United States

The development of robots that can learn to interact with the world and manipulate the objects in it has emerged as one of the greatest and so far largely unsolved challenges in robotics research. In this talk, I will argue that the development of such advanced machines requires a transition from classical manual design with purely model-based control to a novel synthesis paradigm. We need to allow the machine to autonomously develop its own blueprint and algorithmically generate its topological, kinematic, and dynamic self. Building on this, it shall develop controls for its own body as it moves, learns to manipulate objects in a controlled way, and sensitively interacts with the world.

Drawing from our work in torque-controlled lightweight robots towards human-safe tactile robots that can manipulate, fly, or drive, I outline the technological quantum leaps that recently have taken place. In particular, this progress was made possible by a human-centered design, soft and force-sensitive control, contact reflexes, and model-based machine learning. In the real world, by enabling human-robot coexistence, collaboration, and interaction for the first time, this robotic technology has proven transformative to traditional manufacturing already around the globe. Increasingly, it is now impacting professional services, domestic applications, medicine and healthcare.

After that, I will use our current work to chart the path toward the next generation of tactile machines. We have taken first steps towards autonomously designing and building machines that have the ability to learn their self and thus adapt to changes in body topology and ultimately their entire dynamics. Finally, I will present recent results on designing modular control and learning architectures that achieve complex behaviors for challenging manipulation problems while being provably stable.

AAAI 2023

Robots with a sense of touch: self-replicating the machine and learning the self

**31st IEEE International Conference on Robot & Human Interactive Communication**
 This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed.
 
 The conference covers a wide range of topics related to Robot and Human Interactive Communication, involving theories, methodologies, technologies, empirical and experimental studies. Papers related to the study of robotic technology, psychology, cognitive science, artificial intelligence, human factors, ethics and policies, interaction-based robot design and other topics related to human-robot interaction are welcome.
 
 **CONFERENCE THEME:**
 Social, asocial, and antisocial robots
 
 Robots are starting to populate our lives: at work, in our homes, hotels, shops. However, not everybody is comfortable with robots finding a place in society. For example, people may be afraid that robots will replace humans in their jobs. This belief is exacerbated by the media that, on one side, raise expectations of robots’ capabilities, while amplifying fears and worries on the other side.
 
 In the next years, due to the increasing request for interaction with people in social environments, academic research and market analyses predict that robots will become more and more “social”. But, at the same time, robots may incur the risk of being perceived as “asocial” as their pervasiveness in everyday environments may become an obstacle to social interaction between humans. Or they may even be rejected as “antisocial” if researchers do not take care to develop robots that behave according to the customs and habits of the communities where they are expected to operate.
 
 To prevent this from happening, robotic researchers can no longer take the luxury of being mere technicians. They must be capable of addressing all the challenges raised by future robot-populated communities in a multidisciplinary way. They need to share their knowledge with the public to make people aware of what robots really are, what expectations we can have from them, the risks we may incur, and the possible mitigation plans.
 
 While being open to any kind of contribution related to Human-Robot Interactive Communication, the RO-MAN 2022 conference will emphasize these aspects of robotic research. How can we create robots capable of being welcomed as our assistants and companions in society while, at the same time, not being perceived as “asocial” or “antisocial?” How can we create robots perceived as facilitators of the natural development of human beings and communities, rather than obstacles?
 
 Contributions from different disciplines, not exclusively related to technological development, will be welcomed as crucial to pave the way to global acceptance of future robots with an informed attitude.
 
 [![](https://assets.underline.io/uploads/markdown_image/1/image/af259abe105b3791c498ae1552d75710.png)](https://drive.google.com/file/d/18w73BZPB_JVOgcrcdZDqYpEupwVt4CnU/view?usp=sharing)

A registration is required to access this event page. If you have not already done so, please use the below link to register for the event. 

