Decoding the user intention from non-invasive EEG signals is a challenging problem. In this paper, we study the feasibility of predicting the goal for controlling the robot arm in self-paced reaching movements, i.e., ...
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ISBN:
(纸本)9781424479276
Decoding the user intention from non-invasive EEG signals is a challenging problem. In this paper, we study the feasibility of predicting the goal for controlling the robot arm in self-paced reaching movements, i.e., spontaneous movements that do not require an external cue. Our proposed system continuously estimates the goal throughout a trial starting before the movement onset by online classification and generates optimal trajectories for driving the robot arm to the estimated goal. Experiments using EEG signals of one healthy subject (right arm) yield smooth reaching movements of the simulated 7 degrees of freedom KUKA robot arm in planar center-out reaching task with approximately 80% accuracy of reaching the actual goal.
This paper presents a method by which a robot can learn through observation to perform a collaborative manipulation task, namely lifting an object. The task is first demonstrated by a user controlling the robot's ...
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This paper presents a method by which a robot can learn through observation to perform a collaborative manipulation task, namely lifting an object. The task is first demonstrated by a user controlling the robot's hand via a haptic interface. learning extracts statistical redundancies in the examples provided during training by using Gaussian Mixture Regression and Hidden Markov Model. Haptic communication reflects more than pure dynamic information on the task, and includes communication patterns, which result from the two users constantly adapting their hand motion to coordinate in time and space their respective motions. We show that the proposed statistical model can efficiently encapsulate typical communication patterns across different dyads of users, that are stereotypical of collaborative behaviours between humans and robots. The proposed learning approach is generative and can be used to drive the robot's retrieval of the task by ensuring a faithful reproduction of the overall dynamics of the task, namely by reproducing the force patterns for both lift the object and adapt to the human user's hand motion. This work shows the potential that teleoperation holds for transmitting both dynamic and communicative information on the task, which classical methods for programming by demonstration have traditionally overlooked.
While most approaches individually exploit unstructured data from the biomedical literature or structured data from biomedical knowl- edge graphs, their union can better exploit the advantages of such ap- proaches, ul...
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We present algorithms for inferring the cost function and reference trajectory from human demonstrations of hand-writing tasks. These two key elements are then used, through optimal control, to generate an impedance-b...
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ISBN:
(纸本)9781479971756
We present algorithms for inferring the cost function and reference trajectory from human demonstrations of hand-writing tasks. These two key elements are then used, through optimal control, to generate an impedance-based controller for a robotic hand. The key novelty lies in the flexibility of the feature design in the composition of the cost function, in contrast to the traditional approaches that consider linearly combined features. Cross-entropy-based methods form the core of our learning technique, resulting in sample-based stochastic algorithms for task encoding and decoding. The algorithms are validated using an anthropomorphic robot hand. We assess that the correct compliance is well encapsulated by subjecting the robot to perturbations during task reproduction.
A stretchable tactile sensor skin has been demonstrated on the dorsal side of a robotic hand for the first time. The sensors can detect normal pressures on the same scale as human skin but also in excess of 250 kPa an...
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A stretchable tactile sensor skin has been demonstrated on the dorsal side of a robotic hand for the first time. The sensors can detect normal pressures on the same scale as human skin but also in excess of 250 kPa and withstand strains in excess of 15%. Using tactile information from the sensors mounted on a glove worn by a humanoid robot's hand, obstacle detection and surface reconstruction tasks were successfully completed in order to demonstrate the performance of the sensors under applied strains and pressure.
This paper proposes an EMG based learning approach for estimating the displacement along the 2-axes (abduction/adduction and flexion/extension) of the human wrist in real-time. The algorithm extracts features from the...
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ISBN:
(纸本)9781424492695
This paper proposes an EMG based learning approach for estimating the displacement along the 2-axes (abduction/adduction and flexion/extension) of the human wrist in real-time. The algorithm extracts features from the EMG electrodes on the upper and forearm and uses Support Vector Regression to estimate the intended displacement of the wrist. Using data recorded with the arm outstretched in various locations in space, we train the algorithm so as to allow robust prediction even when the subject moves his/her arm across several positions in space. The proposed approach was tested on five healthy subjects and showed that a R index of 63.6% is obtained for generalization across different arm positions and wrist joint angles.
We focus on generating consistent reconstructions of indoor spaces from a freely moving handheld RGB-D sensor, with the aim of creating virtual models that can be used for measuring and remodeling. We propose a novel ...
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ISBN:
(纸本)9781479969357
We focus on generating consistent reconstructions of indoor spaces from a freely moving handheld RGB-D sensor, with the aim of creating virtual models that can be used for measuring and remodeling. We propose a novel 6D RGB-D odometry approach that finds the relative camera pose between consecutive RGB-D frames by keypoint extraction and feature matching both on the RGB and depth image planes. Furthermore, we feed the estimated pose to the highly accurate KinectFusion algorithm, which uses a fast ICP (Iterative-Closest-Point) to fine-tune the frame-to-frame relative pose and fuse the Depth data into a global implicit surface. We evaluate our method on a publicly available RGB-D SLAM benchmark dataset by Sturm et al. The experimental results show that our proposed reconstruction method solely based on visual odometry and KinectFusion outperforms the state-of-the-art RGB-D SLAM system accuracy. Our algorithm outputs a ready-to-use polygon mesh (highly suitable for creating 3D virtual worlds) without any post-processing steps.
Over the years, robots have been developed to help humans in their everyday life, from preparing food, to autism therapy [2]. To accomplish their tasks, in addition to their engineered skills, today's robots are n...
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Over the years, robots have been developed to help humans in their everyday life, from preparing food, to autism therapy [2]. To accomplish their tasks, in addition to their engineered skills, today's robots are now learning from observing humans, from interacting with them [1]. Therefore, one may expect that one day, robots may develop a form of consciousness, and a desire for freedom. Hopefully, this desire will come with a wish for robots, to become an integral part of our human society. Until we can test this hypothesis, we present a fictional adventure of our robot friends: During an official human-robot interaction challenge, Keepon [2] and Chief Cook (a.k.a. Hoap-3) [1] decided to escape their original duties and joined their forces to drive humans into an entertaining and interactive activity that they often forget to practice: Dancing. Indeed, is there any better way for robots to establish a solid communication channel with humans, so that the traditional master-slave relation may turn into friendship?
Efficient and accurate planning of fingertip grasps is essential for dexterous in-hand manipulation. In this work, we present a system for fingertip grasp planning that incrementally learns a heuristic for hand reacha...
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ISBN:
(纸本)9781467380270
Efficient and accurate planning of fingertip grasps is essential for dexterous in-hand manipulation. In this work, we present a system for fingertip grasp planning that incrementally learns a heuristic for hand reachability and multi-fingered inverse kinematics. The system consists of an online execution module and an offline optimization module. During execution the system plans and executes fingertip grasps using Canny's grasp quality metric and a learned random forest based hand reachability heuristic. In the offline module, this heuristic is improved based on a grasping manifold that is incrementally learned from the experiences collected during execution. The system is evaluated both in simulation and on a Schunk-SDH dexterous hand mounted on a KUKA-KR5 arm. We show that, as the grasping manifold is adapted to the system's experiences, the heuristic becomes more accurate, which results in an improved performance of the execution module. The improvement is not only observed for experienced objects, but also for previously unknown objects of similar sizes.
Data-intensive flow computing allows efficient processing of large volumes of data otherwise unapproachable. This paper introduces a new semantic-driven data-intensive flow infrastructure which: (1) provides a robust ...
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