Tag Archives: system

#439051 ‘Neutrobots’ smuggle drugs ...

A team of researchers from the Harbin Institute of Technology along with partners at the First Affiliated Hospital of Harbin Medical University, both in China, has developed a tiny robot that can ferry cancer drugs through the blood-brain barrier (BBB) without setting off an immune reaction. In their paper published in the journal Science Robotics, the group describes their robot and tests with mice. Junsun Hwang and Hongsoo Choi, with the Daegu Gyeongbuk Institute of Science and Technology in Korea, have published a Focus piece in the same journal issue on the work done by the team in China. 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

#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

#439006 Low-Cost Drones Learn Precise Control ...

I’ll admit to having been somewhat skeptical about the strategy of dangling payloads on long tethers for drone delivery. I mean, I get why Wing does it— it keeps the drone and all of its spinny bits well away from untrained users while preserving the capability of making deliveries to very specific areas that may have nearby obstacles. But it also seems like you’re adding some risk as well, because once your payload is out on that long tether, it’s more or less out of your control in at least two axes. And you can forget about your drone doing anything while this is going on, because who the heck knows what’s going to happen to your payload if the drone starts moving around?

NYU roboticists, that’s who.

This research is by Guanrui Li, Alex Tunchez, and Giuseppe Loianno at the Agile Robotics and Perception Lab (ARPL) at NYU. As you can see from the video, the drone makes keeping rock-solid control over that suspended payload look easy, but it’s very much not, especially considering that everything you see is running onboard the drone itself at 500Hz— all it takes is an IMU and a downward-facing monocular camera, along with the drone’s Snapdragon processor.

To get this to work, the drone has to be thinking about two things. First, there’s state estimation, which is the behavior of the drone itself along with its payload at the end of the tether. The drone figures this out by watching how the payload moves using its camera and tracking its own movement with its IMU. Second, there’s predicting what the payload is going to do next, and how that jibes (or not) with what the drone wants to do next. The researchers developed a model predictive control (MPC) system for this, with some added perception constraints to make sure that the behavior of the drone keeps the payload in view of the camera.

At the moment, the top speed of the system is 4 m/s, but it sounds like rather than increasing the speed of a single payload-swinging drone, the next steps will be to make the overall system more complicated by somehow using multiple drones to cooperatively manage tethered payloads that are too big or heavy for one drone to handle alone.

For more on this, we spoke with Giuseppe Loianno, head of the ARPL.

IEEE Spectrum: We've seen some examples of delivery drones delivering suspended loads. How will this work improve their capabilities?

Giuseppe Loianno: For the first time, we jointly design a perception-constrained model predictive control and state estimation approaches to enable the autonomy of a quadrotor with a cable suspended payload using onboard sensing and computation. The proposed control method guarantees the visibility of the payload in the robot camera as well as the respect of the system dynamics and actuator constraints. These are critical design aspects to guarantee safety and resilience for such a complex and delicate task involving transportation of objects.

The additional challenge involves the fact that we aim to solve the aforementioned problem using a minimal sensor suite for autonomous navigation made by a single camera and IMU. This is an ambitious goal since it concurrently involves estimating the load and the vehicle states. Previous approaches leverage GPS or motion capture systems for state estimation and do not consider the perception and physical constraints when solving the problem. We are confident that our solution will contribute to making a reality the autonomous delivery process in warehouses or in dense urban areas where the GPS signal is currently absent or shadowed.

Will it make a difference to delivery systems that use an actuated cable and only leave the load suspended for the delivery itself?

This is certainly an interesting question. We believe that adding an actuated cable will introduce more disadvantages than benefits. Certainly, an actuated cable can be leveraged to compensate for cable's swinging motions in windy conditions and/or increase the delivery precision. However, the introduction of additional actuated mechanisms and components come at the price of an increased system mass and inertia. This will reduce the overall flight time and the vehicle’s agility as well as the system resilience with respect to the transportation task. Finally, active mechanisms are also more difficult to design compared to passive ones.

What's challenging about doing all of this on-vehicle?

There are several challenges to solve on-board this problem. First, it is very difficult to concurrently run perception and action on such computationally constrained platforms in real-time. Second, the first aspect becomes even more challenging if we consider as in our case a perception-based constrained receding horizon control problem that aims to guarantee the visibility of the payload during the motion, while concurrently respecting all the system physical and sensing limitations. Finally, it has been challenging to run the entire system at a high rate to fully unleash the system’s agility. We are currently able to reach rates of 500 Hz.

Can your method adapt to loads of varying shapes, sizes, and masses? What about aerodynamics or flying in wind?

Technically, our approach can easily be adapted to varying objects sizes and masses. Our previous contributions have already shown the ability to estimate online changes in the vehicle/load configuration and can potentially be used to operate the proposed system in dynamic conditions, where the load’s characteristics are unknown and/or may vary across consecutive flights. This can be useful for both package delivery or warehouse operations, where different types of objects need to be transported or manipulated.

The aerodynamics problem is a great point. Overall, our past work has investigated the aerodynamics of wind disturbances for a single robot without a load. Formulating these problems for the proposed system is challenging and is still an open research question. We have some ideas to approach this problem combining Bayesian estimation techniques with more recent machine learning approaches and we will tackle it in the near future.

What are the limitations on the performance of the system? How fast and agile can it be with a suspended payload?

The limits of the performances are established by the actuating and sensing system. Our approach intrinsically considers both physical and sensing limitations of our system. From a sensing and computation perspective, we believe to be close to the limits with speeds of up to 4 m/s. Faster speeds can potentially introduce motion blur while decreasing the load tracking precision. Moreover, faster motions will increase as well aerodynamic disturbances that we have just mentioned. In the future, modeling these phenomena and their incorporation in the proposed solution can further push the agility.

Your paper talks about extending this approach to multiple vehicles cooperatively transporting a payload, can you tell us more about that?

We are currently working on a distributed perception and control approach for cooperative transportation. We already have some very exciting results that we will share with you very soon! Overall, we can employ a team of aerial robots to cooperatively transport a payload to increase the payload capacity and endow the system with additional resilience in case of vehicles’ failures. A cooperative cable suspended payload cooperative transportation system allows as well to concurrently and independently control the load’s position and orientation. This is not possible just using rigid connections. We believe that our approach will have a strong impact in real-world settings for delivery and constructions in warehouses and GPS-denied environments such as dense urban areas. Moreover, in post disaster scenarios, a team of physically interconnected aerial robots can deliver supplies and establish communication in areas where GPS signal is intermittent or unavailable.

PCMPC: Perception-Constrained Model Predictive Control for Quadrotors with Suspended Loads using a Single Camera and IMU, by Guanrui Li, Alex Tunchez, and Giuseppe Loianno from NYU, will be presented (virtually) at ICRA 2021.

<Back to IEEE Journal Watch Continue reading

Posted in Human Robots