Care-O-bot<sup>®</sup> 3 - creating a product vision for service robot applications by integrating design and technology

Reiser, Ulrich; Connette, Christian; Fischer, Jan; Kubacki, Jens; Bubeck, Alexander; Weißhardt, Florian; Jacobs, Theo; Parlitz, Christopher; Hägele, Martin; Verl, Alexander

doi:10.1109/iros.2009.5354526

Cited by 109 publications

(66 citation statements)

References 10 publications

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“…[12,16]). One of the robots in the house is the (commercially available) Care-O-bot robot manufactured by Fraunhofer IPA [10]. It has been specifically developed as a mobile robotic assistant to support people in domestic environments, and is based on the concept of a robot butler.…”

Section: The Robot House and The Care-o-botmentioning

confidence: 99%

“…In this paper we consider the application of formal verification to the Care-O-bot R [10], an autonomous robotic assistant deployed in a domestic-type house at the University of Hertfordshire. Low-level robot actions such as movement, speech, light display, etc., are controlled by groups of high-level rules that together define particular behaviours; for example, a sequence of rules to let the user know that the fridge door is open.…”

Section: Introductionmentioning

confidence: 99%

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“The Fridge Door is Open”–Temporal Verification of a Robotic Assistant’s Behaviours

Dixon

Webster

Saunders

et al. 2014

Lecture Notes in Computer Science

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Abstract. Robotic assistants are being designed to help, or work with, humans in a variety of situations from assistance within domestic situations, through medical care, to industrial settings. Whilst robots have been used in industry for some time they are often limited in terms of their range of movement or range of tasks. A new generation of robotic assistants have more freedom to move, and are able to autonomously make decisions and decide between alternatives. For people to adopt such robots they will have to be shown to be both safe and trustworthy. In this paper we focus on formal verification of a set of rules that have been developed to control the Care-O-bot, a robotic assistant located in a typical domestic environment. In particular, we apply model-checking, an automated and exhaustive algorithmic technique, to check whether formal temporal properties are satisfied on all the possible behaviours of the system. We prove a number of properties relating to robot behaviours, their priority and interruptibility, helping to support both safety and trustworthiness of robot behaviours.

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Section: The Robot House and The Care-o-botmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“The Fridge Door is Open”–Temporal Verification of a Robotic Assistant’s Behaviours

Dixon

Webster

Saunders

et al. 2014

Lecture Notes in Computer Science

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show abstract

“…Precision Graph, our model Graph, Hu et al [3] Graph, linear [13] Uniform, our model Uniform, Hu et al [3] Uniform, linear [13] Avg. Recall Graph, our model Graph, Hu et al [3] Graph, linear [13] Uniform, our model Uniform, Hu et al [3] Uniform, linear [13] (c) Average Recall Avg. F−score Graph, our model Graph, Hu et al [3] Graph, linear [13] Uniform, our model Uniform, Hu et al [3] Uniform, linear [13] (d) Average F1-score Fig.…”

Section: A Experiments Setupmentioning

confidence: 99%

“…Based on the sensory data, human activities can be recognized and the robot can then provide proper services afterwards. The picture (left) shows after the elderly drinks water, Care-O-bot 3 [13] offers the elderly its tray for placing the cup.…”

Section: Introductionmentioning

confidence: 99%

Learning to Recognize Human Activities from Soft Labeled Data

Lou²,

Englebienne³

et al. 2014

Robotics: Science and Systems X

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Abstract-An activity recognition system is a very important component for assistant robots, but training such a system usually requires a large and correctly labeled dataset. Most of the previous works only allow training data to have a single activity label per segment, which is overly restrictive because the labels are not always certain. It is, therefore, desirable to allow multiple labels for ambiguous segments. In this paper, we introduce the method of soft labeling, which allows annotators to assign multiple, weighted, labels to data segments. This is useful in many situations, e.g. when the labels are uncertain, when part of the labels are missing, or when multiple annotators assign inconsistent labels. We treat the activity recognition task as a sequential labeling problem. Latent variables are embedded to exploit sub-level semantics for better estimation. We propose a novel method for learning model parameters from soft-labeled data in a max-margin framework. The model is evaluated on a challenging dataset (CAD-120), which is captured by a RGB-D sensor mounted on the robot. To simulate the uncertainty in data annotation, we randomly change the labels for transition segments. The results show significant improvement over the state-of-the-art approach.

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“…A humanoid robot could serve as as a household assistant [7], that e.g. supports elderly citizens in their homes [92]. Compared to tasks in an industrial scenarios, tasks in the household are not precisely defined and are often given as a natural language description like put the dishes on the table.…”

Section: Service Robotsmentioning

confidence: 99%

Knowledge Representations for Planning Manipulation Tasks

Zacharias

2012

Cognitive Systems Monographs

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A complex manipulation task for a robot has to be planned on different levels of abstraction before it can be executed on a real system. At a high-level of abstraction, a task planner generates a sequence of subtasks from a given abstract command, its pre-and postconditions and a current world model. To restrict the already huge search space of the task planner, aspects like geometric information are not contained in the world model at this level. The task planner then assigns the subtasks to planners on a lower level of abstraction like path planners or grasp planners. These planners rely on geometric information to compute their solutions. However, without considering geometric information the task planner cannot determine good parameters for these low-level planners. Therefore, the derived plans may not be valid for a given situation and may require backtracking. Knowledge representations can bridge the gap between these planning levels and thereby enable scene reasoning. The kinematic capabilities of a robot are one aspect the task planner has to be aware of. Therefore this thesis introduces a novel general representation of the kinematic capabilities of a robot arm. The versatile workspace is defined to describe in which orientations the end effector attached to a robot arm can reach a position. The capability map is a calculable representation of the versatile workspace that allows to determine how well regions of the workspace are reachable. It accurately represents the versatile workspace of arbitrary arm kinematics and enables autonomous taskspecific reasoning. Furthermore, its visualization scheme is intuitively comprehensible for the human. The versatile applicability of the capability map is shown by examples from several distinct application domains. In human-robot interaction, the capability map is used to objectively evaluate a bi-manual interface for tele-operation. The results can also supplement the design process for humanoid robot design. In low-level geometric planning, the capability map is used to reduce the search space and to parameterize path planners and grasp planners. Thus, in a manipulation task for a humanoid robot more human-like motion is planned while simultaneously the computation time is reduced. In high-level task reasoning, the capability map is used to evaluate how well a robot is suited for a task. In an example a humanoid robot has to perform a task involving 3D trajectories. Regions are extracted from the capability map that permit the task execution and the suitability of the robot is inferred.

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Care-O-bot^® 3 - creating a product vision for service robot applications by integrating design and technology

Cited by 109 publications

References 10 publications

“The Fridge Door is Open”–Temporal Verification of a Robotic Assistant’s Behaviours

“The Fridge Door is Open”–Temporal Verification of a Robotic Assistant’s Behaviours

Learning to Recognize Human Activities from Soft Labeled Data

Knowledge Representations for Planning Manipulation Tasks

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Care-O-bot® 3 - creating a product vision for service robot applications by integrating design and technology

Cited by 109 publications

References 10 publications

“The Fridge Door is Open”–Temporal Verification of a Robotic Assistant’s Behaviours

“The Fridge Door is Open”–Temporal Verification of a Robotic Assistant’s Behaviours

Learning to Recognize Human Activities from Soft Labeled Data

Knowledge Representations for Planning Manipulation Tasks

Contact Info

Product

Resources

About

Care-O-bot^® 3 - creating a product vision for service robot applications by integrating design and technology