medicine and Harvard University. The article is titled “Scaffolding protein functional sites using deep learning.” In this section, we will explore the scientists’ research and the implications of the neural networks they developed.
Protein Engineering
For decades, scientists have used computers to try to engineer proteins. Some proteins, such as antibodies and synthetic binding proteins, have been adapted into medicines. Others, such as enzymes, aid in industrial manufacturing. But a single protein molecule often contains thousands of bonded atoms that are difficult to study and engineer, even with specialized scientific software.
Neural Networks
The researchers trained multiple neural networks using information from the Protein Data Bank. The neural networks that resulted have surprised even the scientists who created them. The team developed two approaches for designing proteins with new functions. The first, dubbed hallucination, is akin to Doll e or other generative AI tools that produce new output based on simple prompts. The second, dubbed in painting, is analogous to the autocomplete feature found in modern search bars and email clients. Both books and proteins can be understood as long sequences of letters. In the case of proteins, each letter corresponds to a chemical building block called an amino acid.
The Learning Process
Beginning with a random chain of amino acids, the software mutates the sequence over and over until a final sequence that encodes the desired function is generated. These final amino acid sequences encode proteins that can then be manufactured and studied in the laboratory. The team also showed that neural networks can fill in missing pieces of a protein structure in only a few seconds, which could aid in developing new medicines when starting key features they want to see in a new protein. Laboratory testing revealed that many proteins generated through hallucination and in painting functioned as intended. This included novel proteins that can bind metals as well as those that bind the anti-cancer receptor PD1.
Final Thoughts
In conclusion, the development of the Robi Humanoid Robot, the robot dog experiment, and the creation of artificial intelligence software that can create proteins all showcase the vast potential of innovative technology. These cutting-edge developments point to a future where robots and AI will revolutionize and improve various industry sectors, from healthcare to manufacturing. As scientists continue to push the boundaries of artificial intelligence, there is no telling where this technology will take us.