You can watch this seminar here at 1PM EST (10AM PST) on May 15th.
Abstract: To enable the next generation of smart robots and devices which can truly interact with their environments, Simultaneous Localisation and Mapping (SLAM) will progressively develop into a general real-time geometric and semantic `Spatial AI’ perception capability. I will give many examples from our work on gradually increasing visual SLAM capability over the years. However, much research must still be done to achieve true Spatial AI performance. A key issue is how estimation and machine learning components can be used and trained together as we continue to search for the best long-term scene representations to enable intelligent interaction. Further, to enable the performance and efficiency required by real products, computer vision algorithms must be developed together with the sensors and processors which form full systems, and I will cover research on vision algorithms for non-standard visual sensors and graph-based computing architectures.
Biography: Andrew Davison is Professor of Robot Vision and Director of the Dyson Robotics Laboratory at Imperial College London. His long-term research focus is on SLAM (Simultaneous Localisation and Mapping) and its evolution towards general `Spatial AI’: computer vision algorithms which enable robots and other artificial devices to map, localise within and ultimately understand and interact with the 3D spaces around them. With his research group and collaborators he has consistently developed and demonstrated breakthrough systems, including MonoSLAM, KinectFusion, SLAM++ and CodeSLAM, and recent prizes include Best Paper at ECCV 2016 and Best Paper Honourable Mention at CVPR 2018. He has also had strong involvement in taking this technology into real applications, in particular through his work with Dyson on the design of the visual mapping system inside the Dyson 360 Eye robot vacuum cleaner and as co-founder of applied SLAM start-up SLAMcore. He was elected Fellow of the Royal Academy of Engineering in 2017.
“Robotics Today – A series of technical talks” is a virtual robotics seminar series. The goal of the series is to bring the robotics community together during these challenging times. The seminars are scheduled on Fridays at 1PM EDT (10AM PDT) and are open to the public. The format of the seminar consists of a technical talk live captioned and streamed via Web and Twitter (@RoboticsSeminar), followed by an interactive discussion between the speaker and a panel of faculty, postdocs, and students that will moderate audience questions.
Stay up to date with upcoming seminars with the Robotics Today Google Calendar (or download the .ics file) and view past seminars on the Robotics Today Youtube Channel. And follow us on Twitter!
Seminars will be broadcast at 1PM EST (10AM PST) here.
We’ll post links to the recorded seminars soon!
In this episode, Lilly Clark interviews Jeannette Bohg, Assistant Professor at Stanford, about her work in interactive perception and robot learning for grasping and manipulation tasks. Bohg discusses how robots and humans are different, the challenge of high dimensional data, and unsolved problems including continuous learning and decentralized manipulation.
Jeannette Bohg is an Assistant Professor of Computer Science at Stanford University. She was a group leader at MPI until September 2017 and remains affiliated as a guest researcher. Her research focuses on perception for autonomous robotic manipulation and grasping. She is specifically interested in developing methods that are goal-directed, real-time and multi-modal such that they can provide meaningful feedback for execution and learning.
Before joining the Autonomous Motion lab in January 2012, Jeannette Bohg was a PhD student at the Computer Vision and Active Perception lab (CVAP) at KTH in Stockholm. Her thesis on Multi-modal scene understanding for Robotic Grasping was performed under the supervision of Prof. Danica Kragic. She studied at Chalmers in Gothenburg and at the Technical University in Dresden where she received her Masters in Art and Technology and her Diploma in Computer Science, respectively.
Links
Welcome to the voting for the Audience Choice Demo from HRI 2020. Each of these demos showcases an aspect of Human-Robot Interaction research, and alongside “Best Demo” award, we’re offering an “Audience Choice” award. You can see the video and abstract from each demo here, with voting at the bottom. One vote per person. Deadline May 14 11:59 PM BST. You can also register for the Online HRI 2020 Demo Discussion and Award Presentation on May 21 4:00 PM BST.
1. Demonstration of A Social Robot for Control of Remote Autonomous Systems José Lopes, David A. Robb, Xingkun Liu, Helen Hastie
Abstract: There are many challenges when it comes to deploying robots remotely including lack of situation awareness for the operator, which can lead to decreased trust and lack of adoption. For this demonstration, delegates interact with a social robot who acts as a facilitator and mediator between them and the remote robots running a mission in a realistic simulator. We will demonstrate how such a robot can use spoken interaction and social cues to facilitate teaming between itself, the operator and the remote robots.
