Added Value From an Ejector With Integrated Controls in a Suction Cup Handling System
Important functions provided on an ejector, and in particular all-in-one ejector, can be summarized in three major categories.
This Robot Has Soft Hands. It Could Be the Future of Sustainable Production
Traditional robots have been designed to perform a single task extremely efficiently. But when the item or the task changes, which is often the case with food, these traditional machines need to be reintegrated and reprogrammed
End of the line for ASIMO, Japan’s famed robot?
It has played football with former US president Barack Obama and danced for German leader Angela Merkel, but Honda's ASIMO robot may have reached the end of the line.
Are You Still Manually Programming Your Robot? Why Not Fully Automate!
Focus Auto Design Inc. originally purchased a used robot two years ago to make the jump into automating their business. However, they were manually programming this robot which took time and resources that held their business back from its optimal performance.
How Can Robotic Automation Help Alleviate Labor Shortages?
In the past evaluating automation was typically about the Return On Investment (ROI), but more and more companies are considering automation because they are not able to staff their current manual operations.
The Top Ten Autonomous Delivery Solutions of 2018
The high-costs and inefficiency of last-mile delivery make it prime real estate for automation, with the autonomous delivery market looking poised for exponential growth in the coming years.
Machine learning will redesign, not replace, work
The conversation around artificial intelligence and automation seems dominated by either doomsayers who fear robots will supplant all humans in the workforce, or optimists who think there's nothing new under the sun. But MIT Sloan professor Erik Brynjolfsson and his colleagues say that debate needs to take a different tone.
Wave Gliders Collect Live Ocean Data from Hawaii’s Kilauea Volcano Lava Flow
By using this unmanned technology, scientists have the rare opportunity to study the effects of the lava entering the ocean, the plume it creates and the interactions of the lava and seawater directly from the surface of the ocean.
#263: ICRA 2018 Exhibition, with Juxi Leitner, Nicholas Panitz, Ben Wilson and James Brett
In this episode, Audrow Nash interviews Juxi Leitner, a Postdoctoral Research Fellow at QUT; and Nicholas Panitz, Ben Wilson, and James Brett, from CSIRO.
Leitner speaks about the Amazon Picking challenge, a challenge to advance the state of robotic grasping, and their robot which won the challenge in 2017. Their robot is similar to a cartesian 3D printer in form and uses either a suction cup or a pinch gripper for grabbing objects. Their robot has a depth camera and uses a digital scale to determine if an object has been picked up successfully. Leitner discusses what their team did differently from other teams that helped them win the competition.
Panitz, Wilson, and Brett speak about their hexapod robots. Their hexapods are for several purposes, such as environmental monitoring and remote inspection. They choose to use hexapods because they are statically stable. They discuss the design of their hexapods and how research works at Commonwealth Scientific and Industrial Research Organization, or CSIRO.
Links
#263: ICRA 2018 Exhibition, with Juxi Leitner, Nicholas Panitz, Ben Wilson and James Brett
In this episode, Audrow Nash interviews Juxi Leitner, a Postdoctoral Research Fellow at QUT; and Nicholas Panitz, Ben Wilson, and James Brett, from CSIRO.
Leitner speaks about the Amazon Picking challenge, a challenge to advance the state of robotic grasping, and their robot which won the challenge in 2017. Their robot is similar to a cartesian 3D printer in form and uses either a suction cup or a pinch gripper for grabbing objects. Their robot has a depth camera and uses a digital scale to determine if an object has been picked up successfully. Leitner discusses what their team did differently from other teams that helped them win the competition.
Panitz, Wilson, and Brett speak about their hexapod robots. Their hexapods are for several purposes, such as environmental monitoring and remote inspection. They choose to use hexapods because they are statically stable. They discuss the design of their hexapods and how research works at Commonwealth Scientific and Industrial Research Organization, or CSIRO.
A video of the robot Leitner discusses, #Cartman:
An example of CSIRO’s hexapod robots for inspection:
Links
More Than 110 UAS Solutions Providers Have Signed On To Exhibit At Commercial UAV Expo
These exhibitors will be displaying cutting-edge airframes, components, software and services as well as end-to-end solutions focused on commercial applications.
