IROS 2015 Papers

This paper describes a vacuum gripper that imitates the sucker of the octopus which is useful for the handling of industrial parts. It is difficult to use other sucker type vacuum grippers, but this gripper can adhere to rigid materials since the sucker is flexible. In the present work, we evaluated the effect of a liquid membrane on vacuum suction, since the industrial part often has a liquid membrane of, for example, oil and water. The success rate of grip was increased by wetting the surface, regardless of the object's shape and weight, since the liquid membrane filled the gap between the gripper and object. For the same reason, the maximum adsorption force was improved up to 1.8 times of 46 N.

One of the main barriers preventing widespread use of formal methods is the elicitation of formal specifications. Formal specifications facilitate the testing and verification process for safety critical robotic systems. However, handling the intricacies of formal languages is difficult and requires a high level of expertise in formal logics that many system developers do not have. In this work, we present a graphical tool designed for the development and visualization of formal specifications by people that do not have training in formal logic. The tool enables users to develop specifications using a graphical formalism which is then automatically translated to Metric Temporal Logic (MTL). In order to evaluate the effectiveness of our tool, we have also designed and conducted a usability study with cohorts from the academic student community and industry. Our results indicate that both groups were able to define formal requirements with high levels of accuracy. Finally, we present applications of our tool for defining specifications for operation of robotic surgery and autonomous quadcopter safe operation.

It is easy for human beings to discern whether an observed acoustic signal is a direct speech, reflected speech or noise through simple listening. Relying purely on acoustic cues is enough for human beings to discriminate between the different kinds of sound sources which is not straightforward for machines. A robot equipped with the current robot audition mechanism in most cases, will fail to differentiate a direct speech from the other sound sources because acoustic information alone is insufficient for effective discrimination. Robot audition is an important topic in speech-based human-robot communication. It enables the robot to associate the incoming speech signal to the user for an effective human-robot communication. In challenging environments, this task becomes difficult due to reflections of the direct speech signal and background noise sources. To counter this problem, a robot needs to have a minimum amount of prior information to discriminate the valid speech signal (direct speech) from the contaminants (i.e., speech reflections and background noise sources). Failure to do so would lead to false speech-to-speaker association in robot audition and will gravely impact human-robot communication experience. In this paper we propose to using visual cues to augment the traditional robot audition which relies solely on acoustic information. The proposed method significantly improves accuracy of speech-to-speaker association and machine understanding performance in real environment situation. Experimental results show that our expanded system is robust in discriminating direct speech from speech reflections and background noise sources.

Computing the optimal geometric structure of manipulators is one of the most intricate problems in contemporary robot kinematics. Industrial robotic manipulators are designed and built to perform certain predetermined tasks. It is therefore important to incorporate such task requirements during the design and synthesis of the robotic manipulators. Such task requirements and/or performance constraints can be specified in terms of the required end-effector positions, orientations along the task trajectory. In this work, we define, develop and test a methodology that can generate optimal manipulator geometric structures based on the task requirements. Another objective of this work is to guarantee task performance under user defined joint constraints. Using this methodology, task-specific optimal manipulator structures can be generated that guarantee task performance under a set of operating constraints.

In this work we address the problem of finding replacements of missing objects that are needed for the execution of human-like manipulation tasks. This is a usual problem that is easily solved by humans provided their natural knowledge to find object substitutions: using a knife as a screwdriver or a book as a cutting board. On the other hand, in robotic applications, objects required in the task should be included in advance in the problem definition. If any of these objects is missing from the scenario, the conventional approach is to manually redefine the problem according to the available objects in the scene. In this work we propose an automatic way of finding object substitutions for the execution of manipulation tasks. The approach uses a logic-based planner to generate a plan from a prototypical problem definition and searches for replacements in the scene when some of the objects involved in the plan are missing. This is done by means of a repository of objects and attributes with roles, which is used to identify the affordances of the unknown objects in the scene. Planning actions are grounded using a novel approach that encodes the semantic structure of manipulation actions. The system was evaluated in a KUKA arm platform for the task of preparing a salad with successful results.

