We investigate different approaches to integrating object recognition and planning in a tabletop manipulation domain with the set of objects used in the 2012 RoboCup@Work competition. Results of our preliminary experiments show that, with some approaches, close integration of perception and planning improves the quality of plans, as well as the computation times of feasible plans.
Flexible, general-purpose robots need to autonomously tailor their sensing and information processing to the task at hand. We pose this challenge as the task of planning under uncertainty. In our domain, the goal is to plan a sequence of visual operators to apply on regions of interest (ROIs) in images of a scene, so that a human and a robot can jointly manipulate and converse about objects on a tabletop. We pose visual processing management as an instance of probabilistic sequential decision making, and specifically as a Partially Observable Markov Decision Process (POMDP). The POMDP formulation uses models that quantitatively capture the unreliability of the operators and enable a robot to reason precisely about the trade-offs between plan reliability and plan execution time. Since planning in practical-sized POMDPs is intractable, we partially ameliorate this intractability for visual processing by defining a novel hierarchical POMDP based on the cognitive requirements of the corresponding planning task. We compare our hierarchical POMDP planning system (HiPPo) with a non-hierarchical POMDP formulation and the Continual Planning (CP) framework that handles uncertainty in a qualitative manner. We show empirically that HiPPo and CP outperform the naive application of all visual operators on all ROIs. The key result is that the POMDP methods produce more robust plans than CP or the naive visual processing. In summary, visual processing problems represent a challenging domain for planning techniques and our hierarchical POMDP-based approach for visual processing management opens up a promising new line of research. The authors are interested in a knowledge-based technique (called program supervision) for managing the reuse of a modular set of programs. The focus of the paper is to analyse which reuse problems program supervision can solve. First, they propose a general definition for program supervision, a knowledge representation model, and a reasoning model. They then analyse a program supervision solution for reuse in terms of the structure of the programs to re-use and in terms of the effort for building a program supervision knowledge base. The paper concludes with what program supervision can do for program reuse from the points of view of the code developers, the experts, and the end-users. This paper describes and discusses the K-COPMAN (Knowledge-enabled Cognitive Perception for Manipulation) system, which enables autonomous robots to generate symbolic representations of perceived objects and scenes and to infer answers to complex queries that require the combination of perception and knowledge processing. Using K-COPMAN, the robot can solve inference tasks such as identifying items that are likely to be missing on a breakfast table. To the programmer K-COPMAN, is presented as a logic programming system that can be queried just like a symbolic knowledge base. Internally, K-COPMAN is realized through a data structure framework together with a library of state-of-the-art perception mechanisms for mobile manipulation in human environments. Key features of K-COPMAN are that it can make a robot environment-aware and that it supports goal-directed as well as passive perceptual processing. K-COPMAN is fully integrated into an autonomous mobile manipulation robot and is realized within the open-source robot library ROS.  This article deals with the design of a system that automates the generation of image processing applications. Users describe tasks to perform on images and the system constructs a specific plan, which, after being executed, should yield the desired results. Our approach to this problem belongs to a more general category of systems for the supervision of a library of operators. The generation of an application is considered as the dynamic building of chains of image processing through the selection, parameter tuning and scheduling of existing operators. To develop such a system, we suggest to use a knowledge-rich resolution model and to integrate seven design rules. The Borg system has been developed following these prescriptions. It hinges on hierarchical, opportunistic and incremental planning by means of knowledge sources of the blackboard model, which enable to take into account the planning, evaluation and knowledge acquisition issues. This paper presents a new approach to the knowledge-based composition of processes for image interpretation and analysis. Its computer implementation in the VISIPLAN (Vision Planner) system provides a way of modeling the composition of image analysis processes within a unified, object-centered hierarchical planning framework. The approach has been tested and shown to be flexible in handling a reasonably broad class of multi-modality biomedical image analysis and interpretation problems. It provides a relatively general design or planning framework, within which problem specific image analysis and recognition processes can be generated more efficiently and effectively. In this way, generality is gained at the design and planning stages, even though the final implementation stage of interpretation processes is almost invariably problem- and domain-specific. >