Tag Archives: drones

#439826 Autonomous Racing Drones Dodge Through ...

It seems inevitable that sooner or later, the performance of autonomous drones will
surpass the performance of even the best human pilots. Usually things in robotics that seem inevitable happen later as opposed to sooner, but drone technology seems to be the exception to this. We've seen an astonishing amount of progress over the past few years, even to the extent of sophisticated autonomy making it into the hands of consumers at an affordable price.

The cutting edge of drone research right now is putting drones with relatively simple onboard sensing and computing in situations that require fast and highly aggressive maneuvers. In a paper
published yesterday in Science Robotics, roboticists from Davide Scaramuzza's Robotics and Perception Group at the University of Zurich along with partners at Intel demonstrate a small, self-contained, fully autonomous drone that can aggressively fly through complex environments at speeds of up to 40kph.

The trick here, to the extent that there's a trick, is that the drone performs a direct mapping of sensor input (from an Intel RealSense 435 stereo depth camera) to collision-free trajectories. Conventional obstacle avoidance involves first collecting sensor data; making a map based on that sensor data; and finally making a plan based on that map. This approach works perfectly fine as long as you're not concerned with getting all of that done quickly, but for a drone with limited onboard resources moving at high speed, it just takes too long. UZH's approach is instead to go straight from sensor input to trajectory output, which is much faster and allows the speed of the drone to increase substantially.

The convolutional network that performs this sensor-to-trajectory mapping was trained entirely in simulation, which is cheaper and easier but (I would have to guess) less fun than letting actual drones hammer themselves against obstacles over and over until they figure things out. A simulated “expert” drone pilot that has access to a 3D point cloud, perfect state estimation, and computation that's not constrained by real-time requirements trains its own end-to-end policy, which is of course not achievable in real life. But then, the simulated system that will be operating under real-life constraints just learns in simulation to match the expert as closely as possible, which is how you get that expert-level performance in a way that can be taken out of simulation and transferred to a real drone without any adaptation or fine-tuning.

The other big part of this is making that sim-to-real transition, which can be problematic because simulation doesn't always do a great job of simulating everything that happens in the world that can screw with a robot. But this method turns out to be very robust against motion blur, sensor noise, and other perception artifacts. The drone has successfully navigated through real world environments including snowy terrains, derailed trains, ruins, thick vegetation, and collapsed buildings.

“While humans require years to train, the AI, leveraging high-performance simulators, can reach comparable navigation abilities much faster, basically overnight.” -Antonio Loquercio, UZH

This is not to say that the performance here is flawless—the system still has trouble with very low illumination conditions (because the cameras simply can't see), as well as similar vision challenges like dust, fog, glare, and transparent or reflective surfaces. The training also didn't include dynamic obstacles, although the researchers tell us that moving things shouldn't be a problem even now as long as their speed relative to the drone is negligible. Many of these problems could potentially be mitigated by using
event cameras rather than traditional cameras, since faster sensors, especially ones tuned to detect motion, would be ideal for high speed drones.

The researchers tell us that their system does not (yet) surpass the performance of expert humans in these challenging environments:

Analyzing their performance indicates that humans have a very rich and detailed understanding of their surroundings and are capable of planning and executing plans that span far in the future (our approach plans only one second into the future). Both are capabilities that today's autonomous systems still lack. We see our work as a stepping stone towards faster autonomous flight that is enabled by directly predicting collision-free trajectories from high-dimensional (noisy) sensory input.

This is one of the things that is likely coming next, though—giving the drone the ability to learn and improve from real-world experience. Coupled with more capable sensors and always increasing computer power, pushing that flight envelope past 40 kph in complex environments seems like it's not just possible, but inevitable. Continue reading

Posted in Human Robots

#439568 Corvus Robotics’ Autonomous Drones ...

Warehouses offer all kinds of opportunities for robots. Semi-structured controlled environments, lots of repetitive tasks, and humans that would almost universally rather be somewhere else. Robots have been doing great at taking over jobs that involve moving stuff from one place to another, but there are all kinds of other things that have to happen to keep warehouses operating efficiently.

Corvus Robotics, a YC-backed startup that's just coming out of stealth, has decided that they want to go after warehouse inventory tracking. That is, making sure that a warehouse knows exactly what's inside of it and where. This is a more complicated task than it seems like it should be, and not just any robot is able to do it. Corvus' solution involves autonomous drones that can fly unattended for weeks on end, collecting inventory data without any human intervention at all.