Robots in Depth with Dirk Thomas
In this episode of Robots in Depth, Per Sjöborg speaks with Dirk Thomas about his work with ROS at the OSR Foundation.
We hear about how programmers and roboticists can benefit from being part of and contributing to the open source community.
Dirk discusses the development of ROS and how it is being used both in academia and in commercial projects. He also shares his thoughts on the future development of ROS and how it can support advancements in robotics overall.
This interview was recorded in 2015.
Robots in Depth with Dirk Thomas
In this episode of Robots in Depth, Per Sjöborg speaks with Dirk Thomas about his work with ROS at the OSR Foundation.
We hear about how programmers and roboticists can benefit from being part of and contributing to the open source community.
Dirk discusses the development of ROS and how it is being used both in academia and in commercial projects. He also shares his thoughts on the future development of ROS and how it can support advancements in robotics overall.
This interview was recorded in 2015.
How to control robots with brainwaves and hand gestures
Photo: Joseph DelPreto/MIT CSAIL
Getting robots to do things isn’t easy: Usually, scientists have to either explicitly program them or get them to understand how humans communicate via language.
But what if we could control robots more intuitively, using just hand gestures and brainwaves?
A new system spearheaded by researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) aims to do exactly that, allowing users to instantly correct robot mistakes with nothing more than brain signals and the flick of a finger.
Building off the team’s past work focused on simple binary-choice activities, the new work expands the scope to multiple-choice tasks, opening up new possibilities for how human workers could manage teams of robots.
By monitoring brain activity, the system can detect in real-time if a person notices an error as a robot does a task. Using an interface that measures muscle activity, the person can then make hand gestures to scroll through and select the correct option for the robot to execute.
The team demonstrated the system on a task in which a robot moves a power drill to one of three possible targets on the body of a mock plane. Importantly, they showed that the system works on people it’s never seen before, meaning that organizations could deploy it in real-world settings without needing to train it on users.
“This work combining EEG and EMG feedback enables natural human-robot interactions for a broader set of applications than we’ve been able to do before using only EEG feedback,” says CSAIL Director Daniela Rus, who supervised the work. “By including muscle feedback, we can use gestures to command the robot spatially, with much more nuance and specificity.”
PhD candidate Joseph DelPreto was lead author on a paper about the project alongside Rus, former CSAIL postdoc Andres F. Salazar-Gomez, former CSAIL research scientist Stephanie Gil, research scholar Ramin M. Hasani, and Boston University Professor Frank H. Guenther. The paper will be presented at the Robotics: Science and Systems (RSS) conference taking place in Pittsburgh next week.
In most previous work, systems could generally only recognize brain signals when people trained themselves to “think” in very specific but arbitrary ways and when the system was trained on such signals. For instance, a human operator might have to look at different light displays that correspond to different robot tasks during a training session.
Not surprisingly, such approaches are difficult for people to handle reliably, especially if they work in fields like construction or navigation that already require intense concentration.
Meanwhile, Rus’ team harnessed the power of brain signals called “error-related potentials” (ErrPs), which researchers have found to naturally occur when people notice mistakes. If there’s an ErrP, the system stops so the user can correct it; if not, it carries on.
“What’s great about this approach is that there’s no need to train users to think in a prescribed way,” says DelPreto. “The machine adapts to you, and not the other way around.”
For the project the team used “Baxter,” a humanoid robot from Rethink Robotics. With human supervision, the robot went from choosing the correct target 70 percent of the time to more than 97 percent of the time.
To create the system the team harnessed the power of electroencephalography (EEG) for brain activity and electromyography (EMG) for muscle activity, putting a series of electrodes on the users’ scalp and forearm.
Both metrics have some individual shortcomings: EEG signals are not always reliably detectable, while EMG signals can sometimes be difficult to map to motions that are any more specific than “move left or right.” Merging the two, however, allows for more robust bio-sensing and makes it possible for the system to work on new users without training.
