The increasing number of robots in home environments leads to an emerging coexistence between humans and robots. Robots undertake common tasks and support the residents in their everyday life. People appreciate the presence of robots in their environment as long as they keep the control over them. One important aspect is the control of a robot's workspace. Therefore, we introduce virtual borders to precisely and flexibly define the workspace of mobile robots. First, we propose a novel framework that allows a person to interactively restrict a mobile robot's workspace. To show the validity of this framework, a concrete implementation based on visual markers is implemented. Afterwards, the mobile robot is capable of performing its tasks while respecting the new virtual borders. The approach is accurate, flexible and less time consuming than explicit robot programming. Hence, even non-experts are able to teach virtual borders to their robots which is especially interesting in domains like vacuuming or service robots in home environments.
@cite_13: We present a comprehensive survey of robot Learning from Demonstration (LfD), a technique that develops policies from example state to action mappings. We introduce the LfD design choices in terms of demonstrator, problem space, policy derivation and performance, and contribute the foundations for a structure in which to categorize LfD research. Specifically, we analyze and categorize the multiple ways in which examples are gathered, ranging from teleoperation to imitation, as well as the various techniques for policy derivation, including matching functions, dynamics models and plans. To conclude we discuss LfD limitations and related promising areas for future research.
@cite_18: In this paper, an intuitive interface for collaborative tasks involving a human and a standard industrial robot is presented. The target for this interface is a worker who is experienced in manufacturing processes but has no experience in conventional industrial robot programming. Physical Human-Robot Interaction (pHRI) and interactive GUI control using hand gestures offered by this interface allows this novice user to instruct industrial robots with ease. This interface combines state of the art perception capabilities with first order logic reasoning to generate semantic description of the process plan. This semantic representation creates the possibility of including human and robot tasks in the same plan and also reduces the complexity of problem analysis by allowing process planning at semantic level, thereby isolating the problem description and analysis from the execution and scenario-specific parameters.
@cite_26: We present an approach to control a 6-degree-of-freedom (DOF) manipulator using an uncalibrated visual servoing (VS) approach that addresses the challenges of choosing proper image features for target objects and designing a VS controller to enhance the tracking performance. The main contribution of this paper is the definition of a new virtual visual space (image space). A novel stereo camera model employing virtual orthogonal cameras is used to map 6-D poses from Cartesian space to this virtual visual space . Each component of the 6-D pose vector defined in this virtual visual space is linearly independent, leading to a full-rank @math image Jacobian matrix , which allows avoiding classical problems, such as image space singularities and local minima. Furthermore, the control for rotational and translational motion of robot is decoupled due to the diagonal image Jacobian. Finally, simulation results with an eye-to-hand robotic system confirm the improvement in controller stability and motion performance with respect to conventional VS approaches. Experimental results on a 6-DOF industrial robot are provided to illustrate the effectiveness of the proposed method and the feasibility of using this method in practical scenarios.
@cite_21: This article presents a novel robotic partner which children can teach handwriting. The system relies on the learning by teaching paradigm to build an interaction, so as to stimulate meta-cognition, empathy and increased self-esteem in the child user. We hypothesise that use of a humanoid robot in such a system could not just engage an unmotivated student, but could also present the opportunity for children to experience physically-induced benefits encountered during human-led handwriting interventions, such as motor mimicry. By leveraging simulated handwriting on a synchronised tablet display, a NAO humanoid robot with limited fine motor capabilities has been configured as a suitably embodied handwriting partner. Statistical shape models derived from principal component analysis of a dataset of adult-written letter trajectories allow the robot to draw purposefully deformed letters. By incorporating feedback from user demonstrations, the system is then able to learn the optimal parameters for the appropriate shape models. Preliminary in situ studies have been conducted with primary school classes to obtain insight into children's use of the novel system. Children aged 6-8 successfully engaged with the robot and improved its writing to a level which they were satisfied with. The validation of the interaction represents a significant step towards an innovative use for robotics which addresses a widespread and socially meaningful challenge in education.
@cite_16: Rough terrain autonomous navigation continues to pose a challenge to the robotics community. Robust navigation by a mobile robot depends not only on the individual performance of perception and planning systems, but on how well these systems are coupled. When traversing complex unstructured terrain, this coupling (in the form of a cost function) has a large impact on robot behavior and performance, necessitating a robust design. This paper explores the application of Learning from Demonstration to this task for the Crusher autonomous navigation platform. Using expert examples of desired navigation behavior, mappings from both online and offline perceptual data to planning costs are learned. Challenges in adapting existing techniques to complex online planning systems and imperfect demonstration are addressed, along with additional practical considerations. The benefits to autonomous performance of this approach are examined, as well as the decrease in necessary designer effort. Experimental results are presented from autonomous traverses through complex natural environments.
@cite_10: This paper presents a hierarchical, statistical topic model for representing the grasp preshapes of a set of objects. Observations provided by teleoperation are clustered into latent affordances shared among all objects. Each affordance defines a joint distribution over position and orientation of the hand relative to the object and conditioned on visual appearance. The parameters of the model are learned using a Gibbs sampling method. After training, the model can be used to compute grasp preshapes for a novel object based on its visual appearance. The model is evaluated experimentally on a set of objects for its ability to generate grasp preshapes that lead to successful grasps, and compared to a baseline approach.
@cite_20: Increasingly, personalised robots - robots especially designed and programmed for an individual's needs and preferences - are being used to support humans in their daily lives, most notably in the area of service robotics. Arguably, the closer the robot is programmed to the individual's needs, the more useful it is, and we believe that giving people the opportunity to program their own robots, rather than programming robots for them, will push robotics research one step further in the personalised robotics field. However, traditional robot programming techniques require specialised technical skills from different disciplines and it is not reasonable to expect end-users to have these skills. In this paper, we therefore present a new method of obtaining robot control code - programming by demonstration through system identification - which algorithmically and automatically transfers human behaviours into robot control code, using transparent, analysable mathematical functions. Besides providing a simple means of generating perception-action mappings, they have the additional advantage that can also be used to form hypotheses and theoretical analysis of robot behaviour. We demonstrate the viability of this approach by teaching a Scitos G5 mobile robot to achieve wall following and corridor passing behaviours.
To allow non-experts the teaching of virtual borders, a user-friendly approach has to be chosen. Along classical robot programming, LfD (Learning from Demonstration) is a technique that deals with teaching robots new skills by (human) demonstrations. @cite_13 present a comprehensive survey of robot LfD and explain the foundations of the technique. The authors categorize different approaches according to the mapping between the teacher and the learner. Teleoperation is used to learn the grasping of objects @cite_10 or to perform tasks demonstrated by kinesthetic teaching @cite_18 . If the learner uses its own sensors, but the teacher does not directly control the learner's platform, it is referred to as shadowing. @cite_20 program a robot's movement by demonstration through system identification, and autonomous navigation in complex unstructured terrain is learned in the work of @cite_16 . Other recent applications range from learning handwriting by demonstration @cite_21 to controlling a robot arm manipulator @cite_26 . The last two applications also use visual markers in their teaching process.