Tag Archives: Aerospace

#439032 To Learn To Deal With Uncertainty, This ...

AI is endowing robots, autonomous vehicles and countless of other forms of tech with new abilities and levels of self-sufficiency. Yet these models faithfully “make decisions” based on whatever data is fed into them, which could have dangerous consequences. For instance, if an autonomous car is driving down a highway and the sensor picks up a confusing signal (e.g., a paint smudge that is incorrectly interpreted as a lane marking), this could cause the car to swerve into another lane unnecessarily.

But in the ever-evolving world of AI, researchers are developing new ways to address challenges like this. One group of researchers has devised a new algorithm that allows the AI model to account for uncertain data, which they describe in a study published February 15 in IEEE Transactions on Neural Networks and Learning Systems.

“While we would like robots to work seamlessly in the real world, the real world is full of uncertainty,” says Michael Everett, a post-doctoral associate at MIT who helped develop the new approach. “It's important for a system to be aware of what it knows and what it is unsure about, which has been a major challenge for modern AI.”

His team focused on a type of AI called reinforcement learning (RL), whereby the model tries to learn the “value” of taking each action in a given scenario through trial-and-error. They developed a secondary algorithm, called Certified Adversarial Robustness for deep RL (CARRL), that can be built on top of an existing RL model.

“Our key innovation is that rather than blindly trusting the measurements, as is done today [by AI models], our algorithm CARRL thinks through all possible measurements that could have been made, and makes a decision that considers the worst-case outcome,” explains Everett.

In their study, the researchers tested CARRL across several different tasks, including collision avoidance simulations and Atari pong. For younger readers who may not be familiar with it, Atari pong is a classic computer game whereby an electronic paddle is used to direct a ping pong on the screen. In the test scenario, CARRL helped move the paddle slightly higher or lower to compensate for the possibility that the ball could approach at a slightly different point than what the input data indicated. All the while, CARRL would try to ensure that the ball would make contact with at least some part of paddle.

Gif: MIT Aerospace Controls Laboratory

In a perfect world, the information that an AI model is fed would be accurate all the time and AI model will perform well (left). But in some cases, the AI may be given inaccurate data, causing it to miss its targets (middle). The new algorithm CARRL helps AIs account for uncertainty in its data inputs, yielding a better performance when relying on poor data (right).

Across all test scenarios, the RL model was better at compensating for potential inaccurate or “noisy” data with CARRL, than without CARRL.

But the results also show that, like with humans, too much self-doubt and uncertainty can be unhelpful. In the collision avoidance scenario, for example, indulging in too much uncertainty caused the main moving object in the simulation to avoid both the obstacle and its goal. “There is definitely a limit to how ‘skeptical’ the algorithm can be without becoming overly conservative,” Everett says.

This research was funded by Ford Motor Company, but Everett notes that it could be applicable under many other commercial applications requiring safety-aware AI, including aerospace, healthcare, or manufacturing domains.

“This work is a step toward my vision of creating ‘certifiable learning machines’—systems that can discover how to explore and perform in the real world on their own, while still having safety and robustness guarantees,” says Everett. “We'd like to bring CARRL into robotic hardware while continuing to explore the theoretical challenges at the interface of robotics and AI.” Continue reading

Posted in Human Robots

#439010 Video Friday: Nanotube-Powered Insect ...

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!):

HRI 2021 – March 8-11, 2021 – [Online Conference]
RoboSoft 2021 – April 12-16, 2021 – [Online Conference]
ICRA 2021 – May 30-5, 2021 – Xi'an, China
Let us know if you have suggestions for next week, and enjoy today's videos.

If you’ve ever swatted a mosquito away from your face, only to have it return again (and again and again), you know that insects can be remarkably acrobatic and resilient in flight. Those traits help them navigate the aerial world, with all of its wind gusts, obstacles, and general uncertainty. Such traits are also hard to build into flying robots, but MIT Assistant Professor Kevin Yufeng Chen has built a system that approaches insects’ agility.

