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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Youssef Michel Matteo Saveriano Fares J. Abu-Dakka Dongheui Lee Chair of Human-Centered Assistive Robotics Technical University of Munich Munich Germany Department of Industrial Engineering University of Trento Trento Italy Munich Institute of Robotics and Machine Intelligence Technical University of Munich Munich Germany Autonomous Systems TU Wien Vienna Austria German Aerospace Center (DLR) Institute of Robotics and Mechatronics Munich Germany

Recently, several approaches have attempted to combine motion generation and control in one loop to equip robots with reactive behaviors, that cannot be achieved with traditional time-indexed tracking controllers. These approaches however mainly focused on positions, neglecting the orientation part which can be crucial to many tasks e.g. screwing. In this work, we propose a control algorithm that adapts the robot's rotational motion and impedance in a closed-loop manner. Given a first-order Dynamical System representing an orientation motion plan and a desired rotational stiffness profile, our approach enables the robot to follow the reference motion with an interactive behavior specified by the desired stiffness, while always being aware of the current orientation, represented as a Unit Quaternion (UQ). We rely on the Lie algebra to formulate our algorithm, since unlike positions, UQ feature constraints that should be respected in the devised controller. We validate our proposed approach in multiple robot experiments, showcasing the ability of our controller to follow complex orientation profiles, react safely to perturbations, and fulfill physical interaction tasks.

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Towards an Integrated Evaluation Framework for XAI: An Experimental Study

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Procedia Computer Science 2022年 207卷 3884-3893页

作者： Qiyuan Zhang Mark Hall Mark Johansen Vedran Galetic Jacques Grange Santiago Quintana-Amate Alistair Nottle Dylan M Jones Phillip L Morgan Cardiff University Centre for AI Robotics and Human-Machine Systems (IROHMS) Cardiff University Human Factors Excellence Research Group (HuFEx) School of Psychology Cardiff University Cardiff CF10 3AT UK Airbus Central R&T Bristol UK BS16 1EJ

Increasing prevalence of opaque black-box AI has highlighted the need for explanations of their behaviours, for example, via explanation artefacts/proxy models. The current paper presents a paradigm for human-grounded experiments to evaluate the relationship between explanation fidelity, human learning performance, understanding and trust in a black-box AI by manipulating the complexity of an explanatory artefact. Decision trees were used in the current experiment as exemplar interpretable surrogate models, providing explanations approximating black-box behaviour, by means of explanation by simplification. Consistent with our hypotheses: 1) explanatory artefacts brought about better learning, while greater decision tree depths led to greater interpretability of the AI's performance and greater trust in the AI; and 2) explanatory artefacts facilitated learning and task performance even after they were withdrawn. Findings are discussed in terms of the interplay between human understanding, trust and AI system performance, highlighting the simplifying assumption of a monotonic relationship between explanation fidelity and interpretability.

关键词： Explainable AI understandability interpretability trust category learning decision aids surrogate models decision trees explanation fidelity explanatory artefacts

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Robot Task Commander: A Framework and IDE for Robot Application Development

Robot Task Commander: A Framework and IDE for Robot Applicat...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Stephen Hart Paul Dinh John D. Yamokoski Brian Wightman Nicolaus Radford Manufacturing Systems Research group at General Motors Warren Michigan 48090 USA Robotics & Automation group at Oceaneering Space Systems Houston TX 77058 USA Institute of Human and Machine Cognition Pensacola FL 32502 USA Robotic Systems Technology Branch NASA/Johnson Space Center Houston TX 77058 USA

ISBN: (纸本)9781479969357

This paper introduces the Robot Task Commander (RTC) framework for defining, developing, and deploying robot application software for use in different run-time contexts. RTC was created by NASA-JSC in conjunction with General Motors for use with the Robonaut-2 and Valkyrie humanoid robot platforms. RTC provides a robot programming syntax and an IDE appropriate for use by experts and non-experts for implementation and execution. An expert developer can implement a new application with a combination of scripts, called process nodes, and state machines that set the control mode of the robot. A non-expert developer can assemble process nodes and controller state machines into novel hierarchical applications using a visual programming language (VPL). This VPL also allows developers to interface with other RTC applications or with third-party software packages using a variety of network transport mechanisms (ROS, TCP, shared memory, etc.). RTC represents an advantage over other robot programming frameworks by providing multiple levels of flexibility for development. The efficacy of RTC is demonstrated through examples of sophisticated behaviors, such as programming the Valkyrie robot to grab objects and turn a valve.