[Register Here](http://www.smile.unina.it/ro-man2022/welcome-to-ieee-ro-man-2022/)

Registration Is Required

IEEE RO-MAN 2022

31st IEEE International Conference on Robot & Human Interactive Communication
This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed.

invited talk

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Data augmentation is now an essential part of the image training process, as it effectively prevents overfitting and makes the model more robust against noisy datasets. Recent mixing augmentation strategies have advanced to generate the mixup mask to enrich the saliency information, which is a supervisory signal. However, these methods incur a significant computational burden to optimize the mixup mask. From this motivation, we propose a novel saliency-aware mixup method, GuidedMixup, which aims to retain the salient regions in mixup images with low computational overhead. We develop an efficient pairing algorithm that pursues to minimize the conflict of salient regions of paired images and achieve the rich saliency in mixup images. Moreover, GuidedMixup controls the mixup ratio for each pixel to better preserve the salient region by interpolating two paired images smoothly. The experiments on several datasets demonstrate that GuidedMixup provides a good trade-off between augmentation overhead and generalization performance on classification datasets. In addition, our method shows good performance in experiments with corrupted or reduced datasets.

GuidedMixup: An Efficient Mixup Strategy Guided by Saliency Maps

Adversarial machine learning (AML) research is concerned with robustness of machine learning models and algorithms to malicious tampering. Originating at the intersection between machine learning and cybersecurity, AML has come to have broader research appeal, stretching traditional notions of security to include applications of computer vision, natural language processing, and network science. In addition, the problems of strategic classification, algorithmic recourse, and counterfactual explanations have essentially the same core mathematical structure as AML, despite distinct motivations. I give a simplified overview of the central problems in AML, and then discuss both the security-motivated AML domains, and the problems above unrelated to security. These together span a number of important AI subdisciplines, but can all broadly be viewed as concerned with trustworthy AI. My goal is to clarify both the technical connections among these, as
well as the substantive differences, suggesting directions for future research.

The Many Faces of Adversarial Machine Learning

AI systems can interact in unexpected ways, sometimes with disastrous consequences. As AI gets to control more of our world, these interactions will become more common and have higher stakes. As AI becomes more advanced, these interactions will become more sophisticated, and game theory will provide the tools for analyzing these interactions. However, AI agents are in some ways unlike the agents traditionally studied in game theory, introducing new challenges as well as opportunities. We propose a research agenda to develop the game theory of highly advanced AI agents, with a focus on achieving cooperation.

Foundations of Cooperative AI

The use of virtual agents (bots) has become essential for providing online assistance to customers. However, even though a lot of effort has been dedicated to the research, development, and deployment of such virtual agents, customers are frequently frustrated with the interaction with the virtual agent and require a human instead. We suggest that a holistic approach, combining virtual agents and human operators working together, is the path to providing satisfactory service. However, implementing such a holistic customer service system will not, and cannot, be achieved using any single AI technology or branch. Rather, such a system will inevitably require the integration of multiple and diverse AI technologies, including natural language processing, multi-agent systems, machine learning, reinforcement learning, and behavioral cloning; in addition to integration with other disciplines such as psychology, business, sociology, economics, operation research, informatics, computer-human interaction, and more. As such, we believe this customer service application offers a rich domain for experimentation and application of multidisciplinary AI. In this paper, we introduce the holistic customer service application and discuss the key AI technologies and disciplines required for a successful AI solution for this setting. For each of these AI technologies, we outline the key scientific questions and research avenues stemming from this setting. We demonstrate that integrating technologies from different fields can lead to a cost-effective successful customer service center. The challenge is that there is a need for several communities, each with its own language and modeling techniques, different problem-solving methods, and different evaluation methodologies, all of which need to work together. Real cooperation will require the formation of joint methodologies and techniques that could improve the service to customers, but, more importantly, open new directions in cooperation of diverse communities toward solving joint difficult tasks.

Customer Service Combining Human Operators and Virtual Agents:A Call for Multidisciplinary AI Research

Consider an intelligent virtual assistant such as Siri, or perhaps a more capable future version of it. Should we think of all of Siri as one big agent? Or is there a separate agent on every phone, each with its own objectives and/or beliefs? And what should those objectives and beliefs be? Such questions reveal that the traditional, somewhat anthropomorphic model of an agent – with clear boundaries, centralized belief formation and decision making, and a clear given objective – falls short for thinking about today’s AI systems. We need better methods for specifying the objectives that these agents should pursue in the real world, especially when their actions have ethical implications. I will discuss some methods that we have been developing for this purpose, drawing on techniques from preference elicitation and computational social choice. But we need to specify more than objectives. When agents are distributed, systematically forget what they knew before (say, for privacy reasons), can be simulated by others, and potentially face copies of themselves, it is no longer obvious what the correct way is even to do probabilistic reasoning, let alone to make optimal decisions. I will explain why this is so and discuss our work on doing these things well. (No previous background required.)

