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.

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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.

CAST YOUR VOTE FOR “AUDIENCE CHOICE”

VOTING CLOSES ON THURSDAY MAY 14 AT 11:59 PM BST [British Standard Time]

The fifth article-series of GAN in computer vision - we discuss self-supervision in adversarial training for unconditional image generation as well as in-layer normalization and style incorporation in high-resolution image synthesis.

Inspired by the biomechanics of cheetahs, researchers have developed a new type of soft robot that is capable of moving more quickly on solid surfaces or in the water than previous generations of soft robots. The new soft robotics are also capable of grabbing objects delicately—or with sufficient strength to lift heavy objects.

Not so long ago, the concept of a fully automated store seemed something of a curiosity. Now, in the midst of the COVID-19 pandemic, the idea of relying on computers and robotics, and checking out groceries by simply picking them off the shelf doesn't seem so peculiar after all.

Taking sharp pictures despite poor lighting conditions, taking snapshots without blurring, recognizing traffic signs and road markings or identifying dangerous situations with specific systems - all of this is possible today with the help of modern cameras.

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.”

By Peter Dizikes

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.”

By Benjamin Eysenbach and Abhishek Gupta

This post is cross-listed on the CMU ML blog.

The history of machine learning has largely been a story of increasing abstraction. In the dawn of ML, researchers spent considerable effort engineering features. As deep learning gained popularity, researchers then shifted towards tuning the update rules and learning rates for their optimizers. Recent research in meta-learning has climbed one level of abstraction higher: many researchers now spend their days manually constructing task distributions, from which they can automatically learn good optimizers. What might be the next rung on this ladder? In this post we introduce theory and algorithms for unsupervised meta-learning, where machine learning algorithms themselves propose their own task distributions. Unsupervised meta-learning further reduces the amount of human supervision required to solve tasks, potentially inserting a new rung on this ladder of abstraction.

We start by discussing how machine learning algorithms use human supervision to find patterns and extract knowledge from observed data. The most common machine learning setting is regression, where a human provides labels $Y$ for a set of examples $X$. The aim is to return a predictor that correctly assigns labels to novel examples. Another common machine learning problem setting is reinforcement learning (RL), where an agent takes actions in an environment. In RL, humans indicate the desired behavior through a reward function that the agent seeks to maximize. To draw a crude analogy to regression, the environment dynamics are the examples $X$, and the reward function gives the labels $Y$. Algorithms for regression and RL employ many tools, including tabular methods (e.g., value iteration), linear methods (e.g., linear regression) kernel-methods (e.g., RBF-SVMs), and deep neural networks. Broadly, we call these algorithms learning procedures: processes that take as input a dataset (examples with labels, or transitions with rewards) and output a function that performs well (achieves high accuracy or large reward) on the dataset.

Machine learning research is similar to the control room for large physics experiments. Researchers have a number of knobs they can tune which affect the performance of the learning procedure. The right setting for the knobs depends on the particular experiment: some settings work well for high-energy experiments; others work well for ultracold atom experiments. Figure Credit.

Similar to lab procedures used in physics and biology, the learning procedures used in machine learning have many knobs¹ that can be tuned. For example, the learning procedure for training a neural network might be defined by an optimizer (e.g., Nesterov, Adam) and a learning rate (e.g., 1e-5). Compared with regression, learning procedures specific to RL (e.g., DDPG) often have many more knobs, including the frequency of data collection and how frequently the policy is updated. Finding the right setting for the knobs can have a large effect on how quickly the learning procedure solves a task, and a good configuration of knobs for one learning procedure may be a bad configuration for another.