关键词： Engines Syntactics Process control Libraries Visualization Robot sensing systems

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Brute-forcing hierarchical functionals for paralinguistics: A waste of feature space?

Brute-forcing hierarchical functionals for paralinguistics: ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Bjorn Schuller Matthias Wimmer Lorenz Mosenlechner Christian Kern Dejan Arsic Gerhard Rigoll Institute for Human-Machine Communication Technische Universität München Germany Perceptual Computing Laboratory Waseda University Tokyo Japan Intelligent Autonomous Systems Group Technische Univeristät München Germany

While the " 'quasi-state-of-the-art'" towards acoustic emotion recognition relies on multivariate time-series analysis of e.g. pitch, energy, or MFCC by statistical functionals as moments or extrema, only few respect statistical noise by outliers due to too long segments as turns. Such noise can be overcome by hierarchical functionals as means of extrema over smaller units as words or chunks. Segmentation of such units however usually relies on transcription. We therefore discuss hierarchical functionals based on automatic segmentation and their systematic generation as opposed to common expert-driven selection. To cope with rapidly growing feature spaces iquest5k, we discuss data-driven two-stage compression based on SVM- SFFS. Extensive test-runs are carried out on two known emotion and behavior corpora, and show superiority of the suggested approach.

关键词： Emotion recognition Support vector machines Support vector machine classification Cepstral analysis Mel frequency cepstral coefficient Acoustic noise Acoustic testing Man machine systems Intelligent systems Frequency domain analysis

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Learning to Sail Dynamic Networks: The MARLIN Reinforcement Learning Framework for Congestion Control in Tactical Environments

Learning to Sail Dynamic Networks: The MARLIN Reinforcement ...

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MILCOM, Military Communications Conference

作者： Raffaele Galliera Mattia Zaccarini Alessandro Morelli Roberto Fronteddu Filippo Poltronieri Niranjan Suri Mauro Tortonesi Florida Institute for Human & Machine Cognition (IHMC) Department of Intelligent Systems & Robotics The University of West Florida (UWF) Pensacola FL USA Distributed Systems Research Group University of Ferrara Ferrara Italy US Army DEVCOM Army Research Laboratory (ARL) Adelphi MD USA

Conventional Congestion Control (CC) algorithms, such as TCP Cubic, struggle in tactical environments as they misinterpret packet loss and fluctuating network performance as congestion symptoms. Recent efforts, including our own MARLIN, have explored the use of Reinforcement Learning (RL) for CC, but they often fall short of generalization, particularly in competitive, unstable, and unforeseen scenarios. To address these challenges, this paper proposes an RL framework that leverages an accurate and parallelizable emulation environment to reenact the conditions of a tactical network. We also introduce refined RL formulation and performance evaluation methods tailored for agents operating in such intricate scenarios. We evaluate our RL learning framework by training a MARLIN agent in conditions replicating a bottleneck link transition between a Satellite Communication (SATCOM) and an UHF Wide Band (UHF) radio link. Finally, we compared its performance in file transfer tasks against Transmission Control Protocol (TCP) Cubic and the default strategy implemented in the Mockets tactical communication middleware. The results demonstrate that the MARLIN RL agent outperforms both TCP and Mockets under different perspectives and highlight the effectiveness of specialized RL solutions in optimizing CC for tactical network environments.

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Integrated Bi-Manual Motion Generation and Control shaped for Probabilistic Movement Primitives

Integrated Bi-Manual Motion Generation and Control shaped fo...

