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We reconsider the problem of reactive navigation in sphere worlds, i.e., the construction of a vector field over a compact, convex Euclidean subset punctured by Euclidean disks, whose flow brings a Euclidean disk robot from all but a zero measure set of ...

Motion planning is a fundamental tool in robotics, used to generate collision-free, smooth, trajectories, while satisfying task-dependent constraints. In this paper, we present a novel approach to motion planning using Gaussian processes. In contrast to ...

Cloud Robotics techniques based on Learning from Demonstrations suggest promising alternatives to manual programming of robots and autonomous vehicles. One challenge is that demonstrated trajectories may vary dramatically: it can be very difficult, if not ...

Recent progress in motion planning has made it possible to determine homotopy inequivalent trajectories between an initial and terminal configuration in a robot configuration space. Current approaches have however either assumed the knowledge of ...

We address the question of how resource-aware concepts can be utilized in motion planning algorithms. Resource-awareness facilitate better resource allocation on global system level, e.g. when a humanoid robot needs to distribute and schedule a wide ...

This paper presents a fast approximate multi-agent decision theoretic planning method extended from the well-known Markov Decision Process (MDP). Our objective is to plan motions for a team of homogeneous micro air vehicles (MAVs) toward a set of goals, ...

This paper presents a new approach for fast collision detection in high dimensional configuration spaces for Rapidly-exploring Random Trees (RRT) motion planning. The proposed method is based upon Gaussian Mixture Models (GMM) that are learned using an ...

This paper presents a new approach to C-space exploration and path planning for robotic manipulators using the structure named bur of free C-space. This structure builds upon the so-called bubble, which is a local volume of free C-space, easily computed ...

This paper presents a novel method for using volumetric information in the C-space for generating collision-free paths for robotic manipulators in the presence of obstacles. The method is based on the RRT paradigm, i.e., incrementally building trees in C-...

We present several modifications to the previously proposed MSPP algorithm that can speed-up its execution considerably. The MSPP algorithm leverages a multi-scale representation of the environment in n dimensions encoded in tree structure constructed by ...

We present hierarchical rejection sampling (HRS) to improve the efficiency of asymptotically optimal sampling-based planners for high-dimensional problems with differential constraints. Pruning nodes and rejecting samples that cannot improve the currently ...

This paper presents a highly sensitive dual mode tactile and proximity sensor for robotic applications that uses Carbon Microcoils (CMCs). The sensor consists of multiple electrode layers printed on a Flexible Printed Circuit Board (FPCB) and a dielectric ...

Nanoforce sensors using passive magnetic springs associated to a macroscopic seismic mass are known to be a possible alternative to force sensors based on elastic microstructures like Atomic Force Microscopes if the nanoforces that have to be measured are ...

This paper presents a new approach to the characterization of tactile array sensors that aims to reduce the computational time needed for convergence to obtain a useful estimator for normal and shear forces. This is achieved by breaking up the sensor ...

Tactile manipulation is the ability to control objects in real-time using the sense of touch. Here we examine tactile manipulation from the perspective of active touch with a biomimetic tactile sensor, which combines tactile perception with control of ...

The observation of robotic assembly tasks is required as feedback for decisions and adaption of the task execution on the current situation. A sequential Monte Carlo observation algorithm is proposed, which uses a fast and accurate collision detection ...

We have proposed a multimodal sensing with optical device and its application to robotic grasp control. The proposed device provides both tactile and proximity information with high spatial resolution on the same scale of coordinates, that is necessary ...

Robotic manipulation tasks often require the control of forces and torques exerted on external objects. This paper presents a machine learning approach for estimating forces when no force sensors are present on the robot platform. In the training phase, ...

Whole body tactile perception via tactile skins offers large benefits for robots in unstructured environments. To fully realize this benefit, tactile systems must support real-time data acquisition over a massive number of tactile sensor elements. We ...

Robotic manipulation in unstructured environments requires grasping a wide range of objects. Tactile sensing is presumed to provide essential information in this context, but there has been little work examining the tactile sensor signals produced during ...

Predicting grasp success is useful for avoiding failures in many robotic applications. Based on reasoning in wrench space, we address the question of how well analytic grasp success prediction works if tactile feedback is incorporated. Tactile information ...

Muscle activity monitoring or Electromyography (EMG) is useful in gait analysis, injury prevention, computer or robot interfaces and assisting patients with communication difficulties. However, EMG is typically invasive or obtrusive, expensive and ...

We present a method for estimating the contact event between sensor-free active subtracks, named flippers, of an articulated tracked vehicle (ATV) and the terrain surface. The main idea is to consider both the moving base link and unexpected collisions ...

We present a probabilistic model for joint representation of several sensory modalities and action parameters in a robotic grasping scenario. Our non-linear probabilistic latent variable model encodes relationships between grasp-related parameters, learns ...

Movement primitives are concise movement representations that can be learned from human demonstrations, support generalization to novel situations and modulate the speed of execution of movements. The speed modulation mechanisms proposed so far are ...

We present an approach to learn optimal navigation actions for assistance tasks in which the robot aims at efficiently reaching the final navigation goal of a human where service has to be provided. Always following the human at a close distance might ...

There is growing interest in applying machine learning to motion planning. Potential applications are predicting an initial seed for trajectory optimization, predicting an effective heuristic for search based planning, and even predicting a planning ...

One of the key problems in planning and control of redundant robots is the fast generation of controls when multiple tasks and constraints need to be satisfied. In the literature, this problem is classically solved by multi-task prioritized approaches, ...

We present a new Active Learning approach for classifying objects from streams of 3D point cloud data. The major problems here are the non-uniform occurrence of class instances and the unbalanced numbers of samples per class. We show that standard online ...

In programming by demonstration, generalization is necessary to apply demonstrated motions in novel situations. Many existing trajectory representations have poor generalization capabilities since they are built on trajectory coordinates that depend on ...