Chen’s actuators can flap nearly 500 times per second, giving the drone insect-like resilience. “You can hit it when it’s flying, and it can recover,” says Chen. “It can also do aggressive maneuvers like somersaults in the air.” And it weighs in at just 0.6 grams, approximately the mass of a large bumble bee. The drone looks a bit like a tiny cassette tape with wings, though Chen is working on a new prototype shaped like a dragonfly.

[ MIT ]

National Robotics Week is April 3-11, 2021!

[ NRW ]

This is in a motion capture environment, but still, super impressive!

[ Paper ]

Thanks Fan!

Why wait for Boston Dynamics to add an arm to your Spot if you can just do it yourself?

[ ETHZ ]

This video shows the deep-sea free swimming of soft robot in the South China Sea. The soft robot was grasped by a robotic arm on ‘HAIMA’ ROV and reached the bottom of the South China Sea (depth of 3,224 m). After the releasing, the soft robot was actuated with an on-board AC voltage of 8 kV at 1 Hz and demonstrated free swimming locomotion with its flapping fins.

Um, did they bring it back?

[ Nature ]

Quadruped Yuki Mini is 12 DOF robot equipped with a Raspberry Pi that runs ROS. Also, BUNNIES!

[ Lingkang Zhang ]

Thanks Lingkang!

Deployment of drone swarms usually relies on inter-agent communication or visual markers that are mounted on the vehicles to simplify their mutual detection. The vswarm package enables decentralized vision-based control of drone swarms without relying on inter-agent communication or visual fiducial markers. The results show that the drones can safely navigate in an outdoor environment despite substantial background clutter and difficult lighting conditions.

[ Vswarm ]

A conventional adopted method for operating a waiter robot is based on the static position control, where pre-defined goal positions are marked on a map. However, this solution is not optimal in a dynamic setting, such as in a coffee shop or an outdoor catering event, because the customers often change their positions. We explore an alternative human-robot interface design where a human operator communicates the identity of the customer to the robot instead. Inspired by how [a] human communicates, we propose a framework for communicating a visual goal to the robot, through interactive two-way communications.

[ Paper ]

Thanks Poramate!

In this video, LOLA reacts to undetected ground height changes, including a drop and leg-in-hole experiment. Further tests show the robustness to vertical disturbances using a seesaw. The robot is technically blind, not using any camera-based or prior information on the terrain.

[ TUM ]

RaiSim is a cross-platform multi-body physics engine for robotics and AI. It fully supports Linux, Mac OS, and Windows.

[ RaiSim ]

Thanks Fan!

The next generation of LoCoBot is here. The LoCoBot is an ROS research rover for mapping, navigation and manipulation (optional) that enables researchers, educators and students alike to focus on high level code development instead of hardware and building out lower level code. Development on the LoCoBot is simplified with open source software, full ROS-mapping and navigation packages and modular opensource Python API that allows users to move the platform as well as (optional) manipulator in as few as 10 lines of code.

[ Trossen ]

MIT Media Lab Research Specialist Dr. Kate Darling looks at how robots are portrayed in popular film and TV shows.

Kate's book, The New Breed: What Our History with Animals Reveals about Our Future with Robots can be pre-ordered now and comes out next month.

[ Kate Darling ]

The current autonomous mobility systems for planetary exploration are wheeled rovers, limited to flat, gently-sloping terrains and agglomerate regolith. These vehicles cannot tolerate instability and operate within a low-risk envelope (i.e., low-incline driving to avoid toppling). Here, we present ‘Mars Dogs’ (MD), four-legged robotic dogs, the next evolution of extreme planetary exploration.

[ Team CoSTAR ]

In 2020, first-year PhD students at the MIT Media Lab were tasked with a special project—to reimagine the Lab and write sci-fi stories about the MIT Media Lab in the year 2050. “But, we are researchers. We don't only write fiction, we also do science! So, we did what scientists do! We used a secret time machine under the MIT dome to go to the year 2050 and see what’s going on there! Luckily, the Media Lab still exists and we met someone…really cool!” Enjoy this interview of Cyber Joe, AI Mentor for MIT Media Lab Students of 2050.