Meta-Learning Optimizes Knobs of the Learning Procedure

While machine learning practitioners often carefully tune these knobs by hand, if we are going to solve many tasks, it may be useful to automatic this process. The process of setting the knobs of learning procedures via optimization is called meta-learning [Thrun 1998]. Algorithms that perform this optimization problem automatically are known as meta-learning algorithms. Explicitly tuning the knobs of learning procedures is an active area of research, with various researchers looking at tuning the update rules [Andrychowicz 2016, Duan 2016, Wang 2016], weight initialization [Finn 2017], network weights [Ha 2016], network architectures [Gaier 2019], and other facets of learning procedures.

To evaluate a setting of knobs, meta-learning algorithms consider not one task but a distribution over many tasks. For example, a distribution over supervised learning tasks may include learning a dog detector, learning a cat detector, and learning a bird detector. In reinforcement learning, a task distribution could be defined as driving a car in a smooth, safe, and efficient manner, where tasks differ by the weights they place on smoothness, safety, and efficiency. Ideally, the task distribution is designed to mirror the distribution over tasks that we are likely to encounter in the real world. Since the tasks in a task distribution are typically related, information from one task may be useful in solving other tasks more efficiently. As you might expect, a knob setting that works best on one distribution of tasks may not be the best for another task distribution; the optimal knob setting depends on the task distribution.

An illustration of meta-learning, where tasks correspond to arranging blocks into different types of towers. The human has a particular block tower in mind and rewards the robot when it builds the correct tower. The robot’s aim is to build the correct tower as quickly as possible.

In many settings we want to do well on a task distribution to which we have only limited access. For example, in a self-driving car, tasks may correspond to finding the optimal balance of smoothness, safety, and efficiency for each rider, but querying riders to get rewards is expensive. A researcher can attempt to manually construct a task distribution that mimics the true task distribution, but this can be quite challenging and time consuming. Can we avoid having to manually design such task distributions?

To answer this question, we must understand where the benefits of meta-learning come from. When we define task distributions for meta-learning, we do so with some prior knowledge in mind. Without this prior information, tuning the knobs of a learning procedure is often a zero-sum game: setting the knobs to any configuration will accelerate learning on some tasks while slowing learning on other tasks. Does this suggest there is no way to see the benefit of meta-learning without the manual construction of task distributions? Perhaps not! The next section presents an alternative.

Optimizing the Learning Procedure with Self-Proposed Tasks

If designing task distributions is the bottleneck in applying meta-learning algorithms, why not have meta-learning algorithms propose their own tasks? At first glance this seems like a terrible idea, because the No Free Lunch Theorem suggests that this is impossible, without additional knowledge. However, many real-world settings do provide a bit of additional information, albeit disguised as unlabeled data. For example, in regression, we might have access to an unlabeled dataset and know that the downstream tasks will be labeled versions of this same image dataset. In a RL setting, a robot can interact with its environment without receiving any reward, knowing that downstream tasks will be constructed by defining reward functions for this very environment (i.e. the real world). Seen from this perspective, the recipe for unsupervised meta-learning (doing meta-learning without manually constructed tasks) becomes clear: given unlabeled data, construct task distributions from this unlabeled data or environment, and then meta-learn to quickly solve these self-proposed tasks.

In unsupervised meta-learning, the agent proposes its own tasks, rather than relying on tasks proposed by a human.

How can we use this unlabeled data to construct task distributions which will facilitate learning downstream tasks? In the case of regression, prior work on unsupervised meta-learning [Hsu 2018, Khodadadeh 2019] clusters an unlabeled dataset of images and then randomly chooses subsets of the clusters to define a distribution of classification tasks. Other work [Jabri 2019] look at an RL setting: after exploring an environment without a reward function to collect a set of behaviors that are feasible in this environment, these behaviors are clustered and used to define a distribution of reward functions. In both cases, even though the tasks constructed can be random, the resulting task distribution is not random, because all tasks share the underlying unlabeled data — the image dataset for regression and the environment dynamics for reinforcement learning. The underlying unlabeled data are the inductive bias with which we pay for our free lunch.