New Design Decisions for Modern AI Agents

Anima will present exciting developments in the use of AI for scientific applications. This includes diverse domains such as weather and climate modeling, deep earth modeling, genome modeling, etc. We have developed principled approaches that enables zero-shot generalization beyond the training domain. This includes neural operators that yield 4-5 orders of magnitude speedups over numerical weather models and other scientific simulations. They learn mappings between function spaces that makes them ideal for capturing multi-scale processes.

AI accelerating Science: Neural Operators for Learning on Function Spaces

Reinforcement Learning provides an attractive suite of online learning methods for personalizing interventions in Digital Behavioral Health. However, after a reinforcement learning algorithm has been run in a clinical study, how do we assess whether personalization occurred? We might find users for whom it appears that the algorithm has indeed learned in which contexts the user is more responsive to a particular intervention. But could this have happened completely by chance? We discuss some first approaches to addressing these questions

We used Reinforcement Learning; but did it work?

How do we communicate with AI Agents that learn? One obvious answer is via language. Indeed, humans have evolved languages over tens of thousands of years to provide useful abstractions for understanding and interacting with each other and with the physical world. The claim advanced by some is that language influences how we think, what we perceive, how we focus our attention, and what we remember. We use language to capture and share our understanding of the world around us, to communicate high-level goals, intentions and objectives, and to support coordination with others. Importantly, language can provide us with useful and purposeful abstractions that can help us to generalize and transfer knowledge to new situations.

Language comes in many forms. In Computer Science and in the study of AI, we have historically used formal knowledge representation languages and programming languages to capture our understanding of the world and to communicate unambiguously with computers. In this talk I will discuss how formal language can help agents learn and reason with a deep dive on one particular topic – reinforcement learning. I’ll show how we can exploit the syntax and semantics of formal language and automata to aid in the specification of complex reward-worthy behavior, to improve the sample efficiency of learning, and to help agents learn what to remember. In doing so, formal language can help us address some of the challenges to reinforcement learning in the real world.

(Formal) Languages Help AI Agents Learn and Reason

Most work on scholarly document processing assumes that the information processed is trustworthy and factually correct. However, this is not always the case. There are two core challenges, which should be addressed: 1) ensuring that scientific publications are credible — e.g. that claims are not made without supporting evidence, and that all relevant supporting evidence is provided; and 2) that scientific findings are not misrepresented, distorted or outright misreported when communicated by journalists or the general public. In this talk, I will present some first steps towards addressing these problems, discussing our research on exaggeration detection, scientific fact checking, and on modelling information change in scientific communication more broadly.

Beyond Fact Checking — Modelling Information Change in Scientific Communication

I would like to propose an approach to the science of deep learning that roughly follows what physicists do to understand reality: (1) explore phenomena through controlled experiments, and (2) build theories based on toy mathematical models and non-fully- rigorous mathematical reasoning. I will illustrate (1) with the LEGO study (LEGO stands for Learning Equality and Group Operations), where we observe how transformers learn to solve simple linear systems of equations. I will also briefly illustrate (2) with an analysis of the emergence of threshold units when training a two-layers neural network to solve a simple sparse coding problem. The latter analysis connects to the recently discovered Edge of Stability phenomenon.

Based on joint works with Kwangjun Ahn, Arturs Backurs, Sinho Chewi Ronen Eldan, Suriya Gunasekar, Yin Tat Lee, Felipe Suarez, Tal Wagner, Yi Zhang, see arxiv.org/abs/2206.04301 and arxiv.org/abs/2212.07469.

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GuidedMixup: An Efficient Mixup Strategy Guided by Saliency Maps

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Augmented Environment Representations with Complete Object Models

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