Many warehouses have a dedicated team of humans whose job is to wander around the warehouse scanning stuff to maintain an up to date list of where everything is, a task which is both very important and very boring. As it turns out, autonomous drones can scan up to ten times faster than humans—Corvus Robotics' drones are able to inventory an entire warehouse on a rolling basis in just a couple days, while it would take a human team weeks to do the same task.

Inventory is a significant opportunity for robotics, and we've seen a bunch of different attempts at doing inventory in places like supermarkets, but warehouses are different. Warehouses can be huge, in every dimension, meaning that the kinds of robots that can make supermarket inventory work just won't cut it in a warehouse environment for the simple reason that they can't see inventory stacked on shelves all the way to the ceiling, which can be over 20m high. And this is why the drone form factor, while novel, actually offers a uniquely useful solution.
It's probably fair to think of a warehouse as a semi-structured environment, with emphasis on the “semi.” At the beginning of a deployment, Corvus will generate one map of the operating area that includes both geometric and semantic information. After that, the drones will autonomously update that map with each flight throughout their entire lifetimes. There are walls and ceilings that don't move, along with large shelving units that are mostly stationary, but those things aren't going to do your localization system any favors since they all look the same. And the stuff that does offer some uniqueness, like the items on those shelves, is changing all the time. “That's a huge problem for us,” says Mohammed Kabir, Corvus Robotics' CTO. “Being able to do place recognition at the granularity that we need while everything is changing is really hard.” If you were looking closely at the video, you may have spotted some fiducials (optical patterns placed in the environment that vision systems find easy to spot), but we're told that the video was shot in Corvus Robotics' development warehouse where those markers are used for ground truth testing.
In real deployments, fiducials (or anything else) isn't necessary. The drone has its charging dock, and the initial map, but otherwise it's doing onboard visual-inertial SLAM (simultaneous localization and mapping), dense volumetric mapping, and motion planning with its 10 camera array and an autonomy stack running on ROS and PX4 for real time flight control. Corvus isn't willing to let us in on all of their secrets, but they did tell us that they incorporate some of the structured components of the environment into their SLAM solution, as well as some things are semi-static—that is, things that are unlikely to change over the duration of a single flight, helping the drone with loop closure.
One of the big parts of being able to do this is the ability to localize in very large, unstructured environments where things are constantly changing without having to rely on external infrastructure. For example, a WiFi connection back to our base station is not guaranteed, so everything needs to run on-board the drone, which is a non-trivial task. It's essentially all of the compute of a self-driving car, compressed into the drone. -Mohammed KabirCorvus is able to scan between 200 and 400 pallet positions per hour per drone, inclusive of recharge time. At ground level, this is probably about equivalent in speed to a human (although more sustainable). But as you start looking at inventory higher off the ground, the drone maintains a constant scan rate, while for a human, it gets exponentially harder, involving things like strapping yourself to a forklift. And of course the majority of the items in a high warehouse are not at ground level, because ground level only covers a tier or two of a space that may soar to 20 meters. Overall, Corvus says that they can do inventory up to 10x faster than a human.
With a few exceptions, it's unlikely that most warehouses are going to be able to go human-free in the foreseeable future, meaning that any time you talk about robot autonomy, you also have to talk about safety. “We can operate when no one's around, so our customers often schedule the drones during the third shift when the warehouse is dark,” says Mohammed Kabir. “There are also customers who want us to operate around people, which initially terrified us, because interacting with humans can be quite tricky. But over the last couple years, we've built safety systems to be able to deal with that.” In addition to the collision avoidance that comes with the 360 degree vision system that the drone uses to navigate, it has a variety of safety-first behaviors all the way up to searching for clear flat spots to land in the event of an emergency. But it sounds like the primary way that Corvus tries to maintain safety is by keeping drones and humans as separate as possible, which may involve process changes for the warehouse, explains Corvus Robotics CEO Jackie Wu. “If you see a drone in an aisle, just don't go in until it's done.”
We also asked Wu about what exactly he means when he calls the Corvus Robotics' drone “fully autonomous,” because depending on who you ask (and what kind of robot and task you're talking about), full autonomy can mean a lot of different things.
For us, full autonomy means continuous end to end operation with no human in the loop within a certain scenario or environment. Obviously, it's not level five autonomy, because nobody is doing level five, which would take some kind of generalized intelligence that can fly anywhere. But, for level four, for the warehouse interior, the drones fly on scheduled missions, intelligently find objects of interest while avoiding collisions, come back to land, recharge and share that data, all without anybody touching them. And we're able to do this repeatedly, without external localization infrastructure. -Jackie WuAs tempting as it is, we're not going to get into the weeds here about what exactly constitutes “full autonomy” in the context of drones. Well, okay, maybe we'll get into the weeds a little bit, just to say that being able to repeatedly do a useful task end-to-end without a human in the loop seems close enough to whatever your definition of full autonomy is that it's probably a fair term to apply here. Are there other drones that are arguably more autonomous, in the sense that they require even less structure in the environment? Sure. Are those same drones arguably less autonomous because they don't autonomously recharge? Probably. Corvus Robotics' perspective that the ability to run a drone autonomously for weeks at a time is a more important component of autonomy is perfectly valid considering their use case, but I think we're at the point where “full autonomy” at this level is becoming domain-specific enough to make direct comparisons difficult and maybe not all that useful.
Corvus has just recently come out of stealth, and they're currently working on pilot projects with a handful of Global 2000 companies. Continue reading