Let us take a deeper look into the RL case. Without knowing the downstream tasks or reward functions, what is the “best” task distribution for “practicing” to solve tasks quickly? Can we measure how effective a task distribution is for solving unknown, downstream tasks? Is there any sense in which one unsupervised task proposal mechanism is better than another? Understanding the answers to these questions may guide the principled development of meta-learning algorithms with little dependence on human supervision. Our work [Gupta 2018], takes a first step towards answering these questions. In particular, we examine the worst-case performance of learning procedures, and derive an optimal unsupervised meta-reinforcement learning procedure.

Optimal Unsupervised Meta-Learning

To answer the questions posed above, our first step is to define an optimal meta-learner for the case where the distribution of tasks is known. We define an optimal meta-learner as the learning procedure that achieves the largest expected reward, averaged across the distribution of tasks. More precisely, we will compare the expected reward for a learning procedure $f$ to that of best learning procedure $f^*$, defining the regret of $f$ on a task distribution $p$ as follows:

Extending this definition to the case of unsupervised meta-learning, an optimal unsupervised meta-learner can be defined as a meta-learner that achieves the minimum worst-case regret across all possible task distributions that may be encountered in the environment. In the absence of any knowledge about the actual downstream task, we resort to a worst case formulation. An unsupervised meta-learning algorithm will find a single learning procedure $f$ that has the lowest regret against an adversarially chosen task distribution $p$:

$$\min_f \max_p \;\; {\rm Regret}(f,p).$$

Our work analyzes how exactly we might obtain such an optimal unsupervised meta-learner, and provides bounds on the regret that it might incur in the worst case. Specifically, under some restrictions on the family of tasks that might be encountered at test-time, the optimal distribution for an unsupervised meta-learner to propose is uniform over all possible tasks.

The intuition for this is straightforward: if the test time task distribution can be chosen adversarially, the algorithm must make sure it is uniformly good over all possible tasks that might be encountered. As a didactic example, if test-time reward functions were restricted to the class of goal-reaching tasks, the regret for reaching a goal at test-time is inverse related to the probability of sampling that goal during training-time. If any one of the goals $g$ has lower density than the others, an adversary can propose a task distribution solely consisting of reaching that goal $g$ causing the learning procedure to incur a higher regret. This example suggests that we can find an optimal unsupervised meta-learner using a uniform distribution over goals. Our paper formalizes this idea and extends it to broader classes task distributions.

Now, actually sampling from a uniform distribution over all possible tasks is quite challenging. Several recent papers have proposed RL exploration methods based on maximizing mutual information [Achiam 2018, Eysenbach 2018, Gregor 2016, Lee 2019, Sharma 2019]. In this work, we show that these methods provide a tractable approximation to the uniform distribution over task distributions. To understand why this is, we can look at the form of a mutual information considered by [Eysenbach 2018], between states $s$ and latent variables $z$:

$$\mathcal{I}(s,z) = \mathcal{H}(s) - \mathcal{H}(s|z).$$

In this objective, the first marginal entropy term is maximized when there is a uniform distribution over all possible tasks. The second conditional entropy term ensures consistency, by making sure that for each $z$, the resulting distribution of $s$ is narrow. This suggests constructing unsupervised task-distributions in an environment by optimizing mutual information gives us a provably optimal task distribution, according to our notion of min-max optimality.

While the analysis makes some limiting assumptions about the forms of tasks encountered, we show how this analysis can be extended to provide a bound on the performance in the most general case of reinforcement learning. It also provides empirical gains on several simulated environments as compared to methods which train from scratch, as shown in the Figure below.

Summary & Discussion

In summary:

• Learning procedures are recipes for converting datasets into function approximators. Learning procedures have many knobs, which can be tuned by optimizing the learning procedures to solve a distribution of tasks.

• Manually designing these task distributions is challenging, so a recent line of work suggests that the learning procedure can use unlabeled data to propose its own tasks for optimizing its knobs.

• These unsupervised meta-learning algorithms allow for learning in regimes previously impractical, and further expand that capability of machine learning methods.

