The problem studied in this paper is a mobile robot that autonomously navigates in a domestic environment, builds a map as it moves along and localizes its position in it. In addition, the robot detects predefined objects, estimates their position in the environment and integrates this with the localization module to automatically put the objects in the generated map. Thus, we demonstrate one of the possible strategies for the integration of spatial and semantic knowledge in a service robot scenario where a simultaneous localization and mapping (SLAM) and object detection/recognition system work in synergy to provide a richer representation of the environment than it would be possible with either of the methods alone.Most SLAM systems build maps that are only used for localizing the robot. Such maps are typically based on grids or different types of features such as point and lines. The novelty is the augmentation of this process with an object recognition system that detects objects in the environment and puts them in the map generated by the SLAM system. The metric map is also split into topological entities corresponding to rooms. In this way the user can command the robot to retrieve a certain object from a certain room. We present the results of map building and an extensive evaluation of the object detection algorithm performed in an indoor setting.

In this paper, we deal with the problem of learning by demonstration, task level learning and planning for robotic applications that involve object manipulation. Preprogramming robots for execution of complex domestic tasks such as setting a dinner table is of little use, since the same order of subtasks may not be conceivable in the run time due to the changed state of the world. In our approach, we aim to learn the goal of the task and use a task planner to reach the goal given different initial states of the world. For some tasks, there are underlying constraints that must be fulfille, and knowing just the final goal is not sufficient. We propose two techniques for constraint identification. In the first case, the teacher can directly instruct the system about the underlying constraints. In the second case, the constraints are identified by the robot itself based on multiple observations. The constraints are then considered in the planning phase, allowing the task to be executed without violating any of them. We evaluate our work on a real robot performing pick-and-place tasks.

The demand for flexible and re-programmable robots has increased the need for programming by demonstration systems. In this paper, grasp recognition is considered in a programming by demonstration framework. Three methods for grasp recognition are presented and evaluated. The first method uses Hidden Markov Models to model the hand posture sequence during the grasp sequence, while the second method relies on the hand trajectory and hand rotation. The third method is a hybrid method, in which both the first two methods are active in parallel. The particular contribution is that all methods rely on the grasp sequence and not just the final posture of the hand. This facilitates grasp recognition before the grasp is completed. Also, by analyzing the entire sequence and not just the final grasp, the decision is based on more information and increased robustness of the overall system is achieved.The experimental results show that both arm trajectory and final hand posture provide important information for grasp classification. By combining them, the recognition rate of the overall system is increased.

Abstract-In this paper, we address the problem of automatic grasp generation for robotic hands where experience and shape primitives are used in synergy so to provide a basis not only for grasp generation but also for a grasp evaluation process when the exact pose of the object is not available. One of the main challenges in automatic grasping is the choice of the object approach vector, which is dependent both on the object shape and pose as well as the grasp type. Using the proposed method, the approach vector is chosen not only based on the sensory input but also on experience that some approach vectors will provide useful tactile information that finally results in stable grasps. A methodology for developing and evaluating grasp controllers is presented where the focus lies on obtaining stable grasps under imperfect vision. The method is used in a teleoperation or a Programming by Demonstration setting where a human demonstrates to a robot how to grasp an object. The system first recognizes the object and grasp type which can then be used by the robot to perform the same action using a mapped version of the human grasping posture.

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