2. Demonstrating MoveAE: Modifying Affective Robot Movements Using Classifying Variational Autoencoders Michael Suguitan, Randy Gomez, Guy Hoffman
Abstract: We developed a method for modifying emotive robot movements with a reduced dependency on domain knowledge by using neural networks. We use hand-crafted movements for a Blossom robot and a classifying variational autoencoder to adjust affective movement features by using simple arithmetic in the network’s learned latent embedding space. We will demonstrate the workflow of using a graphical interface to modify the valence and arousal of movements. Participants will be able to use the interface themselves and watch Blossom perform the modified movements in real time.
3. An Application of Low-Cost Digital Manufacturing to HRI Lavindra de Silva, Gregory Hawkridge, German Terrazas, Marco Perez Hernandez, Alan Thorne, Duncan McFarlane, Yedige Tlegenov
Abstract: Digital Manufacturing (DM) broadly refers to applying digital information to enhance manufacturing processes, supply chains, products and services. In past work we proposed a low-cost DM architecture, supporting flexible integration of legacy robots. Here we discuss a demo of our architecture using an HRI scenario.
4. Comedy by Jon the Robot John Vilk, Naomi T. Fitter
Abstract: Social robots might be more effective if they could adapt in playful, comedy-inspired ways based on heard social cues from users. Jon the Robot, a robotic stand-up comedian from the Oregon State University CoRIS Institute, showcases how this type of ability can lead to more enjoyable interactions with robots. We believe conference attendees will be both entertained and informed by this novel demonstration of social robotics.
5. CardBot: Towards an affordable humanoid robot platform for Wizard of Oz Studies in HRI Sooraj Krishna, Catherine Pelachaud
Abstract: CardBot is a cardboard based programmable humanoid robot platform designed for inexpensive and rapid prototyping of Wizard of Oz interactions in HRI incorporating technologies such as Arduino, Android and Unity3d. The table demonstration showcases the design of the CardBot and its wizard controls such as animating the movements, coordinating speech and gaze etc for orchestrating an interaction.
6. Towards Shoestring Solutions for UK Manufacturing SMEs Gregory Hawkridge, Benjamin Schönfuß, Duncan McFarlane, Lavindra de Silva, German Terrazas, Liz Salter, Alan Thorne
Abstract: In the Digital Manufacturing on a Shoestring project we focus on low-cost digital solution requirements for UK manufacturing SMEs. This paper shows that many of these fall in the HRI domain while presenting the use of low-cost and off-the-shelf technologies in two demonstrators based on voice assisted production.
7. PlantBot: A social robot prototype to help with behavioral activation in young people with minor depression Max Jan Meijer, Maaike Dokter, Christiaan Boersma, Ashwin Sadananda Bhat, Ernst Bohlmeijer, Jamy Li
Abstract: The PlantBot is a home device that shows iconographic or simple lights to depict actions that it requests a young person (its user) to do as part of Behavioral Activation therapy. In this initial prototype, a separate conversational speech agent (i.e., Amazon Alexa) is wizarded to act as a second system the user can interact with.
8. TapeBot: The Modular Robotic Kit for Creating the Environments Sonya S. Kwak, Dahyun Kang, Hanbyeol Lee, JongSuk Choi
Abstract: Various types of modular robotic kits such as the Lego Mindstorm [1], edutainment robot kit by ROBOTIS [2], and the interactive face components, FacePartBot [3] have been developed and suggested to increase children’s creativity and to learn robotic technologies. By adopting a modular design scheme, these robotic kits enable children to design various robotic characters with plenty of flexibility and creativity, such as humanoids, robotic animals, and robotic faces. However, because a robot is an artifact that perceives an environment and responds to it accordingly, it can also be characterized by the environment it encounters. Thus, in this study, we propose a modular robotic kit that is aimed at creating an interactive environment for which a robot produces various responses.