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IEEE-RAS International Conference on humanoid Robots

作者： Jonathan Vorndamme João Carvalho Riddhiman Laha Dorothea Koert Luis Figueredo Jan Peters Sami Haddadin Department of Computer Engineering Technische Universität München (TUM) TUM School of Computation Information and Technology (CIT) Munich Institute of Robotics and Machine Intelligence (MIRMI) Germany Intelligent Autonomous Systems group Technische Universitat Darmstadt (TUD) Germany Center for Cognitive Science TUD Germany

ISBN: (纸本)9798350309805

This work introduces a novel cooperative control framework that allows for real-time reactiveness and adaptation whilst satisfying implicit constraints stemming from proba-bilistic/stochastic trajectories. Stemming from task-oriented sampling and/or task-oriented demonstrations, e.g., learning based on motion primitives, such trajectories carry additional information often neglected during real-time control deployment. In particular, methods such as probabilistic movement primitives offer the advantage to capture the inherent stochasticity in human demonstrations - which in turn reflects human's understanding about task-variability and adaption possibilities. This information, however, is often poorly exploited and, mostly, used during offline trajectory planning stage. Our work instead introduces a novel real-time motion-generation strategy that explicitly exploits such information to improve trajectories according to changes in the environmental condition and robot task-space topology. The proposed solution is particularly well-suited for bi-manual and coordinated systems where the increased kinematic complexity, tightly-coupled constraints and reduced workspace have detrimental effects on the manipula-bility, joint-limits, and are even capable of causing unstable behavior and task-failure. Our methodology addresses these challenges, and improves performance and task-execution by taking the confidence range region explicitly into account whilst maneuvering towards better configurations. Furthermore, it can directly cope with different closed-chain kinematics and task-space topologies, resulting for instance from different grasps. Experimental evaluations on a bi-manual Franka panda robot show that the method can run in the inner control loop of the robot and enables successful execution of highly constrained tasks.

关键词： Trajectory planning Robot kinematics humanoid robots Kinematics Probabilistic logic Real-time systems Trajectory

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Sample Efficient Dynamics Learning for Symmetrical Legged Robots: Leveraging Physics Invariance and Geometric Symmetries

Sample Efficient Dynamics Learning for Symmetrical Legged Ro...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Jee-eun Lee Jaemin Lee Tirthankar Bandyopadhyay Luis Sentis Department of Aerospace Engineering and Engineering Mechanics Human Centered Robotics Laboratory The University of Texas at Austin USA Department of Mechanical and Civil Engineering California Institute of Technology Pasadena CA USA Robotics and Autonomous Systems Group Data61 CSIRO QLD Australia Faculty of Department of Aerospace Engineering and Engineering Mechanics The University of Texas at Austin USA

Model generalization of the underlying dynamics is critical for achieving data efficiency when learning for robot control. This paper proposes a novel approach for learning dynamics leveraging the symmetry in the underlying robotic system, which allows for robust extrapolation from fewer samples. Existing frameworks that represent all data in vector space fail to consider the structured information of the robot, such as leg symmetry, rotational symmetry, and physics invariance. As a result, these schemes require vast amounts of training data to learn the system's redundant elements because they are learned independently. Instead, we propose considering the geometric prior by representing the system in symmetrical object groups and designing neural network architecture to assess invariance and equivariance between the objects. Finally, we demonstrate the effectiveness of our approach by comparing the generalization to unseen data of the proposed model and the existing models. We also implement a controller of a climbing robot based on learned inverse dynamics models. The results show that our method generates accurate control inputs that help the robot reach the desired state while requiring less training data than existing methods.