Posted in Human Robots

#439247 Drones and Sensors Could Spot Fires ...

The speed at which a wildfire can rip through an area and wreak havoc is nothing short of awe-inspiring and terrifying. Early detection of these events is critical for fire management efforts, whether that involves calling in firefighters or evacuating nearby communities.

Currently, early fire detection in remote areas is typically done by satellite—but this approach can be hindered by cloud cover. What’s more, even the most advanced satellite systems detect fires once the burning area reaches an average seize of 18.4 km2 (7.1 square miles).

To detect wildfires earlier on, some researchers are proposing a novel solution that harnesses a network of Internet of Things (IoT) sensors and a fleet of drones, or unmanned aerial vehicles (UAVs). The researchers tested their approach through simulations, described in a study published May 5 in IEEE Internet of Things Journal, finding that it can detect fires that are just 2.5 km2 (just under one square mile) in size with near perfect accuracy.

Their idea is timely, as climate change is driving an increase in wildfires around many regions of the world, as seen recently in California and Australia.

“In the last few years, the number, frequency, and severity of wildfires have increased dramatically worldwide, significantly impacting countries’ economies, ecosystems, and communities. Wildfire management presents a significant challenge in which early fire detection is key,” emphasizes Osama Bushnaq, a senior researcher at the Autonomous Robotics Research Center of the Technology Innovation Institute in Abu Dhabi, who was involved in the study.

The approach that Bushnaq and his colleagues are proposing involves a network of IoT sensors scattered throughout regions of concern, such as a national park or forests situated near communities. If a fire ignites, IoT devices deployed in the area will detect it and wait until a patrolling UAV is within transmission range to report their measurements. If a UAV receives multiple positive detections by the IoT devices, it will notify the nearby firefighting department that a wildfire has been verified.

The researchers evaluated a number of different UAVs and IoT sensors based on cost and features to determine the optimal combinations. Next, they tested their UAV-IoT approach through simulations, whereby 420 IoT sensors were deployed and 18 UAVs patrolled per square kilometer of simulated forest. The system could detect fires covering 2.5 km2 with greater than 99 percent accuracy. For smaller fires covering 0.5 km2 the approach yielded 69 percent accuracy.

These results suggest that, if an optimal number of UAVs and IoT devices are present, wildfires can be detected in much shorter time than with the satellite imaging. But Bushnaq acknowledges that this approach has its limitations. “UAV-IoT networks can only cover relatively smaller areas,” he explains. “Therefore, the UAV-IoT network would be particularly suitable for wildfire detection at high-risk regions.”

For these reasons, the researchers are proposing that UAV-IoT approach be used alongside satellite imaging, which can cover vast areas but with less wildfire detection speed and reliability.

Moving forward, the team plans to explore ways of further improving upon this approach, for example by optimizing the trajectory of the UAVs or addressing issues related to the battery life of UAVs.

