We present the robot developed within the Hobbit project, a socially assistive service robot aiming at the challenge of enabling prolonged independent living of elderly people in their own homes. We present the second prototype (Hobbit PT2) in terms of hardware and functionality improvements following first user studies. Our main contribution lies within the description of all components developed within the Hobbit project, leading to autonomous operation of 371 days during field trials in Austria, Greece, and Sweden. In these field trials, we studied how 18 elderly users (aged 75 years and older) lived with the autonomously interacting service robot over multiple weeks. To the best of our knowledge, this is the first time a multifunctional, low-cost service robot equipped with a manipulator was studied and evaluated for several weeks under real-world conditions. We show that Hobbit’s adaptive approach towards the user increasingly eased the interaction between the users and Hobbit. We provide lessons learned regarding the need for adaptive behavior coordination, support during emergency situations, and clear communication of robotic actions and their consequences for fellow researchers who are developing an autonomous, low-cost service robot designed to interact with their users in domestic contexts. Our trials show the necessity to move out into actual user homes, as only there can we encounter issues such as misinterpretation of actions during unscripted human-robot interaction.

In this article, we present results obtained from field trials with the Hobbit robotic platform, an assistive, social service robot aiming at enabling prolonged independent living of older adults in their own homes. Our main contribution lies within the detailed results on perceived safety, usability, and acceptance from field trials with autonomous robots in real homes of older users. In these field trials, we studied how 16 older adults (75 plus) lived with autonomously interacting service robots over multiple weeks. Robots have been employed for periods of months previously in home environments for older people, and some have been tested with manipulation abilities, but this is the first time a study has tested a robot in private homes that provided the combination of manipulation abilities, autonomous navigation, and nonscheduled interaction for an extended period of time. This article aims to explore how older adults interact with such a robot in their private homes. Our results show that all users interacted with Hobbit daily, rated most functions as well working, and reported that they believe that Hobbit will be part of future elderly care. We show that Hobbit's adaptive behavior approach towards the user increasingly eased the interaction between the users and the robot. Our trials reveal the necessity to move into actual users' homes, as only there, we encounter real-world challenges and demonstrate issues such as misinterpretation of actions during non-scripted human-robot interaction. The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement No. 288146 (HOBBIT).

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Figure 1: (a) A learned joint regressor might fail to recover the pose of a hand due to ambiguities or lack of training data. (b) We make use of the inherent uncertainty of a regressor by enforcing it to generate multiple proposals. The crosses show the top three proposals for the proximal interphalangeal joint of the ring finger for which the corresponding ground truth position is drawn in green. The marker size of the proposals corresponds to degree of confidence. (c) A subsequent model-based optimisation procedure exploits these proposals to estimate the true pose.Traditionally, the task of hand pose estimation was approached mostly by model-based or data-driven schemes. Model-based approaches have been shown to perform well in a wide range of scenarios. However, they require initialisation and cannot recover easily from tracking failures that occur due to fast hand motions. Data-driven approaches, on the other hand, can quickly deliver a solution, but the results often suffer from lower accuracy or missing anatomical validity compared to those obtained from modelbased approaches. We propose to combine the merits of both schemes in a hybrid approach. This way, the method provides anatomically valid and accurate solutions without requiring manual initialisation or suffering from track losses. Main IdeaFor the task of 3D hand pose estimation, the substantial similarities between individual fingers and complex finger interactions cause ambiguities and uncertainties which are often disregarded by previous works. In contrast, we have the model-based step exploiting the inherent uncertainties of the data-driven part. First, a learned regressor is employed to deliver multiple initial hypotheses for the 3D position of each hand joint. These proposals approximate the distribution of joint positions under the learned model and thus capture the uncertainty of the model. Subsequently, the parameters of an anatomically valid hand pose are found by model-based optimisation which exploits the uncertainties captured by the proposal distributions. To do this, the optimisation is privy to internal information from the learned regressor. In this way failures of the regressor can be corrected during optimisation (see Fig. 1).Proposal Generation For the generation of an approximated proposal distribution we build upon a prominent approach for body pose estimation [2], which has also been previously adapted for hand pose estimation [5]. The approach relies on Random Forests [1] to infer a 3D distribution of likely hand joint locations. Using the discriminative Random Forest based method, inference of the individual joint proposals is completely independent from the other joints. While, in this way, the complex dependencies do not need to be modeled, the resulting proposals are not necessarily compatible with anatomical constraints.Optimisation In order to obtain a valid pose we employ a predefined model of a hand. Such a model can be specified by a number of parameters defining the global position and orientation of the hand as we...

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