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1. Learning to communicate about shared procedural abstractions

   McCarthy, William P; Hawkins, Robert D; Wang, Haoliang; Holdaway, Cameron; Fan, Judith E (January 2021, Proceedings of the Annual Conference of the Cognitive Science Society)

   null (Ed.)

   Many real-world tasks require agents to coordinate their behavior to achieve shared goals. Successful collaboration requires not only adopting the same communicative conventions, but also grounding these conventions in the same task-appropriate conceptual abstractions. We investigate how humans use natural language to collaboratively solve physical assembly problems more effectively over time. Human participants were paired up in an online environment to reconstruct scenes containing two block towers. One participant could see the target towers, and sent assembly instructions for the other participant to reconstruct. Participants provided increasingly concise instructions across repeated attempts on each pair of towers, using more abstract referring expressions that captured each scene's hierarchical structure. To explain these findings, we extend recent probabilistic models of ad hoc convention formation with an explicit perceptual learning mechanism. These results shed light on the inductive biases that enable intelligent agents to coordinate upon shared procedural abstractions. 

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2. Language Understanding for Field and Service Robots in a Priori Unknown Environments

   https://doi.org/10.55417/fr.2022040  

   Walter, Matthew; Patki, Siddharth; Daniele, Andrea; Fahnestock, Ethan; Duvallet, Felix; Hemachandra, Sachithra; Oh, Jean; Stentz, Anthony; Roy, Nicholas; Howard, Thomas (March 2022, Field Robotics)

   Contemporary approaches to perception, planning, estimation, and control have allowed robots to operate robustly as our remote surrogates in uncertain, unstructured environments. This progress now creates an opportunity for robots to operate not only in isolation, but also with and alongside humans in our complex environments. Realizing this opportunity requires an efficient and flexible medium through which humans can communicate with collaborative robots. Natural language provides one such medium, and through significant progress in statistical methods for natural-language understanding, robots are now able to interpret a diverse array of free-form navigation, manipulation, and mobile-manipulation commands. However, most contemporary approaches require a detailed, prior spatial-semantic map of the robot’s environment that models the space of possible referents of an utterance. Consequently, these methods fail when robots are deployed in new, previously unknown, or partially-observed environments, particularly when mental models of the environment differ between the human operator and the robot. This paper provides a comprehensive description of a novel learning framework that allows field and service robots to interpret and correctly execute natural-language instructions in a priori unknown, unstructured environments. Integral to our approach is its use of language as a “sensor”—inferring spatial, topological, and semantic information implicit in natural-language utterances and then exploiting this information to learn a distribution over a latent environment model. We incorporate this distribution in a probabilistic, language grounding model and infer a distribution over a symbolic representation of the robot’s action space, consistent with the utterance. We use imitation learning to identify a belief-space policy that reasons over the environment and behavior distributions. We evaluate our framework through a variety of different navigation and mobile-manipulation experiments involving an unmanned ground vehicle, a robotic wheelchair, and a mobile manipulator, demonstrating that the algorithm can follow natural-language instructions without prior knowledge of the environment. 

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3. Towards Situated Communication in Multi-Step Interactions: Time is a Key Pressure in Communication Emergence

   Kalinowska, Aleksandra; Davoodi, Elnaz; Mathewson, Kory W.; Murphey, Todd; Pilarski, Patrick M. (October 2022, Annual Meeting of the Cognitive Science Society)

   Enabling efficient communication in artificial agents brings us closer to machines that can cooperate with each other and with human partners. Hand-engineered approaches have substantial limitations, leading to increased interest in methods for communication to emerge autonomously between artificial agents. Most of the research in the field explores unsituated communication in one-step referential tasks. The tasks are not temporally interactive and lack time pressures typically present in natural communication and language learning. In these settings, agents can successfully learn what to communicate but not when or whether to communicate. Here, we extend the literature by assessing emergence of communication between reinforcement learning agents in a temporally interactive, cooperative task of navigating a gridworld environment. We show that, through multi-step interactions, agents develop just-in-time messaging protocols that enable them to successfully solve the task. With memory—which provides flexibility around message timing—agent pairs converge to a look-ahead communication protocol, finding an optimal solution to the task more quickly than without memory. Lastly, we explore situated communication, enabling the acting agent to choose when and whether to communicate. With the opportunity cost of forgoing an action to communicate, the acting agent learns to solicit information sparingly, in line with the Gricean Maxim of quantity. Our results point towards the importance of studying language emergence through situated communication in multi-step interactions. 

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4. Continual Learning for Grounded Instruction Generation by Observing Human Following Behavior

   https://doi.org/10.1162/tacl_a_00428  

   Kojima, Noriyuki; Suhr, Alane; Artzi, Yoav (January 2021, Transactions of the Association for Computational Linguistics)

   Abstract We study continual learning for natural language instruction generation, by observing human users’ instruction execution. We focus on a collaborative scenario, where the system both acts and delegates tasks to human users using natural language. We compare user execution of generated instructions to the original system intent as an indication to the system’s success communicating its intent. We show how to use this signal to improve the system’s ability to generate instructions via contextual bandit learning. In interaction with real users, our system demonstrates dramatic improvements in its ability to generate language over time. 

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5. PaCo: Preconditions Attributed to Commonsense Knowledge

   Qasemi, Ehsan; Ilievski, Filip; Chen, Muhao; Szekely, Pedro (January 2022, Findings of the Association for Computational Linguistics: EMNLP 2022)

   Humans can seamlessly reason with circumstantial preconditions of commonsense knowledge. We understand that a glass is used for drinking water, unless the glass is broken or the water is toxic. Despite state-of-the-art (SOTA) language models’ (LMs) impressive performance on inferring commonsense knowledge, it is unclear whether they understand the circumstantial preconditions. To address this gap, we propose a novel challenge of reasoning with circumstantial preconditions. We collect a dataset, called PaCo, consisting of 12.4 thousand preconditions of commonsense statements expressed in natural language. Based on this dataset, we create three canonical evaluation tasks and use them to examine the capability of existing LMs to understand situational preconditions. Our results reveal a 10-30% gap between machine and human performance on our tasks, which shows that reasoning with preconditions is an open challenge. 

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