Bushnaq envisions such UAV-IoT systems having much broader applications, too. “Although the system is designed for wildfire detection, it can be used for monitoring different forest parameters, such as wind speed, moisture content, or temperature estimation,” he says, noting that such a system could also be extended beyond the forest setting, for example by monitoring oil spills in bodies of water. Continue reading

Posted in Human Robots

#439066 Video Friday: Festo’s BionicSwift

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!):

RoboSoft 2021 – April 12-16, 2021 – [Online Conference]
ICRA 2021 – May 30-5, 2021 – Xi'an, China
DARPA SubT Finals – September 21-23, 2021 – Louisville, KY, USA
WeRobot 2021 – September 23-25, 2021 – Coral Gables, FL, USA
Let us know if you have suggestions for next week, and enjoy today's videos.

Festo's Bionic Learning Network for 2021 presents a flock of BionicSwifts.

To execute the flight maneuvers as true to life as possible, the wings are modeled on the plumage of birds. The individual lamellae are made of an ultralight, flexible but very robust foam and lie on top of each other like shingles. Connected to a carbon quill, they are attached to the actual hand and arm wings as in the natural model.

During the wing upstroke, the individual lamellae fan out so that air can flow through the wing. This means that the birds need less force to pull the wing up. During the downstroke, the lamellae close up so that the birds can generate more power to fly. Due to this close-to-nature replica of the wings, the BionicSwifts have a better flight profile than previous wing-beating drives.

[ Festo ]

While we've seen a wide variety of COVID-motivated disinfecting robots, they're usually using either ultraviolet light or a chemical fog. This isn't the way that humans clean—we wipe stuff down, which gets rid of surface dirt and disinfects at the same time. Fraunhofer has been working on a mobile manipulator that can clean in the same ways that we do.

It's quite the technical challenge, but it has the potential to be both more efficient and more effective.

[ Fraunhofer ]

In recent years, robots have gained artificial vision, touch, and even smell. “Researchers have been giving robots human-like perception,” says MIT Associate Professor Fadel Adib. In a new paper, Adib’s team is pushing the technology a step further. “We’re trying to give robots superhuman perception,” he says. The researchers have developed a robot that uses radio waves, which can pass through walls, to sense occluded objects. The robot, called RF-Grasp, combines this powerful sensing with more traditional computer vision to locate and grasp items that might otherwise be blocked from view.

[ MIT ]

Ingenuity is now scheduled to fly on April 11.

[ JPL ]

The legendary Zenta is back after a two year YouTube hiatus with “a kind of freaky furry hexapod bunny creature.”

[ Zenta ]

It is with great pride and excitement that the South Australia Police announce a new expansion to their kennel by introducing three new Police Dog (PD) recruits. These dogs have been purposely targeted to bring a whole new range of dog operational capabilities known as the ‘small area urban search and guided evacuation’ dogs. Police have been working closely with specialist vets and dog trainers to ascertain if the lightweight dogs could be transported safely by drones and released into hard-to-access areas where at the moment the larger PDs just simply cannot get in due to their size.

[ SA Police ]

SoftBank may not have Spot cheerleading robots for their baseball team anymore, but they've more than made up for it with a full century of Peppers. And one dude doing the robot.

[ SoftBank ]

MAB Robotics is a Polish company developing walking robots for inspection, and here's a prototype they've been working on.

[ MAB Robotics ]

Thanks Jakub!

DoraNose: Smell your way to a better tomorrow.

[ Dorabot ]

Our robots need to learn how to cope with their new neighbors, and we have just the solution for this, the egg detector! Using cutting-edge AI, it provides incredible precision in detecting a vast variety of eggs. We have deployed this new feature on Boston Dynamics Spot, one of our fleet's robots. It can now detect eggs with its cameras and avoid them on his autonomous missions.

[ Energy Robotics ]

When dropping a squishy robot from an airplane 1,000 feet up, make sure that you land as close to people's cars as you can.

Now do it from orbit!

[ Squishy Robotics ]

An autonomous robot that is able to physically guide humans through narrow and cluttered spaces could be a big boon to the visually-impaired. Most prior robotic guiding systems are based on wheeled platforms with large bases with actuated rigid guiding canes. The large bases and the actuated arms limit these prior approaches from operating in narrow and cluttered environments. We propose a method that introduces a quadrupedal robot with a leash to enable the robot-guiding-human system to change its intrinsic dimension (by letting the leash go slack) in order to fit into narrow spaces.

