This paper considers on a number of issues that arise when a trainable machine vision system learns directly from humans. We contrast this to the “normal” situation where machine learning (ML) techniques are applied to a “cleaned” data set which is considered to be perfectly labeled with complete accuracy. This paper is done within the context of a generic system for the visual surface inspection of manufactured parts; however, the issues treated are relevant not only to wider computer vision applications such as medical image screening but also to classification more generally. Many of the issues we consider arise from the nature of humans themselves: They will be not only internally inconsistent but also will often not be completely confident about their decisions, particularly if they are making decisions rapidly. People will also often differ systematically from each other in the decisions they make. Other issues may arise from the nature of the process, which may require the ML to have the capacity for real-time online adaptation in response to users' input. Because of this, it may be that the users cannot always provide input to a consistent level of detail. We describe how all of these issues may be tackled within a coherent methodology. By using a range of classifiers trained on data sets from a compact disc imprint production process, we present results which demonstrate that training methods designed to take proper consideration of these issues may actually lead to improved performance.

This paper presents a novel design of visual state estimation for an image-based tracking control system to estimate system state during visual tracking control process. The advantage of this design is that it can estimate the target status and target image velocity without using the knowledge of target’s 3D motion-model information. This advantage is helpful for real-time visual tracking controller design. In order to increase the robustness against random observation noise, a neural network based self-tuning algorithm is proposed using echo state network (ESN) technique. The visual state estimator is designed by combining a Kalman filter with the ESN-based self-tuning algorithm. The performance of this estimator design has been evaluated using computer simulation. Several interesting experiments on a mobile robot validate the proposed algorithms.

Many elderly and disabled people experience difficulties when maneuvering an electric wheelchair. In order to make wheelchair driving a safer and more comfortable experience, there has long been the claim to equip wheelchairs with some form of intelligent controller assisting in difficult or unsafe situations. It has been observed that every user presents different symptoms causing a specific driving pattern. Therefore, if the user is to be helped and not frustrated, his/her particular driving behavior should be taken into account when assisting him/her. In this paper we present a general user modeling technique for our Bayesian framework for plan recognition and shared wheelchair control. Plan recognition corresponds to estimating the plan a user has in mind. Assistive actions can then be taken based on the estimated user plan. A user modeling technique based on Gaussian processes has been selected, which can be adapted online to any type of driving style. The potential of Gaussian processes for user modeling is illustrated on a case study with a disabled patient suffering from spastic quadriplegia.

Although powered wheelchairs provide a well established solution for severely impaired persons they do not cover all needs regarding mobility of people with impairment. In the course of the EC funded research project MOVEMENT a novel approach for a highly adaptable and modular mobility enhancement system is targeted to cover additional user needs. A system consisting of a robotic platform and several dockable application modules is developed that additionally provides assistance for the driving process itself to the user or even takes over the complete driving autonomously. The project also includes development of new solutions for navigation of mobile robot systems including a "low-cost" sensor system as well as adaptable HMI components. This paper describes the concept and the first prototyping results.

The last years have witnessed a significant increase in the percentage of old and disabled people. Members of this population group very often require extensive help for performing daily tasks like moving around or grasping objects. Unfortunately, assistive technology is not always available to people needing it. For instance, steering a wheelchair can represent an extremely fatiguing or simply impossible task to many elderly or disabled users. Most of the existing assistance platforms try to help users without considering their specific needs. However, driving performance may vary considerably across users due to different pathologies or just due to temporary effects like fatigue. Therefore, we propose in this paper a user adapted shared control approach aimed at helping users in driving a power wheelchair. Adaption to the user is achieved by estimating the user's true intent out of potentially noisy steering signals before assisting him/her. The user's driving performance is explicitly modeled in order to recognize the user's intention or plan together with the uncertainty on it. Safe navigation is achieved by merging the potentially noisy input of the user with fine motion trajectories computed online by a 3D planner. Encouraging results on assisting a user who cannot steer to the left are reported on K.U.Leuven's intelligent wheelchair Sharioto.