[ MIT ]

In this talk, we will give an overview of the diverse research we do at CSIRO’s Robotics and Autonomous Systems Group and delve into some specific technologies we have developed including SLAM and Legged robotics. We will also give insights into CSIRO’s participation in the current DARPA Subterranean Challenge where we are deploying a fleet of heterogeneous robots into GPS-denied unknown underground environments.

[ GRASP Seminar ]

Marco Hutter (ETH) and Hae-Won Park (KAIST) talk about “Robotics Inspired by Nature.”

[ Swiss-Korean Science Club ]

Thanks Fan!

In this keynote, Guy Hoffman Assistant Professor and the Mills Family Faculty Fellow in the Sibley School of Mechanical and Aerospace Engineering at Cornell University, discusses “The Social Uncanny of Robotic Companions.”

[ Designerly HRI ] Continue reading

Posted in Human Robots

#438807 Visible Touch: How Cameras Can Help ...

The dawn of the robot revolution is already here, and it is not the dystopian nightmare we imagined. Instead, it comes in the form of social robots: Autonomous robots in homes and schools, offices and public spaces, able to interact with humans and other robots in a socially acceptable, human-perceptible way to resolve tasks related to core human needs.

To design social robots that “understand” humans, robotics scientists are delving into the psychology of human communication. Researchers from Cornell University posit that embedding the sense of touch in social robots could teach them to detect physical interactions and gestures. They describe a way of doing so by relying not on touch but on vision.

A USB camera inside the robot captures shadows of hand gestures on the robot’s surface and classifies them with machine-learning software. They call this method ShadowSense, which they define as a modality between vision and touch, bringing “the high resolution and low cost of vision-sensing to the close-up sensory experience of touch.”

Touch-sensing in social or interactive robots is usually achieved with force sensors or capacitive sensors, says study co-author Guy Hoffman of the Sibley School of Mechanical and Aerospace Engineering at Cornell University. The drawback to his group’s approach has been that, even to achieve coarse spatial resolution, many sensors are needed in a small area.

However, working with non-rigid, inflatable robots, Hoffman and his co-researchers installed a consumer-grade USB camera to which they attached a fisheye lens for a wider field of vision.

“Given that the robot is already hollow, and has a soft and translucent skin, we could do touch interaction by looking at the shadows created by people touching the robot,” says Hoffman. They used deep neural networks to interpret the shadows. “And we were able to do it with very high accuracy,” he says. The robot was able to interpret six different gestures, including one- or two-handed touch, pointing, hugging and punching, with an accuracy of 87.5 to 96 percent, depending on the lighting.

This is not the first time that computer vision has been used for tactile sensing, though the scale and application of ShadowSense is unique. “Photography has been used for touch mainly in robotic grasping,” says Hoffman. By contrast, Hoffman and collaborators wanted to develop a sense that could be “felt” across the whole of the device.

The potential applications for ShadowSense include mobile robot guidance using touch, and interactive screens on soft robots. A third concerns privacy, especially in home-based social robots. “We have another paper currently under review that looks specifically at the ability to detect gestures that are further away [from the robot’s skin],” says Hoffman. This way, users would be able to cover their robot’s camera with a translucent material and still allow it to interpret actions and gestures from shadows. Thus, even though it’s prevented from capturing a high-resolution image of the user or their surrounding environment, using the right kind of training datasets, the robot can continue to monitor some kinds of non-tactile activities.

In its current iteration, Hoffman says, ShadowSense doesn’t do well in low-light conditions. Environmental noise, or shadows from surrounding objects, also interfere with image classification. Relying on one camera also means a single point of failure. “I think if this were to become a commercial product, we would probably [have to] work a little bit better on image detection,” says Hoffman.

As it was, the researchers used transfer learning—reusing a pre-trained deep-learning model in a new problem—for image analysis. “One of the problems with multi-layered neural networks is that you need a lot of training data to make accurate predictions,” says Hoffman. “Obviously, we don’t have millions of examples of people touching a hollow, inflatable robot. But we can use pre-trained networks trained on general images, which we have billions of, and we only retrain the last layers of the network using our own dataset.” Continue reading

Posted in Human Robots