“By looking at both muscle and brain signals, we can start to pick up on a person’s natural gestures along with their snap decisions about whether something is going wrong,” says DelPreto. “This helps make communicating with a robot more like communicating with another person.”
The team says that they could imagine the system one day being useful for the elderly, or workers with language disorders or limited mobility.
“We’d like to move away from a world where people have to adapt to the constraints of machines,” says Rus. “Approaches like this show that it’s very much possible to develop robotic systems that are a more natural and intuitive extension of us.”
How to control robots with brainwaves and hand gestures
Photo: Joseph DelPreto/MIT CSAIL
Getting robots to do things isn’t easy: Usually, scientists have to either explicitly program them or get them to understand how humans communicate via language.
But what if we could control robots more intuitively, using just hand gestures and brainwaves?
A new system spearheaded by researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) aims to do exactly that, allowing users to instantly correct robot mistakes with nothing more than brain signals and the flick of a finger.
Building off the team’s past work focused on simple binary-choice activities, the new work expands the scope to multiple-choice tasks, opening up new possibilities for how human workers could manage teams of robots.
By monitoring brain activity, the system can detect in real-time if a person notices an error as a robot does a task. Using an interface that measures muscle activity, the person can then make hand gestures to scroll through and select the correct option for the robot to execute.
The team demonstrated the system on a task in which a robot moves a power drill to one of three possible targets on the body of a mock plane. Importantly, they showed that the system works on people it’s never seen before, meaning that organizations could deploy it in real-world settings without needing to train it on users.
“This work combining EEG and EMG feedback enables natural human-robot interactions for a broader set of applications than we’ve been able to do before using only EEG feedback,” says CSAIL Director Daniela Rus, who supervised the work. “By including muscle feedback, we can use gestures to command the robot spatially, with much more nuance and specificity.”
PhD candidate Joseph DelPreto was lead author on a paper about the project alongside Rus, former CSAIL postdoc Andres F. Salazar-Gomez, former CSAIL research scientist Stephanie Gil, research scholar Ramin M. Hasani, and Boston University Professor Frank H. Guenther. The paper will be presented at the Robotics: Science and Systems (RSS) conference taking place in Pittsburgh next week.
In most previous work, systems could generally only recognize brain signals when people trained themselves to “think” in very specific but arbitrary ways and when the system was trained on such signals. For instance, a human operator might have to look at different light displays that correspond to different robot tasks during a training session.
Not surprisingly, such approaches are difficult for people to handle reliably, especially if they work in fields like construction or navigation that already require intense concentration.
Meanwhile, Rus’ team harnessed the power of brain signals called “error-related potentials” (ErrPs), which researchers have found to naturally occur when people notice mistakes. If there’s an ErrP, the system stops so the user can correct it; if not, it carries on.
“What’s great about this approach is that there’s no need to train users to think in a prescribed way,” says DelPreto. “The machine adapts to you, and not the other way around.”
For the project the team used “Baxter,” a humanoid robot from Rethink Robotics. With human supervision, the robot went from choosing the correct target 70 percent of the time to more than 97 percent of the time.
To create the system the team harnessed the power of electroencephalography (EEG) for brain activity and electromyography (EMG) for muscle activity, putting a series of electrodes on the users’ scalp and forearm.
Both metrics have some individual shortcomings: EEG signals are not always reliably detectable, while EMG signals can sometimes be difficult to map to motions that are any more specific than “move left or right.” Merging the two, however, allows for more robust bio-sensing and makes it possible for the system to work on new users without training.
“By looking at both muscle and brain signals, we can start to pick up on a person’s natural gestures along with their snap decisions about whether something is going wrong,” says DelPreto. “This helps make communicating with a robot more like communicating with another person.”
The team says that they could imagine the system one day being useful for the elderly, or workers with language disorders or limited mobility.
“We’d like to move away from a world where people have to adapt to the constraints of machines,” says Rus. “Approaches like this show that it’s very much possible to develop robotic systems that are a more natural and intuitive extension of us.”