We chose intelligent tapes to build the environment for the following reasons: First, we presume that decreasing the expectations of consumers toward the functionalities of robotic products may increase their acceptance of the products, because this hinders the mismatch between the expected functions based on their appearances, and the actual functions of the products [4]. We believe that the tape, which is found in everyday life, is a perfect material to lower the consumers’ expectation toward the product and will be helpful for the consumer’s acceptance of it. Second, the tape is a familiar and enjoyable material for children, and it can be used as a flexible module, which users can cut into whatever size they want and can be attached and detached with ease.
In this study, we developed a modular robotic kit for creating an interactive environment, called the TapeBot. The TapeBot is composed of the main character robot and the modular environments, which are the intelligent tapes. Although previous robotic kits focused on building a robot, the TapeBot allows its users to focus on the environment that the robot encounters. By reversing the frame of thinking, we expect that the TapeBot will promote children’s imagination and creativity by letting them develop creative environments to design the interactions of the main character robot.
9. A Gesture Control System for Drones used with Special Operations Forces Marius Montebaur, Mathias Wilhelm, Axel Hessler, Sahin Albayrak
Abstract: Special Operations Forces (SOF) are facing extreme risks when prosecuting crimes in uncharted environments like buildings. Autonomous drones could potentially save officers’ lives by assisting in those exploration tasks, but an intuitive and reliable way of communicating with autonomous systems is yet to be established. This paper proposes a set of gestures that are designed to be used by SOF during operation for interaction with autonomous systems.
10. CoWriting Kazakh: Learning a New Script with a Robot – Demonstration Bolat Tleubayev, Zhanel Zhexenova, Thibault Asselborn, Wafa Johal, Pierre Dillenbourg, Anara Sandygulova
Abstract: This interdisciplinary project aims to assess and manage the risks relating to the transition of Kazakh language from Cyrillic to Latin in Kazakhstan in order to address challenges of a) teaching and motivating children to learn a new script and its associated handwriting, and b) training and providing support for all demographic groups, in particular senior generation. We present the system demonstration that proposes to assist and motivate children to learn a new script with the help of a humanoid robot and a tablet with stylus.
11. Voice Puppetry: Towards Conversational HRI WoZ Experiments with Synthesised Voices Matthew P. Aylett, Yolanda Vazquez-Alvarez
Abstract: In order to research conversational factors in robot design the use of Wizard of Oz (WoZ) experiments, where an experimenter plays the part of the robot, are common. However, for conversational systems using a synthetic voice, it is extremely difficult for the experimenter to choose open domain content and enter it quickly enough to retain conversational flow. In this demonstration we show how voice puppetry can be used to control a neural TTS system in almost real time. The demo hopes to explore the limitations and possibilities of such a system for controlling a robot’s synthetic voice in conversational interaction.
12. Teleport – Variable Autonomy across Platforms Daniel Camilleri, Michael Szollosy, Tony Prescott
Abstract: Robotics is a very diverse field with robots of different sizes and sensory configurations created with the purpose of carrying out different tasks. Different robots and platforms each require their own software ecosystem and are coded with specific algorithms which are difficult to translate to other robots.
VOTING CLOSES ON THURSDAY MAY 14 AT 11:59 PM BST [British Standard Time]
This is part 2 of a three-part series examining the effects of robots and automation on employment, based on new research from economist and Institute Professor Daron Acemoglu.
By Peter Dizikes
Overall, adding robots to manufacturing reduces jobs — by more than three per robot, in fact. But a new study co-authored by an MIT professor reveals an important pattern: Firms that move quickly to use robots tend to add workers to their payroll, while industry job losses are more concentrated in firms that make this change more slowly.
The study, by MIT economist Daron Acemoglu, examines the introduction of robots to French manufacturing in recent decades, illuminating the business dynamics and labor implications in granular detail.
“When you look at use of robots at the firm level, it is really interesting because there is an additional dimension,” says Acemoglu. “We know firms are adopting robots in order to reduce their costs, so it is quite plausible that firms adopting robots early are going to expand at the expense of their competitors whose costs are not going down. And that’s exactly what we find.”
Indeed, as the study shows, a 20 percentage point increase in robot use in manufacturing from 2010 to 2015 led to a 3.2 percent decline in industry-wide employment. And yet, for firms adopting robots during that timespan, employee hours worked rose by 10.9 percent, and wages rose modestly as well.
