A System For Robot Concept Learning Through Situated Dialogue (2022)

Abstract: Robots operating in unexplored environments with human teammates will need to learn unknown concepts on the fly. To this end, we demonstrate a novel system that combines a computational model of question generation with a cognitive robotic architecture. The model supports dynamic production of back-and-forth dialogue for concept learning given observations of an environment, while the architecture supports symbolic reasoning, action representation, one-shot learning and other capabilities for situated interaction. The system is able to learn about new concepts including objects, locations, and actions, using an underlying approach that is generalizable and scalable. We evaluate the system by comparing learning efficiency to a human baseline in a collaborative reference resolution task and show that the system is effective and efficient in learning new concepts, and that it can informatively generate explanations about its behavior.

## introduction an autonomous robot interacting with a human teammate will often encounter concepts 1 that are unfamiliar to it. this is especially problematic when exploring new environments where training data is naturally limited or expensive to collect and label. for example, a robot for space exploration may encounter objects or need to carry out procedures that do not exist on earth, and for which data is scarce. a natural approach to online learning in many environments is to take advantage of the human-robot interface, enabling the robot to engage in multi-turn dialogue where it asks the human questions to learn about a novel concept. many of the foundations of this approach -e.g., robot architectures capable of engaging in natural figure 1 : example dialogue between a human and our system situated in an unexplored spacecraft environment, where the robot must learn new locations, objects, and actions through interaction with the human. the system's behavior is indicated in red. in this work, we demonstrate a generalizable cognitive robotic system that is able to efficiently learn about unknown concepts through interactive natural language dialogue. this system leverages a probabilistic decision network model 2 to dynam-ically generate and ask optimal questions for concept learning within any environment, while also employing natural language capabilities and an explicit knowledge representation enabled by a cognitive robotic architecture. an example dialogue from our system is shown in figure 1 . ## background early work in robot concept learning through dialogue explored the use of pre-specified ontologies or graphical models to allow an agent to ask questions about objects in an environment @xcite @xcite , or to learn actions through dialogue @xcite . other work explores the use of proactive symbol grounding or pragmatic models for reference resolution @xcite . in contrast to these studies, our work includes a notion of uncertainty and can scale to new task domains through dynamic adaptation of a decision network. recent work has built upon these approaches by introducing information-theoretic measures for selecting optimal questions. @xcite propose a robot that can ask questions about object properties that maximize information gain, and test the system using colors and shapes as properties. @xcite relatedly demonstrate a system that can instantiate templatic questions to minimize entropy of the robot's probabilistic symbol grounding function. both approaches, however, rely on the use of a small fixed set of properties or question templates; we present a scalable approach that can generate questions from arbitrary properties. ## system design our system combines a decision network model for question selection @xcite with the diarc (distributed integrated affect reflection cognition) robotic architecture @xcite in order to enable interactive concept learning. the diarc architecture, which follows a distributed, component-based design, allows for semantic parsing, introspection on knowledge, explanation generation, and support for one-shot learning of actions. the particular configuration of di-arc used by our system is shown in figure 2 . in the remainder of this section, we describe the primary components of this architecture. ## evaluation to evaluate the integrated system, we implemented it on a pr2 robot in a virtual spacecraft environment containing unknown objects and procedures for the robot to learn. the robot performed a collaborative tool organization task in which it was instructed via typed natural language commands to place novel tools in their correct containers. in our evaluation, the robot is given sequences of commands from a subset of the human-robot dialogue learning (hurdl) corpus @xcite consisting of dialogues from 10 participants foot_2 . the human-generated questions in these dialogues are compared to the questions generated by the robot for the same commands in terms of accuracy (the proportion of commands that the robot is able to execute after resolving unknown referents) and question efficiency (the average number of questions that the agent must ask to learn each new concept). the spacecraft environment contains 18 tools, with six main types and three instances of each type (given novel sci-fi names, such as "electro capacitor") that also vary along six feature dimensions such as color, size, etc. the environment also contains 18 containers such as platforms, lockers, and crates; some of these are locked and require learning specialized procedures to open. the robot starts with a basic perceptual representation of the entities in the environment, including their observed properties (e.g., an entity is red, small, etc.), but without a name for any of them. our results are summarized in ## conclusion and future work we presented a robotic system that combines a decision network model for question generation with a cognitive robotic architecture to allow the system to efficiently learn about new concepts in unexplored environments through dialogue. the design of our system is scalable due to the dynamic construction of the decision network, while the robotic architecture allows for broader situated interaction including symbolic reasoning and explanation generation. our evaluation demonstrated that our system, while having slightly lower question efficiency than human participants on the same task, was adept at learning new concepts in our experimental setting. in the future, we aim to allow the robot to automatically acquire property knowledge through exploration prior to concept learning.