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Speedy collision detector could make robots better human assistants

Electrical engineers at the University of California San Diego have developed a faster collision detection algorithm that uses machine learning to help robots avoid moving objects and weave through complex, rapidly changing environments in real time. The algorithm, dubbed "Fastron," runs up to 8 times faster than existing collision detection algorithms.

A team of engineers, led by Michael Yip, a professor of electrical and computer engineering and member of the Contextual Robotics Institute at UC San Diego, will present the new algorithm at the first annual Conference on Robot Learning Nov. 13 to 15 at Google headquarters in Mountain View, Calif. The conference brings the top machine learning scientists to an invitation-only event. Yip's team will deliver one of the long talks during the 3-day conference.

The team envisions that Fastron will be broadly useful for robots that operate in human environments where they must be able to work with moving objects and people fluidly. One application they are exploring in particular is robot-assisted surgeries using the da Vinci Surgical System, in which a robotic arm would autonomously perform assistive tasks (suction, irrigation or pulling tissue back) without getting in the way of the surgeon-controlled arms or the patient's organs.

"This algorithm could help a robot assistant cooperate in surgery in a safe way," Yip said.

The team also envisions that Fastron can be used for robots that work at home for assisted living applications, as well as for computer graphics for the gaming and movie industry, where collision checking is often a bottleneck for most algorithms.

A problem with existing collision detection algorithms is that they are very computation-heavy. They spend a lot of time specifying all the points in a given space--the specific 3D geometries of the robot and obstacles--and performing collision checks on every single point to determine whether two bodies are intersecting at any given time. The computation gets even more demanding when obstacles are moving.

To lighten the computational load, Yip and his team in the Advanced Robotics and Controls Lab (ARClab) at UC San Diego developed a minimalistic approach to collision detection. The result was Fastron, an algorithm that uses machine learning strategies--which are traditionally used to classify objects--to classify collisions versus non-collisions in dynamic environments. "We actually don't need to know all the specific geometries and points. All we need to know is whether the robot's current position is in collision or not," said Nikhil Das, an electrical engineering Ph.D. student in Yip's group and the study's first author.

The name Fastron comes from combining Fast and Perceptron, which is a machine learning technique for performing classification. An important feature of Fastron is that it updates its classification boundaries very quickly to accommodate for moving scenes, something that has been challenging for the machine learning community in general to do.

Fastron's active learning strategy works using a feedback loop. It starts out by creating a model of the robot's configuration space, or C-space, which is the space showing all possible positions the robot can attain. Fastron models the C-space using just a sparse set of points, consisting of a small number of so-called collision points and collision-free points. The algorithm then defines a classification boundary between the collision and collision-free points--this boundary is essentially a rough outline of where the abstract obstacles are in the C-space. As obstacles move, the classification boundary changes. Rather than performing collision checks on each point in the C-space, as is done with other algorithms, Fastron intelligently selects checks near the boundaries. Once it classifies the collisions and non-collisions, the algorithm updates its classifier and then continues the cycle.

Because Fastron's models are more simplistic, the researchers set its collision checks to be more conservative. Since just a few points represent the entire space, Das explained, it's not always certain what's happening in the space between two points, so the team developed the algorithm to predict a collision in that space. "We leaned toward making a risk-averse model and essentially padded the workspace obstacles," Das said. This ensures that the robot can be tuned to be more conservative in sensitive environments like surgery, or for robots that work at home for assisted living.

The team has so far demonstrated the algorithm in computer simulations on robots and obstacles in simulation. Moving forward, the team is working to further improve the speed and accuracy of Fastron. Their goal is to implement Fastron in a robotic surgery and a homecare robot setting.


Paper title: "Fastron: An Online Learning-Based Model and Active Learning Strategy for Proxy Collision Detection." Authors of the study are Nikhil Das, Naman Gupta and Michael Yip in the Advanced Robotics and Controls Lab (ARClab) at UC San Diego.

Source: https://www.eurekalert.org/pub_releases/2017-11/uoc--scd111317.php

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Founded by Russian entrepreneur Dmitry Itskov in February 2011 with the participation of leading Russian specialists in the field of neural interfaces, robotics, artificial organs and systems.

The main goals of the 2045 Initiative: the creation and realization of a new strategy for the development of humanity which meets global civilization challenges; the creation of optimale conditions promoting the spiritual enlightenment of humanity; and the realization of a new futuristic reality based on 5 principles: high spirituality, high culture, high ethics, high science and high technologies. 

The main science mega-project of the 2045 Initiative aims to create technologies enabling the transfer of a individual’s personality to a more advanced non-biological carrier, and extending life, including to the point of immortality. We devote particular attention to enabling the fullest possible dialogue between the world’s major spiritual traditions, science and society.

A large-scale transformation of humanity, comparable to some of the major spiritual and sci-tech revolutions in history, will require a new strategy. We believe this to be necessary to overcome existing crises, which threaten our planetary habitat and the continued existence of humanity as a species. With the 2045 Initiative, we hope to realize a new strategy for humanity's development, and in so doing, create a more productive, fulfilling, and satisfying future.

The "2045" team is working towards creating an international research center where leading scientists will be engaged in research and development in the fields of anthropomorphic robotics, living systems modeling and brain and consciousness modeling with the goal of transferring one’s individual consciousness to an artificial carrier and achieving cybernetic immortality.

An annual congress "The Global Future 2045" is organized by the Initiative to give platform for discussing mankind's evolutionary strategy based on technologies of cybernetic immortality as well as the possible impact of such technologies on global society, politics and economies of the future.


Future prospects of "2045" Initiative for society


The emergence and widespread use of affordable android "avatars" controlled by a "brain-computer" interface. Coupled with related technologies “avatars’ will give people a number of new features: ability to work in dangerous environments, perform rescue operations, travel in extreme situations etc.
Avatar components will be used in medicine for the rehabilitation of fully or partially disabled patients giving them prosthetic limbs or recover lost senses.


Creation of an autonomous life-support system for the human brain linked to a robot, ‘avatar’, will save people whose body is completely worn out or irreversibly damaged. Any patient with an intact brain will be able to return to a fully functioning  bodily life. Such technologies will  greatly enlarge  the possibility of hybrid bio-electronic devices, thus creating a new IT revolution and will make  all  kinds of superimpositions of electronic and biological systems possible.


Creation of a computer model of the brain and human consciousness  with the subsequent development of means to transfer individual consciousness  onto an artificial carrier. This development will profoundly change the world, it will not only give everyone the possibility of  cybernetic immortality but will also create a friendly artificial intelligence,  expand human capabilities  and provide opportunities for ordinary people to restore or modify their own brain multiple times.  The final result  at this stage can be a real revolution in the understanding of human nature that will completely change the human and technical prospects for humanity.


This is the time when substance-independent minds will receive new bodies with capacities far exceeding those of ordinary humans. A new era for humanity will arrive!  Changes will occur in all spheres of human activity – energy generation, transportation, politics, medicine, psychology, sciences, and so on.

Today it is hard to imagine a future when bodies consisting of nanorobots  will become affordable  and capable of taking any form. It is also hard to imagine body holograms featuring controlled matter. One thing is clear however:  humanity, for the first time in its history, will make a fully managed evolutionary transition and eventually become a new species. Moreover,  prerequisites for a large-scale  expansion into outer space will be created as well.


Key elements of the project in the future

• International social movement
• social network immortal.me
• charitable foundation "Global Future 2045" (Foundation 2045)
• scientific research centre "Immortality"
• business incubator
• University of "Immortality"
• annual award for contribution to the realization of  the project of "Immortality”.

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