[ Hybrid Robotics ]

How to prove that your drone is waterproof.

[ UNL ]

Well this ought to be pretty good once it gets out of simulation.

[ Hybrid Robotics ]

MIDAS is Aurora’s AI-enabled, multi-rotor sUAV outfitted with optical sensors and a customized payload that can defeat multiple small UAVs per flight with low-collateral effects.

[ Aurora ]

The robots​ of the DFKI have the advantage of being able to reach extreme environments: they can be used for decontamination purposes in high-risk areas or inspect and maintain underwater​ structures, for which they are tested in the North Sea near Heligoland​.

[ DFKI ]

After years of trying, 60 Minutes cameras finally get a peek inside the workshop at Boston Dynamics, where robots move in ways once only thought possible in movies. Anderson Cooper reports.

[ 60 Minutes ]

In 2007, Noel Sharky stated that “we are sleepwalking into a brave new world where robots decide who, where and when to kill.” Since then thousands of AI and robotics researchers have joined his calls to regulate “killer robots.” But sometime this year, Turkey will deploy fully autonomous home-built kamikaze drones on its border with Syria. What are the ethical choices we need to consider? Will we end up in an episode of Black Mirror? Or is the UN listening to calls and starting the process of regulating this space? Prof. Toby Walsh will discuss this important issue, consider where we are at and where we need to go.

[ ICRA 2020 ]

In the second session of HAI's spring conference, artists and technologists discussed how technology can enhance creativity, reimagine meaning, and support racial and social justice. The conference, called “Intelligence Augmentation: AI Empowering People to Solve Global Challenges,” took place on 25 March 2021.

[ Stanford HAI ]

This spring 2021 GRASP SFI comes from Monroe Kennedy III at Stanford University, on “Considerations for Human-Robot Collaboration.”

The field of robotics has evolved over the past few decades. We’ve seen robots progress from the automation of repetitive tasks in manufacturing to the autonomy of mobilizing in unstructured environments to the cooperation of swarm robots that are centralized or decentralized. These abilities have required advances in robotic hardware, modeling, and artificial intelligence. The next frontier is robots collaborating in complex tasks with human teammates, in environments traditionally configured for humans. While solutions to this challenge must utilize all the advances of robotics, the human element adds a unique aspect that must be addressed. Collaborating with a human teammate means that the robot must have a contextual understanding of the task as well as all participant’s roles. We will discuss what constitutes an effective teammate and how we can capture this behavior in a robotic collaborator.

[ UPenn ] Continue reading

Posted in Human Robots

#439036 Video Friday: Shadow Plays Jenga, and ...

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here’s what we have so far (send us your events!):

RoboSoft 2021 – April 12-16, 2021 – [Online Conference]
ICRA 2021 – May 30-5, 2021 – Xi'an, China
DARPA SubT Finals – September 21-23, 2021 – Louisville, KY, USA
WeRobot 2021 – September 23-25, 2021 – Coral Gables, FL, USA
Let us know if you have suggestions for next week, and enjoy today’s videos.

The Shadow Robot team couldn't resist! Our Operator, Joanna, is using the Shadow Teleoperation System which, fun and games aside, can help those in difficult, dangerous and distant jobs.

Shadow could challenge this MIT Jenga-playing robot, but I bet they wouldn't win:

[ Shadow Robot ]

Digit is gradually stomping the Agility Robotics logo into a big grassy field fully autonomously.

[ Agility Robotics ]

This is a pretty great and very short robotic magic show.

[ Mario the Magician ]

A research team at the Georgia Institute of Technology has developed a modular solution for drone delivery of larger packages without the need for a complex fleet of drones of varying sizes. By allowing teams of small drones to collaboratively lift objects using an adaptive control algorithm, the strategy could allow a wide range of packages to be delivered using a combination of several standard-sized vehicles.

[ GA Tech ]

I've seen this done using vision before, but Flexiv's Rizon 4s can keep a ball moving along a specific trajectory using only force sensing and control.

[ Flexiv ]

Thanks Yunfan!

This combination of a 3D aerial projection system and a sensing interface can be used as an interactive and intuitive control system for things like robot arms, but in this case, it's being used to make simulated pottery. Much less messy than the traditional way of doing it.

More details on Takafumi Matsumaru's work at the Bio-Robotics & Human-Mechatronics Laboratory at Waseda University at the link below.

