SpLU-RoboNLP 2021

Thora Tenbrink, Bangor University

Beyond physical robots: How to achieve joint spatial reference with a smart environment

Abstract: Interacting with a smart environment involves joint understanding of where things and people are or where they should be. Face-to-face interaction between humans, or between humans and robots, implies clearly identifiable perspectives on the environment that can be used to establish such a joint understanding. A smart environment, in contrast, is ubiquitous and thus perspective-independent. In this talk I will review the implications of this situation in terms of the challenges for establishing joint spatial reference between humans and smart systems, and present a somewhat unconventional solution as an opportunity.

Bio: Thora Tenbrink is a Professor of Linguistics at Bangor University (Wales, UK), who uses linguistic analysis to understand how people think. She is author of “Cognitive Discourse Analysis: An Introduction” (Cambridge University Press, 2020) and "Space, Time, and the Use of Language" (Mouton de Gruyter, 2007), and has co-edited various further books on spatial language, representation, and dialogue

Jean Oh, Carnegie Mellon University

Core Challenges of Embodied Vision-Language Planning

Abstract: Service dogs or police dogs work on real jobs in human environments. Are embodied AI agents intelligent enough to perform service tasks in a real, physical space? Embodied AI is generally considered as one of the ultimate AI problems that would require complex, integrated intelligence combining multiple subfields of AI including natural language understanding, visual understanding, planning, reasoning, inferencing, and prediction. While steep progresses have been witnessed in several subfields in recent years, the field of embodied AI remains extremely challenging. In this talk, we will focus on the Embodied Vision-Language Planning (EVLP) problem to understand the unique technical challenges imposed at the intersection of computer vision, natural language understanding, and planning problems. We will review several examples of the EVLP problem to discuss the current approaches, training environments, and evaluation methodologies. Through in-depth investigation of the current progress on the EVLP problem, this talk aims to assess where we are in term of making progress in EVLP and facilitate future interdisciplinary research to tackle core challenges that have not been fully addressed.

Bio: Jean Oh is an Associate Research Professor at the Robotics Institute at Carnegie Mellon University. She is passionate about creating persistent robots that can co-exist and collaborate with humans in shared environments, learning to improve themselves over time through continuous training, exploration, and interactions. Jean’s current research is focused on autonomous social navigation, natural language direction following, and creative AI. Her team has won two Best Paper Awards in Cognitive Robotics at IEEE International Conference on Robotics and Automation (ICRA) for the works on following natural language directions in unknown environments and socially compliant robot navigation in human crowds, in 2015 and 2018, respectively. Jean received her Ph.D. in Language and Information Technologies at Carnegie Mellon University, M.S. in Computer Science at Columbia University, and B.S. in Biotechnology at Yonsei University in South Korea.

Kartik Narasimhan, Princeton University

Language-guided policy learning for better generalization and safety

Abstract: Recent years have seen exciting developments in autonomous agents that can understand natural language in interactive settings. As we gear up to transfer some of these advances into real-world systems (e.g physical robots, autonomous cars or virtual assistants), we encounter unique challenges that stem from these agents operating in an ever-changing, chaotic world. In this talk, I will focus on our recent efforts at addressing two of these challenges through a combination of NLP and reinforcement learning — 1) grounding novel concepts to their linguistic symbols through interaction, and 2) specification of safety constraints during policy learning. First, I will demonstrate a new benchmark of tasks we designed specifically to measure an agent's ability to ground new concepts for generalization, along with a new model for grounding entities and dynamics without any prior mapping provided. Next, I will show how we can train control policies with safety constraints specified in natural language. This will encourage more widespread use of methods for safety-aware policy learning, which otherwise require domain expertise to specify constraints. Scaling up these techniques can help bring us closer to deploying learning systems that can interact seamlessly and responsibly with humans in everyday life.

Bio: Karthik Narasimhan is an assistant professor in the Computer Science department at Princeton University. His research spans the areas of natural language processing and reinforcement learning, with a view towards building intelligent agents that learn to operate in the world through both their own experience and leveraging existing human knowledge. Karthik received his PhD from MIT in 2017, and spent a year as a visiting research scientist at OpenAI prior to joining Princeton in 2018. His work has received a best paper award at EMNLP 2016 and an honorable mention for best paper at EMNLP 2015.

Maja Matarić, University of Southern California

Socially Assistive Robotics: Implications for Health, Education, Training, and the Future of Work

Abstract: The nexus of advances in robotics, NLU, and machine learning has created opportunities for personalized robots for the ultimate robotics frontier: the home. The current pandemic has both caused and exposed unprecedented levels of health & wellness, education, and training needs worldwide, which must increasingly be addressed in the home. Socially assistive robotics has the potential to address those needs through personalized and affordable in-home support. This talk will discuss human-robot interaction methods for socially assistive robotics that utilize multi-modal interaction data and expressive and persuasive robot behavior to monitor, coach, and motivate users to engage in health, wellness, education and training activities. Methods and results will be presented that include modeling, learning, and personalizing user motivation, engagement, and coaching of healthy children and adults, stroke patients, Alzheimer's patients, and children with autism spectrum disorders, in short and long-term (month+) deployments in schools, therapy centers, and homes. Research and commercial implications and pathways will be discussed.

Bio: Maja Matarić is the Chan Soon-Shiong Distinguished Professor in the Computer Science Department, Neuroscience Program, and the Department of Pediatrics and Interim Vice President for Research at the University of Southern California, founding director of the USC Robotics and Autonomous Systems Center (RASC), co-director of the USC Robotics Research Lab, and the lead of the Viterbi K-12 STEM Center. She received her PhD in Computer Science and Artificial Intelligence from MIT in 1994, MS in Computer Science from MIT in 1990, and BS in Computer Science from the University of Kansas in 1987.