Talking About Task Progress: Towards Integrating Task Planning and Dialog for Assistive Robotic Services

The use of service robots to assist ageing people in their own homes has the potential to allow people to maintain their independence, increasing their health and quality of life. In many assistive applications, robots perform tasks on people’s behalf that they are unable or unwilling to monitor directly. It is important that users be given useful and appropriate information about task progress. People being assisted in homes and other realworld environments are likely be engaged in other activities while they wait for a service, so information should also be presented in an appropriate, nonintrusive manner. This paper presents a human-robot interaction experiment investigatingwhat type of feedback people prefer in verbal updates by a service robot about distributed assistive services. People found feedback about time until task completion more useful than feedback about events in task progress or no feedback. We also discuss future research directions that involve giving non-expert users more input into the task planning process when delays or failures occur that necessitate replanning or modifying goals

BWIBots: A platform for bridging the gap between AI and human–robot interaction research

Recent progress in both AI and robotics have enabled the development of general purpose robot platforms that are capable of executing a wide variety of complex, temporally extended service tasks in open environments. This article introduces a novel, custom-designed multi-robot platform for research on AI, robotics, and especially human–robot interaction for service robots. Called BWIBots, the robots were designed as a part of the Building-Wide Intelligence (BWI) project at the University of Texas at Austin. The article begins with a description of, and justification for, the hardware and software design decisions underlying the BWIBots, with the aim of informing the design of such platforms in the future. It then proceeds to present an overview of various research contributions that have enabled the BWIBots to better (a) execute action sequences to complete user requests, (b) efficiently ask questions to resolve user requests, (c) understand human commands given in natural language, and (d) understand human intention from afar. The article concludes with a look forward towards future research opportunities and applications enabled by the BWIBot platform

A Survey of Knowledge-based Sequential Decision Making under Uncertainty

Reasoning with declarative knowledge (RDK) and sequential decision-making (SDM) are two key research areas in artificial intelligence. RDK methods reason with declarative domain knowledge, including commonsense knowledge, that is either provided a priori or acquired over time, while SDM methods (probabilistic planning and reinforcement learning) seek to compute action policies that maximize the expected cumulative utility over a time horizon; both classes of methods reason in the presence of uncertainty. Despite the rich literature in these two areas, researchers have not fully explored their complementary strengths. In this paper, we survey algorithms that leverage RDK methods while making sequential decisions under uncertainty. We discuss significant developments, open problems, and directions for future work

Integrating Action Knowledge and LLMs for Task Planning and Situation Handling in Open Worlds

Task planning systems have been developed to help robots use human knowledge (about actions) to complete long-horizon tasks. Most of them have been developed for "closed worlds" while assuming the robot is provided with complete world knowledge. However, the real world is generally open, and the robots frequently encounter unforeseen situations that can potentially break the planner's completeness. Could we leverage the recent advances on pre-trained Large Language Models (LLMs) to enable classical planning systems to deal with novel situations? This paper introduces a novel framework, called COWP, for open-world task planning and situation handling. COWP dynamically augments the robot's action knowledge, including the preconditions and effects of actions, with task-oriented commonsense knowledge. COWP embraces the openness from LLMs, and is grounded to specific domains via action knowledge. For systematic evaluations, we collected a dataset that includes 1,085 execution-time situations. Each situation corresponds to a state instance wherein a robot is potentially unable to complete a task using a solution that normally works. Experimental results show that our approach outperforms competitive baselines from the literature in the success rate of service tasks. Additionally, we have demonstrated COWP using a mobile manipulator. Supplementary materials are available at: https://cowplanning.github.io/Comment: arXiv admin note: substantial text overlap with arXiv:2210.0128