• This work closely relates to other works on unsupervised skill discovery, exploration and representation learning, but explicitly optimizes for transferability of the representations and skills to downstream tasks.

A number of open questions remain about unsupervised meta-learning:

• Unsupervised learning is closely connected to unsupervised meta-learning: the former uses unlabeled data to learn features, while the second uses unlabeled data to tune the learning procedure. Might there be some unifying treatment of both approaches?

• Our analysis only proves that task proposal based on mutual information is optimal for memoryless meta-learning algorithms. Meta-learning algorithms with memory, which we expect will perform better, may perform best with different task proposal mechanisms.

• Scaling unsupervised meta learning to leverage large-scale datasets and complex tasks holds the promise of acquiring learning procedures for solving real-world problems more efficiently than our current learning procedures.

Check out our paper for more experiments and proofs: https://arxiv.org/abs/1806.04640

Acknowledgments

Thanks to Jake Tyo, Conor Igoe, Sergey Levine, Chelsea Finn, Misha Khodak, Daniel Seita, and Stefani Karp for their feedback.This article was initially published on the BAIR blog, and appears here with the authors’ permission.

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Modern technology affects different workers in different ways. In some white-collar jobs—designer, engineer—people become more productive with sophisticated software at their side. In other cases, forms of automation, from robots to phone-answering systems, have simply replaced factory workers, receptionists, and many other kinds of employees.

Dang robots are crummy at so many jobs, and they tell lousy jokes to boot. In two new studies, these were common biases human participants held toward robots.

Dr. Tracy Xu, lecturer in hospitality at the University of Surrey's School of Hospitality and Tourism Management, has published a paper in the International Journal of Contemporary Hospitality Management derived from interviews with 19 hotel HR experts to identify the key trends and major challenges that will emerge in the next 10 years and how leaders should deal with the challenges brought about by service robot technologies.

There have been talks of surgical robots since as early as 1985. While the medical sector has taken significant steps since then to implement such technology, major barriers are still evident.

By Peter Dizikes
This is part 3 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. 

Modern technology affects different workers in different ways. In some white-collar jobs — designer, engineer — people become more productive with sophisticated software at their side. In other cases, forms of automation, from robots to phone-answering systems, have simply replaced factory workers, receptionists, and many other kinds of employees.

Now a new study co-authored by an MIT economist suggests automation has a bigger impact on the labor market and income inequality than previous research would indicate — and identifies the year 1987 as a key inflection point in this process, the moment when jobs lost to automation stopped being replaced by an equal number of similar workplace opportunities.

“Automation is critical for understanding inequality dynamics,” says MIT economist Daron Acemoglu, co-author of a newly published paper detailing the findings.

Within industries adopting automation, the study shows, the average “displacement” (or job loss) from 1947-1987 was 17 percent of jobs, while the average “reinstatement” (new opportunities) was 19 percent. But from 1987-2016, displacement was 16 percent, while reinstatement was just 10 percent. In short, those factory positions or phone-answering jobs are not coming back.

“A lot of the new job opportunities that technology brought from the 1960s to the 1980s benefitted low-skill workers,” Acemoglu adds. “But from the 1980s, and especially in the 1990s and 2000s, there’s a double whammy for low-skill workers: They’re hurt by displacement, and the new tasks that are coming, are coming slower and benefitting high-skill workers.”

The new paper, “Unpacking Skill Bias: Automation and New Tasks,” 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, and Pascual Restrepo PhD ’16, an assistant professor of economics at Boston University.

Low-skill workers: Moving backward

The new paper is one of several studies Acemoglu and Restrepo have conducted recently examining the effects of robots and automation in the workplace. In a just-published paper, they concluded that across the U.S. from 1993 to 2007, each new robot replaced 3.3 jobs.