Infant body posture assessment is very important for monitoring the development of motor skills. The currently utilized sensory-supported measurement systems are often subject to shortcomings, such as system complexity or inaccuracy. This paper presents a novel approach integrating a combination of a pressure mattress along with inertial and magnetic measurement units on infant trunk and arms to diminish the effects of such drawbacks. The proposed multi-sensor system is validated by comparison of kinematic parameters, such as head movement, arm workspace area, and spectral arc length of hand velocity data to results of a referential optoelectronic measurement system. Integration of sensory data fusion methods provides high system accuracy and reliability. Acquired results confirm that the proposed system can be used as an adequate substitution to expensive optoelectronic measurement systems, as the arm kinematic estimation errors are in the range of 2 cm. Also presented are the results of simplified system versions with only 1 IMU per arm. Such approaches however result in lower system complexity, but also lower accuracy. Nevertheless results imply that the arm posture and workspace assessment is still reliable enough for frequent practical use. By taking into account all the evaluation results, the proposed measurement system could importantly contribute to the field of sensory-supported infant body posture assessment.

Place recognition is the frond-end of Simultaneous Localization and Mapping (SLAM). Topological representations depend on good association of vertices, which ultimately depends on the front-end. In this paper, we consider a robot lost in an unknown environment trying to construct a topological map to localize itself using a laser range finder and odometry information. The algorithm makes use of an efficient observation model based on kernel density estimates (KDEs) to detect loops. The observation model separates the map into regions denominated words, classified based on the density of free space, number of observations and segment orientation. Loop closing results from the matching of sequences of N consecutive words (n-grams). The proposed approach is orders of magnitude faster than a sequence of Iterative Closest Point (ICP) matches. The method is evaluated varying input parameters in real and simulated scenarios.

In this paper, a method to extract the fingertip forces of the index and middle fingers from surface electromyography (sEMG) signals is studied by adopting the known common spatial pattern (CSP) approach. For unsupervised estimation of fingertip forces in real-time, CSP filtering is shown to be a notably effective method compared with known approaches for handling sEMG signals. The results of the proposed method are comparable to those of supervised estimations, such as linear regression and artificial neural network. The efficacy of the proposed method is validated by experiments.

Snake robots are highly redundant robots that could potentially be used as robotic arms, allowing them to interact with the environment. However they do not have a fixed base, making them difficult to control accurately. In this paper, a complete modeling approach and an optimization algorithm are presented that show the torques that allow a snake robot to track a trajectory while keeping one of its links from slipping. To do this, it is essential to consider the static friction between the ground and a snake robot. The forces of static friction are modeled as bounded constraints, in a similar fashion as the constraint forces studied in grasping with robotic hands. Simulations are carried out to analyze the problem.

Rehabilitative follow-up care is important for stroke patients to regain their motor and cognitive skills. We aim to develop a robotic rehabilitation assistant for walking exercises in late stages of rehabilitation. The robotic rehab assistant is to accompany inpatients during their self-training, practicing both mobility and spatial orientation skills. To hold contact to the patient, even after temporally full occlusions, robust user re-identification is essential. Therefore, we implemented a person re-identification module that continuously re-identifies the patient, using only few amount of the robot's processing resources. It is robust to varying illumination and occlusions. State-of-the-art performance is confirmed on a standard benchmark dataset, as well as on a recorded scenario-specific dataset. Additionally, the benefit of using a visual re-identification component is verified by live-tests with the robot in a stroke rehab clinic.

Assistive robots can improve the well-being of disabled or frail human users by reducing the burden that activities of daily living impose on them. To enable personalised assistance, such robots benefit from building a user-specific model, so that the assistance is customised to the particular set of user abilities. In this paper, we present an end-to-end approach for home-environment assistive humanoid robots to provide personalised assistance through a dressing application for users who have upper-body movement limitations. We use randomised decision forests to estimate the upper-body pose of users captured by a top-view depth camera, and model the movement space of upper-body joints using Gaussian mixture models. The movement space of each upper-body joint consists of regions with different reaching capabilities. We propose a method which is based on real-time upper-body pose and user models to plan robot motions for assistive dressing. We validate each part of our approach and test the whole system, allowing a Baxter humanoid robot to assist human to wear a sleeveless jacket.

This paper presents an algorithm for camera calibration. The algorithm, inspired by work in the field of developmental robotic on the concept of space in naive agents, is particularly suitable for robotic applications: it is completely unsupervised, and it does not assume any model of the camera, making it applicable to many kinds of optical devices. Testing of the algorithm, in a simulated environment, shows very good results, outperforming the main unsupervised and model-free calibration algorithm in the literature.

