Domain independent goal recognition

Goal recognition is generally considered to follow plan recognition. The plan recognition problem is typically defined to be that of identifying which plan in a given library of plans is being executed, given a sequence of observed actions. Once a plan has been identified, the goal of the plan can be assumed to follow. In this work, we address the problem of goal recognition directly, without assuming a plan library. Instead, we start with a domain description, just as is used for plan construction, and a sequence of action observations. The task, then, is to identify which possible goal state is the ultimate destination of the trajectory being observed. We present a formalisation of the problem and motivate its interest, before describing some simplifying assumptions we have made to arrive at a first implementation of a goal recognition system, AUTOGRAPH. We discuss the techniques employed in AUTOGRAPH to arrive at a tractable approximation of the goal recognition problem and show results for the system we have implemented

Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal

Model-free reinforcement learning has recently been shown to be effective at learning navigation policies from complex image input. However, these algorithms tend to require large amounts of interaction with the environment, which can be prohibitively costly to obtain on robots in the real world. We present an approach for efficiently learning goal-directed navigation policies on a mobile robot, from only a single coverage traversal of recorded data. The navigation agent learns an effective policy over a diverse action space in a large heterogeneous environment consisting of more than 2km of travel, through buildings and outdoor regions that collectively exhibit large variations in visual appearance, self-similarity, and connectivity. We compare pretrained visual encoders that enable precomputation of visual embeddings to achieve a throughput of tens of thousands of transitions per second at training time on a commodity desktop computer, allowing agents to learn from millions of trajectories of experience in a matter of hours. We propose multiple forms of computationally efficient stochastic augmentation to enable the learned policy to generalise beyond these precomputed embeddings, and demonstrate successful deployment of the learned policy on the real robot without fine tuning, despite environmental appearance differences at test time. The dataset and code required to reproduce these results and apply the technique to other datasets and robots is made publicly available at rl-navigation.github.io/deployable

Semantic Robot Programming for Goal-Directed Manipulation in Cluttered Scenes

We present the Semantic Robot Programming (SRP) paradigm as a convergence of robot programming by demonstration and semantic mapping. In SRP, a user can directly program a robot manipulator by demonstrating a snapshot of their intended goal scene in workspace. The robot then parses this goal as a scene graph comprised of object poses and inter-object relations, assuming known object geometries. Task and motion planning is then used to realize the user's goal from an arbitrary initial scene configuration. Even when faced with different initial scene configurations, SRP enables the robot to seamlessly adapt to reach the user's demonstrated goal. For scene perception, we propose the Discriminatively-Informed Generative Estimation of Scenes and Transforms (DIGEST) method to infer the initial and goal states of the world from RGBD images. The efficacy of SRP with DIGEST perception is demonstrated for the task of tray-setting with a Michigan Progress Fetch robot. Scene perception and task execution are evaluated with a public household occlusion dataset and our cluttered scene dataset.Comment: published in ICRA 201

Analysis of Three-Dimensional Protein Images

A fundamental goal of research in molecular biology is to understand protein structure. Protein crystallography is currently the most successful method for determining the three-dimensional (3D) conformation of a protein, yet it remains labor intensive and relies on an expert's ability to derive and evaluate a protein scene model. In this paper, the problem of protein structure determination is formulated as an exercise in scene analysis. A computational methodology is presented in which a 3D image of a protein is segmented into a graph of critical points. Bayesian and certainty factor approaches are described and used to analyze critical point graphs and identify meaningful substructures, such as alpha-helices and beta-sheets. Results of applying the methodologies to protein images at low and medium resolution are reported. The research is related to approaches to representation, segmentation and classification in vision, as well as to top-down approaches to protein structure prediction.Comment: See http://www.jair.org/ for any accompanying file

An Efficient Implementation of the Head-Corner Parser

This paper describes an efficient and robust implementation of a bi-directional, head-driven parser for constraint-based grammars. This parser is developed for the OVIS system: a Dutch spoken dialogue system in which information about public transport can be obtained by telephone. After a review of the motivation for head-driven parsing strategies, and head-corner parsing in particular, a non-deterministic version of the head-corner parser is presented. A memoization technique is applied to obtain a fast parser. A goal-weakening technique is introduced which greatly improves average case efficiency, both in terms of speed and space requirements. I argue in favor of such a memoization strategy with goal-weakening in comparison with ordinary chart-parsers because such a strategy can be applied selectively and therefore enormously reduces the space requirements of the parser, while no practical loss in time-efficiency is observed. On the contrary, experiments are described in which head-corner and left-corner parsers implemented with selective memoization and goal weakening outperform `standard' chart parsers. The experiments include the grammar of the OVIS system and the Alvey NL Tools grammar. Head-corner parsing is a mix of bottom-up and top-down processing. Certain approaches towards robust parsing require purely bottom-up processing. Therefore, it seems that head-corner parsing is unsuitable for such robust parsing techniques. However, it is shown how underspecification (which arises very naturally in a logic programming environment) can be used in the head-corner parser to allow such robust parsing techniques. A particular robust parsing model is described which is implemented in OVIS.Comment: 31 pages, uses cl.st