## 2 Related Work  
Over the recent years, advanced service robot systems are rapidly growing in domestic applications, capable of performing specific skills or functionalities, such as manipulation [17] or object recognition [18], or complex global tasks [13]. The emergence of many different solutions has led to the need for a more systematic evaluation of the performance of robotic systems [19] and is triggering initiatives to create benchmarks and standard experimental testbeds. Our approach to benchmarking robot solutions is based on the definition of two types of benchmarks that were originally introduced in a former project, RoCKIn [19; 20]:  
* Functionality Benchmarks (FBMs): evaluates the performance of hardware and software systems dedicated to a single and specific functionality, in the context of experiments focused on that functionality. Examples: Navigation FBM and Object Perception FBM;
* Task Benchmarks (TBMs): evaluate the performance of integrated robot systems executing complex tasks that need the interaction/composition of different functionalities. Examples: Welcoming Visitors TBM and Cleaning the house TBM. In our previous work, we have demonstrated a set of TBMs and FBMs in the context of Service Robot competitions and introduced FBMs such as Navigation, Object Perception and Speech Understanding [16]. In this paper, we will introduce three additional FBMs while developing and evaluating three key functionalities for a domestic service robot. Human-robot interaction (HRI) is one of the most important capabilities that a domestic service robot needs to hold, of which People perception is the first key element for having a successful HRI. People perception can be divided into several sub-problems: (1) detecting the presence of a person, (2) estimating the person's position and (3) recognizing the identity of the detected person. To detect a person, many different sensors and approaches are available today, ranging from simple laser-based leg detectors [21; 22] to detecting heat signatures through a thermal camera [23] or other computer vision tech niques [24]. Upon detection, the person's position in the world can be estimated using the depth component of an RGB-D camera, laser sensors [25] or using a stereo camera [26] or multi-view images. Finally, for person recognition or people re-identification, the state-of-the-art methods mostly rely on vision sensors and deep learning methods trained from RGB images or videos [27; 28] but also using other sensors such as depth cameras [29] or infrared cameras [30] to overcome visual limitations such as varying light conditions or changes in clothing, amongst others. In the context of HRI, in particular for domestic and consumer applications, the ability for a robot to effectively follow a human is significantly important. For example, to follow a child, the elderly or a visitor inside the house or to carry the bags of a person inside an airport or a retail store. The people-following problem is commonly addressed by (1) detecting and tracking a target person in the environment and (2) controlling the movement of the robot to maintain a desired distance with the target person. Most solutions for people following addressed in the literature rely on visual cues [31; 32], however, attempts using other type of sensors such as laser scanners or a combination of sensors are also available [33]. Pick and place manipulation is yet another important capability that is crucial for service and domestic service robots [34], for example, to set up a dining table or find and deliver objects in the house. Such capability is tightly linked to the object detection and recognition functionality and would require a robust image processing algorithm [35]. A pick and place routine usually consists of four main steps [36]: (1) choosing a grasp on the item, (2) planning a motion to grasp the item, (3) planning a motion to the placement location and (4) plan a motion to extract the gripper.