We present a novel method for clustering segmented dynamic parts of indoor RGB-D scenes across repeated observations by performing an analysis of their spatial-temporal distributions. We segment areas of interest in the scene using scene differencing for change detection. We extend the Meta-Room method and evaluate the performance on a complex dataset acquired autonomously by a mobile robot over a period of 30 days. We use an initial clustering method to group the segmented parts based on appearance and shape, and we further combine the clusters we obtain by analyzing their spatial-temporal behaviors. We show that using the spatial-temporal information further increases the matching accuracy.

In this paper we present a methodology for discriminating between different objects using only a single force closure grasp with an underactuated robot hand equipped with force sensors. The technique leverages the benefits of simple, adaptive robot grippers (which can grasp successfully without prior knowledge of the hand or the object model), with an advanced machine learning technique (Random Forests). Unlike prior work in literature, the proposed methodology does not require object exploration, release or re-grasping and works for arbitrary object positions and orientations within the reach of a grasp. A two-fingered compliant, underactuated robot hand is controlled in an open-loop fashion to grasp objects with various shapes, sizes and stiffness. The Random Forests classification technique is used in order to discriminate between different object classes. The feature space used consists only of the actuator positions and the force sensor measurements at two specific time instances of the grasping process. A feature variables importance calculation procedure facilitates the identification of the most crucial features, concluding to the minimum number of sensors required. The efficiency of the proposed method is validated with two experimental paradigms involving two sets of fabricated model objects with different shapes, sizes and stiffness and a set of everyday life objects.

This work proposes an approach to manipulate unknown objects based on tactile information. The manipulation can have three goals: the optimization of the hand configuration, the optimization of the grasp quality and the optimization of the object configuration. Three different motion strategies are introduced in order to move the fingers trying to deal with each of the three goals. The strategies can be applied independently or combined in a sequential way. The feasibility of the motion strategies was proven in real experimentation using the Schunk Dexterous Hand SDH2.

Our recently developed Reverse Priority (RP) redundancy resolution method is extended here to the presence of unilateral constraints. The RP method computes the solution to a stack of prioritized tasks starting from the lowest priority one, and adding iteratively the contributions of higher priority tasks. In this framework, unilateral constraints can be added efficiently, while guaranteeing also continuity of joint velocity commands. Since unilateral (hard) constraints are typically placed at the highest priority levels, their treatment within the RP method leads to the least possible modification of the solution computed so far, when analyzing the need to activate or not these constraints. The effectiveness of the approach is shown by simulations on a planar 6R robot and on a humanoid robot, as well as experiments on a KUKA LWR manipulator.

We propose a scheme that uses the signal strength received from commercial RF sensors to determine the position of a sensor underwater. The scheme can give accurate position information and has fast update rate, but is limited to short sensor range with the signal identification problem. The infrastructure-based localization and a method that assigns frequency bands to RF sensors can be a solution to overcome this range limitation without loss of update rate or position accuracy. The RF sensor modules are used as ANs, and are easily identified by their channel assignments. To verify the proposed localization scheme, we organized an experimental environment and performed repetitive experiments to track the positions of mobile nodes and stationary nodes in 2D environments. The experimental results showed reliable localization accuracy with fast sampling rate and demonstrates the feasibility of localization that exploits EM wave attenuation.

Humans can walk stably and adaptively in the presence of various environmental changes. Their bodies have very complex musculoskeletal structures that contribute to their walking stability and adaptability. In this paper, we focus on the gluteus medius, one of muscles contributing to the support of the pelvis. The gluteus medius supports the pelvis when the leg is in a standing position, but it should not inhibit its smooth swing motion. This mechanism is assumed to be realized by the musculoskeletal structure. This paper is devoted to explaining the mechanism by reproducing it using artificial pneumatic muscles. We developed a musculoskeletal robot with a gluteus medius, and showed that the mechanism is well-designed for realizing two seemingly contradictory functions: being stiff to support the pelvis while standing and being smooth to allow swinging. This finding can be utilized for adaptive walking of musculoskeletal humanoid robots.

In this paper, we propose a novel robot hand called uGRIPP (Underactuated Gripper for Power and Precision Grasp) which has underactuated fingers for dual-arm manipulation tasks. A newly designed underactuated linkage enables the robot hand to achieve not only an adaptive grasp and a pinch grasp, but also manipulation by a palm of the hand. We introduce performance of the proposed linkage by a static analysis. A prototype of the robot hand which has the proposed linkage as fingers was developed. We conducted handling experiments by using the robot hand.