Machine Learning in Robotics
Machine learning in robotics to learn from mistakes and adapt. People get smarter through experience. Thanks to technologies such as machine learning, robotic applications can have the same capacity. When this happens, they may not need continuous and intensive training from humans.
The following overview of machine learning applications in robotics highlights five key areas where machine learning has had a significant impact on robotic technologies, both now and in stages of development for future use. While by no means inclusive, the purpose of the summary is to give readers an idea of the types of machine learning applications in robotics and stimulate the desire for further research in these areas and others.
5 Current Applications of Machine Learning in Robotics
1. Computer Vision
Although related, some would say that the correct term is machine vision or robotic vision rather than computer vision. Because “robots that see” involve more than just computer algorithms.
Engineers and robotics specialists also need to consider camera hardware that enables robots to process biological data. Robotic vision is closely related to the artificial picture. Its credit can give for the emergence of automated guidance and automatic review systems.
The slight difference between the two may be in the kinematics applied to the robot’s vision. Which encompasses the reference frame calibration and a robot’s ability to affect its surroundings physically. An influx of big data, i.e., graphic information obtainable on the web (including annotated/tagged photos and videos), has resulted in advancements in computer vision.
This has helped promote machine learning-based structured prediction learning techniques at universities like Carnegie Mellon and elsewhere. It leading to robotic vision applications such as object identification and classification.
A resulting example is detecting anomalies with unsupervised learning, such as building systems capable of finding and evaluating defects in silicon wafers using convolutional neural networks. It’s designed by researchers from Biomimetic Robotics and Machine Learning Laboratory, the Assistenzrobotik association. eV in Munich.
Extrasensory technologies such as radar, lidar, and ultrasound, like those from Nvidia, also drive the development of systems based on a 360-degree vision for autonomous vehicles and drones.
2. Learning by Imitation
Imitation learning closely relates to observational learning, a behavior exhibited by infants and young children. Imitation learning is also a general category of reinforcement learning or the challenge of getting an agent to act in the world to maximize its rewards. Bayesian or probabilistic models are a common sense of this machine learning approach. The question of whether imitation learning could use for humanoid-type robots pose as early as 1999.
Imitation learning has developed an integral part of field robotics. The characteristics of mobility outside a factory environment in construction, agriculture, search and rescue, l army, and others make it difficult to program robotic solutions manually.
Examples include inverse optimal control methods, or “demonstration programming,” which have been applied by CMU. And other organizations in the fields of humanoid robotics, legged locomotion, and all-terrain mobile navigators, and All-Terrain. Arizona State researchers released this video two years ago that shows a humanoid robot using imitation learning to acquire different gripping techniques:
Bayesian belief networks have also applied to advanced learning models, in which a robot learns without a priori knowledge of its motor system or the external setting. An example of this is “motor jabbering,” as established by Robotics. And Language Acquisition Group at the University of Illinois at Urbana-Champaign (UIUC) with Bert, the humanoid robot “iCub.”
3. Self-Supervised Learning
Self-supervised learning approaches allow robots to generate their training examples to improve performance. This includes using a priori training and data captured at close range to interpret “long-range ambiguous sensor data.” It has integrated into robots and optical devices capable of detecting and rejecting objects (dust and snow, for example); identify vegetables and obstacles on rough terrain, and in 3D scene analysis and vehicle dynamics modeling
The Watch-Bot is a real-life example created by researchers at Cornell and Stanford that uses a 3D sensor (a Kinect), a camera, a laptop. And a laser pointer to detect “normal human activity,” which are models learns by probabilistic methods.
The Watch-Bot uses a laser pointer to point at the object as a reminder (for example, milk left out of the refrigerator). In initial testing, the bot managed to remind humans 60% of the time (it has no idea what it’s doing or why). The researchers have extended testing by allowing their robot to learn how to do it from videos online (called the RoboWatch project).
Other examples of self-supervised learning methods practical in robotics include a path detection algorithm in a frontal monocular camera with a probabilistic path distribution model (RPDM). And fuzzy support vector machines (FSVM), designed in the MIT autonomous vehicles and other mobiles robots on the road
Autonomous learning, a variation of self-supervised knowledge involving deep understanding and unsupervised methods, has also been applied to control tasks and robots.
In teamwork with researchers from the University of Cambridge and the University of Washington. A team from Imperial College London created a new method to accelerate learning that incorporates model uncertainty (a probabilistic model) into long-term planning. And training controllers, thus reducing the effect of the model mistakes when learning a new skill.
4. Assistive and Medical Technologies
An assistive robot (according to David L. Jaffe of Stanford) is a device capable of detecting, processing sensory information, and performing actions that benefit people with disabilities. And the elderly (although there are also smart assistive technologies for the general population, like driving AIDS.). Motion therapy robots provide a diagnostic or therapeutic benefit. Both technologies are largely (and sadly) still confined to the laboratory. As they remain prohibitively expensive for most hospitals in the United States and abroad.
Early examples of assistive technology included DeVAR or Professional Office Assistant Robot. It develops in the early 1990s by Stanford and Palo Alto Veterans Affairs Rehabilitation Research and Development. More new examples of machine learning-based robotic assistive technology set.
They include the combination of assistive machines with more autonomy, such as the MICO robotic arm (developed at Northwestern University) that observes the world through a Kinect sensor. The implications are more complex, but smarter assistive robots are more easily adapted to user needs. And require partial autonomy (i.e., shared control between robots and humans).
In the medical world, loans in machine learning methodologies applied to robotics are advancing rapidly, although they are not available in many medical facilities. Collaboration finished the Cal-MR: Center for Automation and Learning for Medical Robotics, between investigators from several universities. And a network of doctors (collaborations with researchers from several universities).
And doctors led to the creation of the Smart Tissue Autonomous Robot (STAR), tested through the National Children’s Health System in DC using autonomous learning and 3D sensing innovations.
STAR can unite the “pig intestines” (used instead of tissue) with greater accuracy. And reliability than top surgeons, researchers, and physicians. STAR does not replace surgeons who would be left around to manage emergencies for the foreseeable future. But offers significant benefits by performing similar delicate surgeries.
5. Multi-Agent Learning
Coordination and negotiation are key elements of multi-agent learning, which involves robots (or agents) based on machine learning; this technique has widely applied to games) which can adapt to a changing landscape of other robots/agents and find the “balancing strategies.” Examples of multi-agent learning approaches include remorse-free learning. It involves weighted algorithms that “drive” learning outcomes in multi-agent preparation and learning in market-based distributed control systems.
A more real example is an algorithm for distributed go-betweens or robots created by researchers at MIT Decision and Information Systems Laboratory in late 2014. The robots collaborated to build a better and more inclusive learning model. They could do with a single robot (smaller pieces of information processed and combined). It bases on exploring a building and its parts and building a knowledge base on its own.
Each robot built its catalog and, combined with the datasets of other robots. The distributed algorithm outperformed the standard algorithm in creating this knowledge base. While not a perfect system, this kind of machine learning approach. It allows robots to compare catalogs or datasets, reinforce mutual observations, and correct oversights or omissions. And will undoubtedly play a role soon in various robotic applications, including multiple autonomous piles of earth and air vehicles.
The above brief description of machine learning-based approaches in robotics, combined with contracts. And challenges offered by powerful military sponsors (e.g., DARPA, ARL); innovations from major robotics manufacturers (e.g., Silicon Valley Robotics).
And startups (Mayfield Robotics); And the increased investment from a barrage of automakers (from Toyota to BMW) in the next generation of autonomous vehicles indicates. The machine learning trend is a long-term priority.
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