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检索条件"机构=Learning Algorithms & Systems Laboratory"

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Rewards-Driven Control of Robot Arm by Decoding EEG Signals

Rewards-Driven Control of Robot Arm by Decoding EEG Signals

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Ajay Kumar Tanwani Jose del. R. Millan Aude Billard Learning Algorithms and Systems Laboratory (LASA) Ecole Polytechnique Federale de Lausanne (EPFL) Defitech Chair on Non-Invasive Brain-Machine Interface (CNBI) Center for Neuropros-thetics in EPFL

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.

关键词： Electroencephalography Robots Decoding Trajectory Accuracy Correlation Training

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learning collaborative manipulation tasks by demonstration using a haptic interface

Learning collaborative manipulation tasks by demonstration u...

引用

International Conference on Advanced Robotics (ICAR)

作者： Sylvain Calinon Paul Evrard Elena Gribovskaya Aude Billard Abderrahmane Kheddar Learning Algorithms and Systems Laboratory (LASA) Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland UMI3218/CRT CNRS-AIST JRL Tsukuba Japan

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.

关键词： Collaboration Haptic interfaces Robot kinematics Hidden Markov models Humans Orbital robotics Dynamic programming Robot control Communication system control Data mining

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ProtSTonKGs: A Sophisticated Transformer Trained on Protein Sequences, Text, and Knowledge Graphs 13

ProtSTonKGs: A Sophisticated Transformer Trained on Protein ...

引用

13th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences, SWAT4HCLS 2022

作者： Balabin, Helena Hoyt, Charles Tapley Gyori, Benjamin M. Bachman, John Kodamullil, Alpha Tom Hofmann-Apitius, Martin Domingo-Fernández, Daniel Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing Sankt Augustin53757 Germany Bonn-Rhein-Sieg University of Applied Sciences Sankt Augustin53757 Germany Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States Fraunhofer Center for Machine Learning Germany Enveda Biosciences BoulderCO80301 United States

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, ultimately improving representations of biology. Using mul- timodal transformers for such purposes can improve performance on context dependent classification tasks, as demonstrated by our previ- ous model, the Sophisticated Transformer Trained on Biomedical Text and Knowledge Graphs (STonKGs). In this work, we introduce Prot- STonKGs, a transformer aimed at learning all-encompassing representa- tions of protein-protein interactions. ProtSTonKGs presents an extension to our previous work by adding textual protein descriptions and amino acid sequences (i.e., structural information) to the text- and knowledge graph-based input sequence used in STonKGs. We benchmark Prot- STonKGs against STonKGs, resulting in improved F1 scores by up to 0.066 (i.e., from 0.204 to 0.270) in several tasks such as predicting protein interactions in several contexts. Our work demonstrates how multimodal transformers can be used to integrate heterogeneous sources of infor- mation, paving the foundation for future approaches that use multiple modalities for biomedical applications. Copyright © 2022 for this paper by its authors.

关键词： Natural language processing systems

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learning cost function and trajectory for robotic writing motion

Learning cost function and trajectory for robotic writing mo...

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IEEE-RAS International Conference on Humanoid Robots

作者： Hang Yin Ana Paiva Aude Billard Intelligent Agents and Synthetic Characters Group (GAIPS) University of Lisbon Portugal Ecole Polytechnique Fédérale de Lausanne (EPFL) Learning Algorithms and Systems Laboratory (LASA) Switzerland

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.

关键词： Trajectory Robots Cost function Optimal control Decoding Stochastic processes

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Stretchable capacitive tactile skin on humanoid robot fingers — First experiments and results

Stretchable capacitive tactile skin on humanoid robot finger...

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IEEE-RAS International Conference on Humanoid Robots

作者： Aaron P. Gerratt Nicolas Sommer Stéphanie P. Lacour Aude Billard Laboratory for Soft Bioelectronic Interfaces Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland Learning Algorithms and Systems Laboratory École Polytechnique Fédérale de Lausanne Lausanne Switzerland

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.

关键词： Strain Robot sensing systems Electrodes Skin Joints

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EMG-based learning approach for estimating wrist motion

EMG-based learning approach for estimating wrist motion

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： S. El-Khoury I. Batzianoulis C. W. Antuvan S. Contu L. Masia S. Micera A. Billard Learning Algorithms and Systems Laboratory (LASA)at Ecole Polytechnique Federale de Lausanne (EPFL) Switzerland Assistive Robotics and Interactive Ergonomic Systems Laboratory (ARIES) at Nanyang Technological University Singapore Translational Neural Engineering Laboratory (TNE) at Ecole Polytechnique Federale de Lausanne (EPFL) Switzerland

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.

关键词： EMG EMG-based learning approach estimating wrist motion

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Towards Consistent Reconstructions of Indoor Spaces Based on 6D RGB-D Odometry and KinectFusion

Towards Consistent Reconstructions of Indoor Spaces Based on...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Haiwei Dong Nadia Figueroa Abdulmotaleb El Saddik Multimedia Communications Research Lab School of Electrical Engineering and Computer Science University of Ottawa Ontario Canada Learning Algorithms and Systems Laboratory Ecole Polytechnique Federale de Lausanne (EPFL) Switzerland

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.

关键词： indoor spaces odometry process step Remodeling

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Chief cook and keepon in the bot's funk

Chief cook and keepon in the bot's funk

引用

ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Eric Sauser Marek Michalowski Aude Billard Hideki Kozima EPFL Learning Algorithms and Systems Laboratory Lausanne Switzerland BeatBots LLC Pittsburgh PA USA School of Project Design Miyagi University Sendai Japan Miyagi University BeatBots LLC Pittsburgh PA USA

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?

关键词： Abstracts

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On the Evolution of Fingertip Grasping Manifolds

On the Evolution of Fingertip Grasping Manifolds

引用

IEEE International Conference on Robotics and Automation

作者： K. Hang J. A. Haustein M. Li A. Billard C. Smith D. Kragic the Computer Vision and Active Perception Lab CAS CSC at KTH Royal Institute of Technology Stockholm Sweden the Learning Algorithms and Systems Laboratory (LASA) at école Polytechnique Fédérale de Lausanne (EPFL) Switzerland

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.

关键词： Evolution Fingertip Grasping

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Meandre: semantic-driven data-intensive flows in the clouds

Meandre: semantic-driven data-intensive flows in the clouds

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4th IEEE International Conference on eScience, eScience 2008

作者： Llorà, Xavier Ács, Bernie Auvil, Loretta S. Capitanu, Boris Welge, Michael E. Goldberg, David E. Data-Intensive Technologies and Applications National Center for Supercomputing Applications University of Illinois at Urbana-Champaign Urbana IL 61801 United States Automated Learning Group National Center for Supercomputing Applications University of Illinois at Urbana-Champaign Urbana IL 61801 United States Illinois Genetic Algorithms Laboratory Dept. of Industrial and Enterprise Systems Engineering University of Illinois at Urbana-Champaign Urbana IL 61801 United States

ISBN: (纸本)9780769535357

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 and transparent scalable solution from a laptop to large-scale clusters,(2) creates an unified solution for batch and interactive tasks in high-performance computing environments, and (3) encourages reusing and sharing components. Banking on virtualization and cloud computing techniques the Meandre infrastructure is able to create and dispose Meandre clusters on demand, being transparent to the final user. This paper also presents a prototype of such clustered infrastructure and some results obtained using it. © 2008 IEEE.

关键词： Semantics

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