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#319: Micro-scale Surgical Robots, with Eric Diller

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Dr. Diller received his B.S. and M.S. in Mechanical Engineering at Case Western Reserve University, and Ph.D. at Carnegie Mellon University in 2013. His work is enabling a new approach to non-invasive medical procedures, micro-factories and scientific tools. He does this by shrinking the mechanical and electrical components of robots to centimeter, millimeter or even micrometer size. He uses magnetic fields and other smart-material actuation methods to make mobile functional devices. Dr. Diller envisions a future where drug delivery and surgery can be done in a fast, painless and focused way, and where new materials and devices can be manufactured using swarms of tiny gripping, cutting, and sensing wireless robots.


Robot takes contact-free measurements of patients' vital signs

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During the current coronavirus pandemic, one of the riskiest parts of a health care worker's job is assessing people who have symptoms of Covid-19. Researchers from MIT, Boston Dynamics, and Brigham and Women's Hospital hope to reduce that risk by using robots to remotely measure patients' vital signs. The robots, which are controlled by a handheld device, can also carry a tablet that allows doctors to ask patients about their symptoms without being in the same room. "In robotics, one of our goals is to use automation and robotic technology to remove people from dangerous jobs," says Henwei Huang, an MIT postdoc. "We thought it should be possible for us to use a robot to remove the health care worker from the risk of directly exposing themselves to the patient."


Can RL from pixels be as efficient as RL from state?

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A remarkable characteristic of human intelligence is our ability to learn tasks quickly. Most humans can learn reasonably complex skills like tool-use and gameplay within just a few hours, and understand the basics after only a few attempts. This suggests that data-efficient learning may be a meaningful part of developing broader intelligence. On the other hand, Deep Reinforcement Learning (RL) algorithms can achieve superhuman performance on games like Atari, Starcraft, Dota, and Go, but require large amounts of data to get there. Achieving superhuman performance on Dota took over 10,000 human years of gameplay. Unlike simulation, skill acquisition in the real-world is constrained to wall-clock time.


#318: Humanized Intelligence in Academia and Industry, with Ayanna Howard

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Professor Howard describes her wide range of work in robotics, from robots that assist children with special needs to trust in autonomous systems. Ayanna Howard Ayanna Howard is a Professor and Chair of the School of Interactive Computing at Georgia Tech. Professor Howard is the director and founder of the Human-Automation Systems (HumAnS) Laboratory. Her research focuses on humanized intelligence, with a wide range of applications from Human-Robot Interaction to science-driven robotics. Prior to Georgia Tech, she led research projects at NASA's Jet Propulsion Laboratory.


Cutting surgical robots down to size

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Minimally invasive laparoscopic surgery, in which a surgeon uses tools and a tiny camera inserted into small incisions to perform operations, has made surgical procedures safer for both patients and doctors over the last half-century. Recently, surgical robots have started to appear in operating rooms to further assist surgeons by allowing them to manipulate multiple tools at once with greater precision, flexibility, and control than is possible with traditional techniques. However, these robotic systems are extremely large, often taking up an entire room, and their tools can be much larger than the delicate tissues and structures on which they operate. A collaboration between Wyss Associate Faculty member Robert Wood, Ph.D. and Robotics Engineer Hiroyuki Suzuki of Sony Corporation has brought surgical robotics down to the microscale by creating a new, origami-inspired miniature remote center of motion manipulator (the "mini-RCM"). The robot is the size of a tennis ball, weighs about as much as a penny, and successfully performed a difficult mock surgical task, as described in a recent issue of Nature Machine Intelligence. "The Wood lab's unique technical capabilities for making micro-robots have led to a number of impressive inventions over the last few years, and I was convinced that it also had the potential to make a breakthrough in the field of medical manipulators as well," said Suzuki, who began working with Wood on the mini-RCM in 2018 as part of a Harvard-Sony collaboration.


#317: Environmental Monitoring with the SlothBot, with Gennaro Notomista

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Gennaro discusses the SlothBot, a solar-powered robot that slowly traverses wires, like its animal namesake, to monitor the environment. Gennaro Notomista is a robotics PhD student in the Georgia Robotics and InTelligent Systems Laboratory at Georgia Tech. Gennaro studies control frameworks, with the goal of making robots robust against a changing environment so they can handle long-duration deployments. Toward this goal, he explores constraints-driven control and approaches to coverage control, or enabling robots to traverse closed environments. In addition to the SlothBot, Gennaro has applied his research to areas such as autonomous driving and swarm robotics.


Data systems that learn to be better

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Big data has gotten really, really big: By 2025, all the world's data will add up to an estimated 175 trillion gigabytes. For a visual, if you stored that amount of data on DVDs, it would stack up tall enough to circle the Earth 222 times. One of the biggest challenges in computing is handling this onslaught of information while still being able to efficiently store and process it. A team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) believe that the answer rests with something called "instance-optimized systems." Traditional storage and database systems are designed to work for a wide range of applications because of how long it can take to build them -- months or, often, several years.


#316: Introspective Robots, with Nathan Michael

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They discuss introspection, adaptation, and evolvement in robotics. Michael speaks about topics in state estimation and distributed perception, and other challenges in control, perception, and cognition for both single and multi-robot systems. Nathan Michael is an Associate Research Professor and the Director of the Resilient Intelligent Systems Lab at the Robotics Institute within Carnegie Mellon University (CMU). Professor Michael's research seeks to enable the development of self-sufficient robots and teams of robots that can respond to disasters; robots which can perform the tasks of first responders in order to reduce the number of people placed in harm's way. His research employs the principles of resilient intelligence and persistent knowledge to enable robots to innovate to solve complex problems, to overcome unanticipated challenges and to learn from their experiences.


Soft robotics actuators heal themselves

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Repeated activity wears on soft robotic actuators, but these machines' moving parts need to be reliable and easily fixed. Now a team of researchers has a biosynthetic polymer, patterned after squid ring teeth, that is self-healing and biodegradable, creating a material not only good for actuators, but also for hazmat suits and other applications where tiny holes could cause a danger. "Current self-healing materials have shortcomings that limit their practical application, such as low healing strength and long healing times (hours)," the researchers report in today's (July 27) issue of Nature Materials. The researchers produced high-strength synthetic proteins that mimic those found in nature. Like the creatures they are patterned on, the proteins can self-heal both minute and visible damage.


Exploring the DARPA SubTerranean Challenge

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The DARPA Subterranean (SubT) Challenge aims to develop innovative technologies that would augment operations underground. On July 20, Dr Timothy Chung, the DARPA SubTChallenge Program Manager, joined Silicon Valley Robotics to discuss the upcoming Cave Circuit and Subterranean Challenge Finals, and the opportunities that still exist for individual and team entries in both Virtual and Systems Challenges, as per the video below. The SubT Challenge allows teams to demonstrate new approaches for robotic systems to rapidly map, navigate, and search complex underground environments, including human-made tunnel systems, urban underground, and natural cave networks. The SubT Challenge is organized into two Competitions (Systems and Virtual), each with two tracks (DARPA-funded and self-funded). Teams in the Systems Competition completed four total runs, two 60-minute runs on each of two courses, Experimental and Safety Research.