[ BLHM ]

U.S. Vice President Kamala Harris called astronauts Shannon Walker and Kate Rubins on the ISS, and they brought up Astrobee, at which point Shannon reaches over and rips Honey right off of her charging dock to get her on camera.

[ NASA ]

Here's a quick three minute update on Perseverance and Ingenuity from JPL.

[ Mars 2020 ]

Rigid grippers used in existing aerial manipulators require precise positioning to achieve successful grasps and transmit large contact forces that may destabilize the drone. This limits the speed during grasping and prevents “dynamic grasping,” where the drone attempts to grasp an object while moving. On the other hand, biological systems (e.g. birds) rely on compliant and soft parts to dampen contact forces and compensate for grasping inaccuracy, enabling impressive feats. This paper presents the first prototype of a soft drone—a quadrotor where traditional (i.e. rigid) landing gears are replaced with a soft tendon-actuated gripper to enable aggressive grasping.

[ MIT ]

In this video we present results from a field deployment inside the Løkken Mine underground pyrite mine in Norway. The Løkken mine was operative from 1654 to 1987 and contains narrow but long corridors, alongside vast rooms and challenging vertical stopes. In this field study we evaluated selected autonomous exploration and visual search capabilities of a subset of the aerial robots of Team CERBERUS towards the goal of complete subterranean autonomy.

[ Team CERBERUS ]

What you can do with a 1,000 FPS projector with a high speed tracking system.

[ Ishikawa Group ]

ANYbotics’ collaboration with BASF, one of the largest global chemical manufacturers, displays the efficiency, quality, and scalability of robotic inspection and data-collection capabilities in complex industrial environments.

[ ANYbotics ]

Does your robot arm need a stylish jacket?

[ Fraunhofer ]

Trossen Robotics unboxes a Unitree A1, and it's actually an unboxing where they have to figure out everything from scratch.

[ Trossen ]

Robots have learned to drive cars, assist in surgeries―and vacuum our floors. But can they navigate the unwritten rules of a busy sidewalk? Until they can, robotics experts Leila Takayama and Chris Nicholson believe, robots won’t be able to fulfill their immense potential. In this conversation, Chris and Leila explore the future of robotics and the role open source will play in it.

[ Red Hat ]

Christoph Bartneck's keynote at the 6th Joint UAE Symposium on Social Robotics, focusing on what roles robots can play during the Covid crisis and why so many social robots fail in the market.

[ HIT Lab ]

Decision-making based on arbitrary criteria is legal in some contexts, such as employment, and not in others, such as criminal sentencing. As algorithms replace human deciders, HAI-EIS fellow Kathleen Creel argues arbitrariness at scale is morally and legally problematic. In this HAI seminar, she explains how the heart of this moral issue relates to domination and a lack of sufficient opportunity for autonomy. It relates in interesting ways to the moral wrong of discrimination. She proposes technically informed solutions that can lessen the impact of algorithms at scale and so mitigate or avoid the moral harm identified.

[ Stanford HAI ]

Sawyer B. Fuller speaks on Autonomous Insect-Sized Robots at the UC Berkeley EECS Colloquium series.

Sub-gram (insect-sized) robots have enormous potential that is largely untapped. From a research perspective, their extreme size, weight, and power (SWaP) constraints also forces us to reimagine everything from how they compute their control laws to how they are fabricated. These questions are the focus of the Autonomous Insect Robotics Laboratory at the University of Washington. I will discuss potential applications for insect robots and recent advances from our group. These include the first wireless flights of a sub-gram flapping-wing robot that weighs barely more than a toothpick. I will describe efforts to expand its capabilities, including the first multimodal ground-flight locomotion, the first demonstration of steering control, and how to find chemical plume sources by integrating the smelling apparatus of a live moth. I will also describe a backpack for live beetles with a steerable camera and conceptual design of robots that could scale all the way down to the “gnat robots” first envisioned by Flynn & Brooks in the ‘80s.

[ UC Berkeley ]

Thanks Fan!

Joshua Vander Hook, Computer Scientist, NIAC Fellow, and Technical Group Supervisor at NASA JPL, presents an overview of the AI Group(s) at JPL, and recent work on single and multi-agent autonomous systems supporting space exploration, Earth science, NASA technology development, and national defense programs.

[ UMD ] Continue reading

Posted in Human Robots