Many elderly and disabled people today experience difficulties when manoeuvring an electric wheelchair. In order to help these people, several robotic assistance platforms have been devised in the past. In most cases, these platforms consist of separate assistance modes, and heuristic rules are used to automatically decide which assistance mode should be selected in each time step. As these decision rules are often hard-coded and do not take uncertainty regarding the user's intent into account, assistive actions may lead to confusion or even irritation if the user's actual plans do not correspond to the assistive system's behavior. In contrast to previous approaches, this paper presents a more user-centered approach for recognizing the intent of wheelchair drivers, which explicitly estimates the uncertainty on the user's intent. The paper shows the benefit of estimating this uncertainty using experimental results with our wheelchair platform Sharioto

This paper describes a novel adaptive filter approach to reduce the handicap a patient may experience when navigating an electric wheelchair. The filter automatically adapts to the specific handicap the patient has by training a connectionist structure that converts the joystick signal of the patient to the signal a reference user would give in the same context. Experimental results show that for various handicaps the filter improves the driving performance significantly

This paper presents an extension of current global dynamic window approaches to holonomic and nonholonomic mobile robots with an arbitrary cross-section. The algorithm proceeds in two stages. In order to account for an arbitrary robot footprint, the first stage takes the robot's orientation explicitly into account by constructing a navigation function in the (x, y, /spl theta/) configuration space. In a second stage, an admissible velocity is chosen from a window around the robot's current velocity, which contains all velocities that can be reached under the acceleration constraints. Fast computation over large areas is achieved by adopting multi-resolution (x, y) and (x, y, /spl theta/) planning. Several measures are taken to obtain safe and robust robot behaviour. Experimental results on our wheelchair test platform show the feasibility of the approach.

Vision sensors are attractive for autonomous robots because they are a rich source of environment information. The main challenge in using images for mobile robots is managing this wealth of information. A relatively recent approach is the use of fast wide baseline local features, which we developed and used in the novel approach to sparse visual path following described in this paper. These local features have the great advantage that they can be recognized even if the viewpoint differs significantly. This opens the door to a memory efficient description of a path by descriptors of sparse images. We propose a method for re-execution of these paths by a series of visual homing operations which yield a navigation method with unique properties: it is accurate, robust, fast, and without odometry error build-up.

This paper presents our approach to extend the niche of behavior-based robotics toward manipulation. We use results from neuroscience to derive some qualitative design rules for the mechanics of the manipulator, resulting in a next-generation manipulator, the "soft arm". By defining the basic behaviors of the manipulator as trajectory-producing behaviors (which is also biologically plausible), we have designed a first test case: writing on a board with a mobile manipulator. The soft arm has not yet been developed; therefore, we have emulated such a soft robot arm on an industrial robot.

This paper presents our approach to extending the niche of behavior-based robotics to manipulation. We use results from neuroscience to define the basic behaviors of the manipulator. Furthermore, we derive some qualitative design rules for the mechanics of the manipulator. With these principles, we have designed a first demo application: writing on a board with a mobile manipulator.

Many elderly and disabled people today experience difficulties when maneuvering an electric wheelchair. In order to help these people, several robotic assistance platforms have been devised in the past. These platforms' architectures usually consist of separate assistance modes that each realise a specific navigation behaviour, such as "avoid-obstacles", or "drive-through-door". In most cases, heuristic rules are used to decide automatically which assistance mode should be selected in each time step. These decision rules are often hard coded and therefore not very adaptable to different user's actual plans do not correspond to the assistive system's behaviour. Moreover, navigation algorithms are used that take the wheelchair's kinematic and dynamic constraints only approximately into account. Consequently, these robotic wheelchairs may and do fail in executing the very same maneuvers with which elderly and disabled people have problems. In contrast with previous approaches, this paper presents a user-centered architecture for shared wheelchair control. The framework continuously estimates the user's intention explicitly before trying to assist him or her. The actual navigation assistance is performed by a fine motion planner that takes the kinematic and dynamic constraints into account. The paper presents experimental results and an evaluation of the architecture.