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Sample Efficient Dynamics Learning for Symmetrical Legged Robots: Leveraging Physics Invariance and Geometric Symmetries

arXiv

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arXiv 2022年

作者： Lee, Jee-Eun Lee, Jaemin Bandyopadhyay, Tirthankar Sentis, Luis Human Centered Robotics Laboratory Department of Aerospace Engineering and Engineering Mechanics The University of Texas Austin United States The Department of Mechanical and Civil Engineering California Institute of Technology PasadenaCA United States Faculty of Department of Aerospace Engineering and Engineering Mechanics The University of Texas Austin United States Robotics and Autonomous Systems Group Data61 Csiro QLD 4069 Australia

关键词： Vector spaces

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Latent event-predictive encodings through counterfactual regularization

arXiv

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arXiv 2021年

作者： Humaidan, Dania Otte, Sebastian Gumbsch, Christian Wu, Charley Butz, Martin V. Neuro-Cognitive Modeling Group Dep. of Psychology Dep. of Computer Science Sand 14 Tübingen72076 Germany Cluster of Excellence – Machine Learning for Science Maria-von-Linden-Str. 6 Tübingen72076 Germany Autonomous Learning Group MPI for Intelligent Systems Tübingen72076 Germany Human and Machine Cognition Lab Maria-von-Linden-Str. 6 Tübingen72076 Germany

A critical challenge for any intelligent system is to infer structure from continuous data streams. Theories of event-predictive cognition suggest that the brain segments sensorimotor information into compact event encodings, which are used to anticipate and interpret environmental dynamics. Here, we introduce a SUrprise-GAted Recurrent neural network (SUGAR) using a novel form of counterfactual regularization. We test the model on a hierarchical sequence prediction task, where sequences are generated by alternating hidden graph structures. Our model learns to both compress the temporal dynamics of the task into latent event-predictive encodings and anticipate event transitions at the right moments, given noisy hidden signals about them. The addition of the counterfactual regularization term ensures fluid transitions from one latent code to the next, whereby the resulting latent codes exhibit compositional properties. The implemented mechanisms offer a host of useful applications in other domains, including hierarchical reasoning, planning, and decision making. Copyright © 2021, The Authors. All rights reserved.

关键词： Recurrent neural networks

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An EMG-Based Objective Function for human-in-the-Loop Optimization

An EMG-Based Objective Function for Human-in-the-Loop Optimi...

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IEEE International Conference on Rehabilitation robotics (ICORR)

作者： María Alejandra Díaz Sander De Bock Philipp Beckerle Jan Babič Tom Verstraten Kevin De Pauw Human Physiology and Sports Physiotherapy Research Group Brussels Human Robotic Research Center Vrije Universiteit Brussel Brussels Belgium Department of Electrical Engineering and the Department of Artificial Intelligence in Biomedical Engineering Institute of Autonomous Systems and Mechatronics Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Automation Biocybernetics and Robotics Laboratory for Neuromechanics and Biorobotics Jožef Stefan Institute Ljubljana Slovenia Faculty of Electrical Engineering University of Ljubljana Ljubljana Slovenia Robotics and Multibody Mechanics Research Group Vrije Universiteit Brussel and Flanders Make Brussels Belgium

Advancements in wearable robots aim to improve the users' motion, performance, and comfort by optimizing, mainly, energetic cost (EC). However, EC is a noisy measurement with a physiological delayed response that requires long evaluation periods and wearing an uncomfortable mask. This study aims to estimate and minimize an EMG-based objective function that describes the natural energetic expenditure of individuals walking. This objective is assessed by combining multiple electromyography (EMG) variables from the EMG intensity and muscle synergies. To evaluate this objective function simply and repeatedly, we prescribed step frequency (SF) via a metronome and optimized this frequency to minimize muscle activity demands. Further, a linear mixed-effects model was fitted for EC, with the EMG variables as fixed-effects and a random intercept that varies by participant. After the model was fitted to the data, a cubic polynomial was used to identify the optimal SF that reduces the overall EMG-based objective function. Our analysis outlines that the proposed objective function is comparable to the EC during walking, the primary objective function used in human-in-the-loop optimization. Thus, this EMG-based objective function could be potentially used to optimize wearable robots and improve human-robot interaction.

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