Understanding user instructions in natural language is an active research topic in AI and robotics. Typically, natural user instructions are high-level and can be reduced into low-level tasks expressed in common verbs (e.g., take', get', put'). For robots understanding such instructions, one of the key challenges is to process high-level user instructions and achieve the specified tasks with robots' primitive actions. To address this, we propose novel algorithms by utilizing semantic roles of common verbs defined in semantic dictionaries and integrating multiple open knowledge to generate task plans. Specifically, we present a new method for matching and recovering semantics of user instructions and a novel task planner that exploits functional knowledge of robot's action model. To verify and evaluate our approach, we implemented a prototype system using knowledge from several open resources. Experiments on our system confirmed the correctness and efficiency of our algorithms. Notably, our system has been deployed in the KeJia robot, which participated the annual RoboCup@Home competitions in the past three years and achieved encouragingly high scores in the benchmark tests.
The approaches that are most related to ours are the ones using OMICS for robots to complete household tasks. The first attempt to utilize OMICS to accomplish a household task is @cite_26 , which proposed a generative model based on the Markov chain techniques. Later on, @cite_17 @cite_9 @cite_27 presented a system called KNOWROB for processing knowledge in order to achieve more flexible and general behavior. Most recently, we proposed a formal description of knowledge gaps between user instructions and local knowledge in robotic system for instruction understanding @cite_25 @cite_24 @cite_16 @cite_10 . However, in these efforts using OMICS for robot task planning with user instructions, common verbs are normally not defined in the knowledge base, which limits their performance on utilizing existing open knowledge. Thus, our work is proposed to address the weakness of state-of-the-art methods.
@cite_26 @cite_9 @cite_24 @cite_27 @cite_16 @cite_10 @cite_25 @cite_17
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Much recent work in robot learning from demonstration has focused on automatically segmenting continuous task demonstrations into simpler, reusable primitives. However, strong assumptions are often made about how these primitives can be sequenced, limiting the potential for data reuse. We introduce a novel method for discovering semantically grounded primitives and incrementally building and improving a finite-state representation of a task in which various contingencies can arise. Specifically, a Beta Process Autoregressive Hidden Markov Model is used to automatically segment demonstrations into motion categories, which are then further subdivided into semantically grounded states in a finite-state automaton. During replay of the task, a data-driven approach is used to collect additional data where they are most needed through interactive corrections, which are then used to improve the finite-state automaton. Together, this allows for intelligent sequencing of primitives to create novel, adaptive behavior that can be incrementally improved as needed. We demonstrate the utility of this technique on a furniture assembly task using the PR2 mobile manipulator. Autonomous service robots will have to understand vaguely described tasks, such as âset the tableâ or âclean upâ. Performing such tasks as intended requires robots to fully, precisely, and appropriately parameterize their low-level control programs. We propose knowledge processing as a computational resource for enabling robots to bridge the gap between vague task descriptions and the detailed information needed to actually perform those tasks in the intended way. In this article, we introduce the KnowRob knowledge processing system that is specifically designed to provide autonomous robots with the knowledge needed for performing everyday manipulation tasks. The system allows the realization of âvirtual knowledge basesâ: collections of knowledge pieces that are not explicitly represented but computed on demand from the robot's internal data structures, its perception system, or external sources of information. This article gives an overview of the different kinds of knowledge, the different inference mechanisms, and interfaces for acquiring knowledge from external sources, such as the robot's perception system, observations of human activities, Web sites on the Internet, as well as Web-based knowledge bases for information exchange between robots. We evaluate the system's scalability and present different integrated experiments that show its versatility and comprehensiveness. Deployment of robots in practical domains poses key knowledge representation and reasoning challenges. Robots need to represent and reason with incomplete domain knowledge, acquiring and using sensor inputs based on need and availability. This paper presents an architecture that exploits the complementary strengths of declarative programming and probabilistic graphical models as a step toward addressing these challenges. Answer Set Prolog (ASP), a declarative language, is used to represent, and perform inference with, incomplete domain knowledge, including default information that holds in all but a few exceptional situations. A hierarchy of partially observable Markov decision processes (POMDPs) probabilistically models the uncertainty in sensor input processing and navigation. Nonmonotonic logical inference in ASP is used to generate a multinomial prior for probabilistic state estimation with the hierarchy of POMDPs. It is also used with historical data to construct a beta (meta) density model of priors for metareasoning and early termination of trials when appropriate. Robots equipped with this architecture automatically tailor sensor input processing and navigation to tasks at hand, revising existing knowledge using information extracted from sensor inputs. The architecture is empirically evaluated in simulation and on a mobile robot visually localizing objects in indoor domains. We address the problem of a mobile manipulation robot searching for an object in a cluttered domain that is populated with an unknown number of objects in an unknown arrangement. The robot must move around its environment, looking in containers, moving occluding objects to improve its view, and reasoning about collocation of objects of different types, all in service of finding a desired object. The key contribution in reasoning is a Markov-chain Monte Carlo (MCMC) method for drawing samples of the arrangements of objects in an occluded container, conditioned on previous observations of other objects as well as spatial constraints. The key contribution in planning is a receding-horizon forward search in the space of distributions over arrangements (including number and type) of objects in the domain; to maintain tractability the search is formulated in a model that abstracts both the observations and actions available to the robot. The strategy is shown empirically to improve upon a baseline systematic search strategy, and sometimes outperforms a method from previous work.