A new paper detailing the study, “Competing with Robots: Firm-Level Evidence from France,” will appear in the May issue of the American Economic Association: Papers and Proceedings. The authors are Acemoglu, who is an Institute Professor at MIT; Clair Lelarge, a senior research economist at the Banque de France and the Center for Economic Policy Research; and Pascual Restrepo Phd ’16, an assistant professor of economics at Boston University.
A French robot census
To conduct the study, the scholars examined 55,390 French manufacturing firms, of which 598 purchased robots during the period from 2010 to 2015. The study uses data provided by France’s Ministry of Industry, client data from French robot suppliers, customs data about imported robots, and firm-level financial data concerning sales, employment, and wages, among other things.
The 598 firms that did purchase robots, while comprising just 1 percent of manufacturing firms, accounted for about 20 percent of manufacturing production during that five-year period.
“Our paper is unique in that we have an almost comprehensive [view] of robot adoption,” Acemoglu says.
The manufacturing industries most heavily adding robots to their production lines in France were pharmaceutical companies, chemicals and plastic manufacturers, food and beverage producers, metal and machinery manufacturers, and automakers.
The industries investing least in robots from 2010 to 2015 included paper and printing, textiles and apparel manufacturing, appliance manufacturers, furniture makers, and minerals companies.
The firms that did add robots to their manufacturing processes became more productive and profitable, and the use of automation lowered their labor share — the part of their income going to workers — between roughly 4 and 6 percentage points. However, because their investments in technology fueled more growth and more market share, they added more workers overall.
By contrast, the firms that did not add robots saw no change in the labor share, and for every 10 percentage point increase in robot adoption by their competitors, these firms saw their own employment drop 2.5 percent. Essentially, the firms not investing in technology were losing ground to their competitors.
This dynamic — job growth at robot-adopting firms, but job losses overall — fits with another finding Acemoglu and Restrepo made in a separate paper about the effects of robots on employment in the U.S. There, the economists found that each robot added to the work force essentially eliminated 3.3 jobs nationally.
“Looking at the result, you might think [at first] it’s the opposite of the U.S. result, where the robot adoption goes hand in hand with destruction of jobs, whereas in France, robot-adopting firms are expanding their employment,” Acemoglu says. “But that’s only because they’re expanding at the expense of their competitors. What we show is that when we add the indirect effect on those competitors, the overall effect is negative and comparable to what we find the in the U.S.”
Superstar firms and the labor share issue
The competitive dynamics the researchers found in France resemble those in another high-profile piece of economics research recently published by MIT professors. In a recent paper, MIT economists David Autor and John Van Reenen, along with three co-authors, published evidence indicating the decline in the labor share in the U.S. as a whole was driven by gains made by “superstar firms,” which find ways to lower their labor share and gain market power.
While those elite firms may hire more workers and even pay relatively well as they grow, labor share declines in their industries, overall.
“It’s very complementary,” Acemoglu observes about the work of Autor and Van Reenen. However, he notes, “A slight difference is that superstar firms [in the work of Autor and Van Reenen, in the U.S.] could come from many different sources. By having this individual firm-level technology data, we are able to show that a lot of this is about automation.”
So, while economists have offered many possible explanations for the decline of the labor share generally — including technology, tax policy, changes in labor market institutions, and more — Acemoglu suspects technology, and automation specifically, is the prime candidate, certainly in France.
“A big part of the [economic] literature now on technology, globalization, labor market institutions, is turning to the question of what explains the decline in the labor share,” Acemoglu says. “Many of those are reasonably interesting hypotheses, but in France it’s only the firms that adopt robots — and they are very large firms — that are reducing their labor share, and that’s what accounts for the entirety of the decline in the labor share in French manufacturing. This really emphasizes that automation, and in particular robots, is a critical part in understanding what’s going on.”
Image: Stock image edited by MIT News
This is part 1 of a three-part series examining the effects of robots and automation on employment, based on new research from economist and Institute Professor Daron Acemoglu.
In many parts of the U.S., robots have been replacing workers over the last few decades. But to what extent, really? Some technologists have forecast that automation will lead to a future without work, while other observers have been more skeptical about such scenarios.
