Sebastian Stock

Bio: Sebastian Stock is an academic researcher from University of Osnabrück. The author has contributed to research in topics: Computer science & Robot. The author has an hindex of 6, co-authored 9 publications receiving 138 citations.

An Ontology-based Multi-level Robot Architecture for Learning from Experiences

Abstract: One way to improve the robustness and flexibility of robot performance is to let the robot learn from its ex- periences. In this paper, we describe the architecture and knowledge-representation framework for a service robot being developed in the EU project RACE, and present examples illustrating how learning from experi- ences will be achieved. As a unique innovative feature, the framework combines memory records of low-level robot activities with ontology-based high-level seman- tic descriptions.

Online task merging with a hierarchical hybrid task planner for mobile service robots

Abstract: Plan-based robot control has to consider a multitude of aspects of tasks at once, such as task dependency, time, space, and resource usage. Hybrid planning is a strategy for treating them jointly. However, by incorporating all these aspects into a hybrid planner, its search space is huge by construction. This paper introduces the planner CHIMP, which is based on meta-CSP planning to represent the hybrid plan space and uses hierarchical planning as the strategy for cutting efficiently through this space. The paper makes two contributions: First, it describes how HTN planning is integrated into meta-CSP reasoning leading to a planner that can reason about different forms of knowledge and that is fast enough to be used on a robot. Second, it demonstrates CHIMP's task merging capabilities, i.e., the unification of different tasks from different plan parts, resulting in plans that are more efficient to execute. It also allows to merge new tasks online into a plan that is being executed. This is demonstrated on a PR2 robot.

The RACE Project : Robustness by Autonomous Competence Enhancement

Abstract: This paper reports on the aims, the approach, and the results of the European project RACE. The project aim was to enhance the behavior of an autonomous robot by having the robot learn from conceptualized experiences of previous performance, based on initial models of the domain and its own actions in it. This paper introduces the general system architecture; it then sketches some results in detail regarding hybrid reasoning and planning used in RACE, and instances of learning from the experiences of real robot task execution. Enhancement of robot competence is operationalized in terms of performance quality and description length of the robot instructions, and such enhancement is shown to result from the RACE system.

Planning Domain + Execution Semantics: A Way Towards Robust Execution?

Abstract: Robots are expected to carry out complex plans in real world environments. This requires the robot to track the progress of plan execution and detect failures which may occur. Planners use very abstract world models to generate plans. Additional causal, temporal, categorical knowledge about the execution, which is not included in the planner's model, is often avail- able. Can we use this knowledge to increase robustness of execution and provide early failure detection? We propose to use a dedicated Execution Model to monitor the executed plan based on runtime observations and rich execution knowl- edge. We show that the combined used of causal, temporal and categorical knowledge allows the robot to detect failures even when the effects of actions are not directly observable. A dedicated Execution model also introduces a degree of mod- ularity, since the platform- and execution-specific knowledge does not need to be encoded into the planner.

Hierarchical Hybrid Planning in a Mobile Service Robot

Abstract: Planning with diverse knowledge, i.e., hybrid planning, is essential for robotic applications. However, powerful heuristics are needed to reason efficiently in the resulting large search spaces. HTN planning provides a means to reduce the search space; furthermore, meta-CSP search has shown promise in hybrid domains, both wrt. search and online plan adaptation. In this paper we combine the two approaches by implementing HTN-style task decomposition as a meta-constraint in a meta-CSP search, resulting in an HTN planner able to handle very rich domain knowledge. The planner produces partial-order plans and if several goal tasks are given, subtasks can be shared, leading to shorter plans. We demonstrate the straightforward integration of different kinds of knowledge for causal, temporal and resource knowledge as well as knowledge provided by an external path planner. The resulting online planner, CHIMP, is integrated in a plan-based robot control system and is demonstrated to physically guide a PR2 robot.

Automated Planning: Theory and Practice.

Abstract: A translation apparatus is provided which comprises: an inputting section for inputting a source document in a natural language; a layout analyzing section for analyzing layout information including cascade information, itemization information, numbered itemization information, labeled itemization information and separator line information in the source document inputted by the inputting section and specifying a translation range on the basis of the layout information; a translation processing section for translating a source document text in the specified translation range into a second language; and an outputting section for outputting a translated text provided by the translation processing section.

Ego-centered networks and the ripple effect

Abstract: Recent work has demonstrated that many social networks, and indeed many networks of other types also, have broad distributions of vertex degree. Here we show that this has a substantial impact on the shape of ego-centered networks, i.e., sets of network vertices that are within a given distance of a specified central vertex, the ego. This in turn affects concepts and methods based on ego-centered networks, such as snowball sampling and the "ripple effect". In particular, we argue that one's acquaintances, one's immediate neighbors in the acquaintance network, are far from being a random sample of the population, and that this biases the numbers of neighbors two and more steps away. We demonstrate this concept using data drawn from academic collaboration networks, for which, as we show, current simple theories for the typical size of ego-centered networks give numbers that differ greatly from those measured in reality. We present an improved theoretical model which gives significantly better results.

Deliberation for autonomous robots: A survey

Abstract: Autonomous robots facing a diversity of open environments and performing a variety of tasks and interactions need explicit deliberation in order to fulfill their missions. Deliberation is meant to endow a robotic system with extended, more adaptable and robust functionalities, as well as reduce its deployment cost. The ambition of this survey is to present a global overview of deliberation functions in robotics and to discuss the state of the art in this area. The following five deliberation functions are identified and analyzed: planning, acting, monitoring, observing, and learning. The paper introduces a global perspective on these deliberation functions and discusses their main characteristics, design choices and constraints. The reviewed contributions are discussed with respect to this perspective. The survey focuses as much as possible on papers with a clear robotics content and with a concern on integrating several deliberation functions.

The actor's view of automated planning and acting: A position paper

Abstract: Planning is motivated by acting. Most of the existing work on automated planning underestimates the reasoning and deliberation needed for acting; it is instead biased towards path-finding methods in a compactly specified state-transition system. Researchers in this AI field have developed many planners, but very few actors. We believe this is one of the main causes of the relatively low deployment of automated planning applications. In this paper, we advocate a change in focus to actors as the primary topic of investigation. Actors are not mere plan executors: they may use planning and other deliberation tools, before and during acting. This change in focus entails two interconnected principles: a hierarchical structure to integrate the actor@?s deliberation functions, and continual online planning and reasoning throughout the acting process. In the paper, we discuss open problems and research directions toward that objective in knowledge representations, model acquisition and verification, synthesis and refinement, monitoring, goal reasoning, and integration.

Object Learning Through Active Exploration

Abstract: This paper addresses the problem of active object learning by a humanoid child-like robot, using a developmental approach. We propose a cognitive architecture where the visual representation of the objects is built incrementally through active exploration. We present the design guidelines of the cognitive architecture, its main functionalities, and we outline the cognitive process of the robot by showing how it learns to recognize objects in a human-robot interaction scenario inspired by social parenting. The robot actively explores the objects through manipulation, driven by a combination of social guidance and intrinsic motivation. Besides the robotics and engineering achievements, our experiments replicate some observations about the coupling of vision and manipulation in infants, particularly how they focus on the most informative objects. We discuss the further benefits of our architecture, particularly how it can be improved and used to ground concepts.