In still another new paper, Acemoglu and Restrepo examined French industry from 2010 to 2015. They found that firms that quickly adopted robots became more productive and hired more workers, while their competitors fell behind and shed workers — with jobs again being reduced overall.

In the current study, Acemoglu and Restrepo construct a model of technology’s effects on the labor market, while testing the model’s strength by using empirical data from 44 relevant industries. (The study uses U.S. Census statistics on employment and wages, as well as economic data from the Bureau of Economic Analysis and the Bureau of Labor Studies, among other sources.)

The result is an alternative to the standard economic modeling in the field, which has emphasized the idea of “skill-biased” technological change — meaning that technology tends to benefit select high-skilled workers more than low-skill workers, helping the wages of high-skilled workers more, while the value of other workers stagnates. Think again of highly trained engineers who use new software to finish more projects more quickly: They become more productive and valuable, while workers lacking synergy with new technology are comparatively less valued.  

However, Acemoglu and Restrepo think even this scenario, with the prosperity gap it implies, is still too benign. Where automation occurs, lower-skill workers are not just failing to make gains; they are actively pushed backward financially. Moreover,  Acemoglu and Restrepo note, the standard model of skill-biased change does not fully account for this dynamic; it estimates that productivity gains and real (inflation-adjusted) wages of workers should be higher than they actually are.

More specifically, the standard model implies an estimate of about 2 percent annual growth in productivity since 1963, whereas annual productivity gains have been about 1.2 percent; it also estimates wage growth for low-skill workers of about 1 percent per year, whereas real wages for low-skill workers have actually dropped since the 1970s.

“Productivity growth has been lackluster, and real wages have fallen,” Acemoglu says. “Automation accounts for both of those.” Moreover, he adds, “Demand for skills has gone down almost exclusely in industries that have seen a lot of automation.”

Why “so-so technologies” are so, so bad

Indeed, Acemoglu says, automation is a special case within the larger set of technological changes in the workplace. As he puts it, automation “is different than garden-variety skill-biased technological change,” because it can replace jobs without adding much productivity to the economy.

Think of a self-checkout system in your supermarket or pharmacy: It reduces labor costs without making the task more efficient. The difference is the work is done by you, not paid employees. These kinds of systems are what Acemoglu and Restrepo have termed “so-so technologies,” because of the minimal value they offer.

“So-so technologies are not really doing a fantastic job, nobody’s enthusiastic about going one-by-one through their items at checkout, and nobody likes it when the airline they’re calling puts them through automated menus,” Acemoglu says. “So-so technologies are cost-saving devices for firms that just reduce their costs a little bit but don’t increase productivity by much. They create the usual displacement effect but don’t benefit other workers that much, and firms have no reason to hire more workers or pay other workers more.”

To be sure, not all automation resembles self-checkout systems, which were not around in 1987. Automation at that time consisted more of printed office records being converted into databases, or machinery being added to sectors like textiles and furniture-making. Robots became more commonly added to heavy industrial manufacturing in the 1990s. Automation is a suite of technologies, continuing today with software and AI, which are inherently worker-displacing.

“Displacement is really the center of our theory,” Acemoglu says. “And it has grimmer implications, because wage inequality is associated with disruptive changes for workers. It’s a much more Luddite explanation.”

After all, the Luddites — British textile mill workers who destroyed machinery in the 1810s — may be synonymous with technophobia, but their actions were motivated by economic concerns; they knew machines were replacing their jobs. That same displacement continues today, although, Acemoglu contends, the net negative consequences of technology on jobs is not inevitable. We could, perhaps, find more ways to produce job-enhancing technologies, rather than job-replacing innovations.

“It’s not all doom and gloom,” says Acemoglu. “There is nothing that says technology is all bad for workers. It is the choice we make about the direction to develop technology that is critical.”

In a recent project for Mercedes-Benz (Daimler AG) RobCo S.W.A.T. used RoboDK software to simulate ABB and Motoman robots working on a range of automotive manufacturing tasks including assembly and painting.

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.