Now a study co-authored by an MIT professor puts firm numbers on the trend, finding a very real impact — although one that falls well short of a robot takeover. The study also finds that in the U.S., the impact of robots varies widely by industry and region, and may play a notable role in exacerbating income inequality.
“We find fairly major negative employment effects,” MIT economist Daron Acemoglu says, although he notes that the impact of the trend can be overstated.
From 1990 to 2007, the study shows, adding one additional robot per 1,000 workers reduced the national employment-to-population ratio by about 0.2 percent, with some areas of the U.S. affected far more than others.
This means each additional robot added in manufacturing replaced about 3.3 workers nationally, on average.
That increased use of robots in the workplace also lowered wages by roughly 0.4 percent during the same time period.
“We find negative wage effects, that workers are losing in terms of real wages in more affected areas, because robots are pretty good at competing against them,” Acemoglu says.
The paper, “Robots and Jobs: Evidence from U.S. Labor Markets,” appears in advance online form in the Journal of Political Economy. The authors are Acemoglu and Pascual Restrepo PhD ’16, an assistant professor of economics at Boston University.
Displaced in Detroit
To conduct the study, Acemoglu and Restrepo used data on 19 industries, compiled by the International Federation of Robotics (IFR), a Frankfurt-based industry group that keeps detailed statistics on robot deployments worldwide. The scholars combined that with U.S.-based data on population, employment, business, and wages, from the U.S. Census Bureau, the Bureau of Economic Analysis, and the Bureau of Labor Statistics, among other sources.
The researchers also compared robot deployment in the U.S. to that of other countries, finding it lags behind that of Europe. From 1993 to 2007, U.S. firms actually did introduce almost exactly one new robot per 1,000 workers; in Europe, firms introduced 1.6 new robots per 1,000 workers.
“Even though the U.S. is a technologically very advanced economy, in terms of industrial robots’ production and usage and innovation, it’s behind many other advanced economies,” Acemoglu says.
In the U.S., four manufacturing industries account for 70 percent of robots: automakers (38 percent of robots in use), electronics (15 percent), the plastics and chemical industry (10 percent), and metals manufacturers (7 percent).
Across the U.S., the study analyzed the impact of robots in 722 commuting zones in the continental U.S. — essentially metropolitan areas — and found considerable geographic variation in how intensively robots are utilized.
Given industry trends in robot deployment, the area of the country most affected is the seat of the automobile industry. Michigan has the highest concentration of robots in the workplace, with employment in Detroit, Lansing, and Saginaw affected more than anywhere else in the country.
“Different industries have different footprints in different places in the U.S.,” Acemoglu observes. “The place where the robot issue is most apparent is Detroit. Whatever happens to automobile manufacturing has a much greater impact on the Detroit area [than elsewhere].”
In commuting zones where robots were added to the workforce, each robot replaces about 6.6 jobs locally, the researchers found. However, in a subtle twist, adding robots in manufacturing benefits people in other industries and other areas of the country — by lowering the cost of goods, among other things. These national economic benefits are the reason the researchers calculated that adding one robot replaces 3.3 jobs for the country as a whole.
The inequality issue
In conducting the study, Acemoglu and Restrepo went to considerable lengths to see if the employment trends in robot-heavy areas might have been caused by other factors, such as trade policy, but they found no complicating empirical effects.
The study does suggest, however, that robots have a direct influence on income inequality. The manufacturing jobs they replace come from parts of the workforce without many other good employment options; as a result, there is a direct connection between automation in robot-using industries and sagging incomes among blue-collar workers.
“There are major distributional implications,” Acemoglu says. When robots are added to manufacturing plants, “The burden falls on the low-skill and especially middle-skill workers. That’s really an important part of our overall research [on robots], that automation actually is a much bigger part of the technological factors that have contributed to rising inequality over the last 30 years.”
So while claims about machines wiping out human work entirely may be overstated, the research by Acemoglu and Restrepo shows that the robot effect is a very real one in manufacturing, with significant social implications.
“It certainly won’t give any support to those who think robots are going to take all of our jobs,” Acemoglu says. “But it does imply that automation is a real force to be grappled with.”