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4th International Conference "Neurotechnologies and Neurointerfaces", CNN 2022

作者： Grigorev, Nikita Savosenkov, Andrey Udoratina, Anna Kolchina, Anna Maksimenko, Vladimir Gordleeva, Susanna Lobachevsky State University of Nizhny Novgorod Neurotechnology Department Nizhny Novgorod Russia Immanuel Kant Baltic Federal University Center for Neurotechnology and Machine Learning Kaliningrad Russia Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Lobachevsky State University of Nizhny Novgorod Mathematics and Mechanics Department Nizhny Novgorod Russia Neuroscience and Cognitive Technology Lab. Innopolis University Innopolis Russia

ISBN: (数字)9781665463294

ISBN: (纸本)9781665463294

In this study, we investigate how excitatory transcranial magnetic stimulation (TMS) can influence decision speed in the task of classifying ambiguous visual patterns. 30 healthy volunteers were divided into 2 groups. During two experimental sessions they classified ambiguous Necker cubes. Before the second experimental session, stimulation of the area of the cerebral cortex, which is related to decision-making, was performed. One group received sham stimulation;the other group was stimulated with repetitive TMS (rTMS) for 3 minutes at a frequency of 10 Hz. As a result: the rTMS group significantly (p © 2022 IEEE.

关键词： Decision making

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Anterior TMS Speeds up Responses in Perceptual Decision-making Task 6

Anterior TMS Speeds up Responses in Perceptual Decision-maki...

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6th Scientific School "Dynamics of Complex Networks and their Applications", DCNA 2022

作者： Maksimenko, Vladimir Gordleeva, Susanna Grigorev, Nikita Savosenkov, Andrey Kuc, Alexander Udoratina, Anna Grubov, Vadim Kolchina, Anna Kurkin, Semen Kazantsev, Victor Hramov, Alexander Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Mathematics and Mechanics Department Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia Neurotechnology Department Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Neuroscience and Cognitive Technology Lab. Innopolis University Innopolis Russia Nizhny Novgorod Russia

ISBN: (数字)9781665461740

ISBN: (纸本)9781665461740

Our preliminary behavioral experiments suggest that the response time decreases when subjects respond to the repeatedly presented visual stimuli. A potential explanation is that the brain preactivates neural ensembles responsible for stimulus processing. If so, activating these areas before the experiment may reduce the response time immediately, opening ways for exciting practical applications. To test this opportunity, we apply transcranial magnetic stimulation (TMS) before the subjects start performing a perceptual decision-making task. Having compared the response time between the TMS and control groups, we observed a significant change confirming our hypothesis. © 2022 IEEE.

关键词： Decision making

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Deeper Introspective SLAM: How to Avoid Tracking Failures Over Longer Routes?

Deeper Introspective SLAM: How to Avoid Tracking Failures Ov...

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

作者： Kanwal Naveed Muhammad Latif Anjum Wajahat Hussain Donghwan Lee Robotics & Machine Intelligence (ROMI) Lab School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology Islamabad Pakistan Reinforcement Learning Research Lab School of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon South Korea

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Large scale active exploration has recently revealed limitations of visual SLAM’s tracking ability. Active view planning methods based on reinforcement learning have been proposed to improve visual tracking *** this work, we expose the limitations of deep reinforcement learning-based visual SLAM over longer routes. We demonstrate that additional modalities (depth, scene layout) offer little improvement. Furthermore, reward shaping is not the main reason behind the shortsightedness of the state-of-the-art visual SLAM tracker. We propose a novel video vision transformer-based architecture that improves the farsightedness of the visual tracker, which results in the completion of longer routes with efficient *** of 60 challenging routes, our approach manages to complete 56 routes, which is a three-fold improvement over the state-of-the-art active view mapping (DI-SLAM) baseline. Interestingly, ORB-SLAM3 was unable to complete a single route without tracking failure. Our code is available at https://***/w935spuz.

关键词： Visualization Simultaneous localization and mapping Codes Layout Transformers Deep reinforcement learning Robustness Planning Intelligent robots

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Recurrent neural state estimation in domains with long-term dependencies 20th

Recurrent neural state estimation in domains with long-term ...

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20th European Symposium on Artificial Neural Networks, Computational Intelligence and machine learning, ESANN 2012

作者： Duell, Siegmund Weichbrodt, Lina Hans, Alexander Udluft, Steffen Siemens AG Corporate Technology Intelligent Systems and Control Otto-Hahn-Ring 6 Munich81739 Germany Berlin University of Technology Machine Learning Franklinstr. 28-29 Berlin10587 Germany Otto-von-Guericke-University Magdeburg P.O.Box 4120 Magdeburg39016 Germany Ilmenau University of Technology Neuroinformatics and Cognitive Robotics Lab P.O.Box 100565 Ilmenau98684 Germany

ISBN: (纸本)9782874190490

This paper presents a state estimation approach for reinforcement learning (RL) of a partially observable Markov decision process. It is based on a special recurrent neural network architecture, the Markov decision process extraction network with shortcuts (MPEN-S). In contrast to previous work regarding this topic, we address the problem of long-term dependencies, which cause major problems in many real-world applications. The architecture is designed to model the reward-relevant dynamics of an environment and is capable to condense large sets of continuous observables to a compact Markovian state representation. The resulting estimate can be used as input for RL methods that assume the underlying system to be a Markov decision process. Although the approach was developed with RL in mind, it is also useful for general prediction tasks. © 2012, *** publication. All rights reserved.

关键词： State estimation

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Guided Decoding for Robot On-line Motion Generation and Adaption

Guided Decoding for Robot On-line Motion Generation and Adap...

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IEEE-RAS International Conference on Humanoid Robots

作者： Nutan Chen Botond Cseke Elie Aljalbout Alexandros Paraschos Marvin Alles Patrick van der Smagt Machine Learning Research Lab Volkswagen Group Germany Department of Informatics Robotics and Perception Group University of Zurich (UZH) Department of Neuroinformatics UZH and ETH Zurich Switzerland Faculty of Informatics Eötvös Loránd University Budapest Hungary

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

We present a novel motion generation approach for robot arms, with high degrees of freedom, in complex settings that can adapt online to obstacles or new via points. learning from Demonstration facilitates rapid adaptation to new tasks and optimizes the utilization of accumulated expertise by allowing robots to learn and generalize from demonstrated trajectories. We train a transformer architecture, based on conditional variational autoencoder, on a large dataset of simulated trajectories used as demonstrations. Our architecture learns essential motion generation skills from these demonstrations and is able to adapt them to meet auxiliary tasks. Additionally, our approach implements auto-regressive motion generation to enable real-time adaptations, as, for example, introducing or changing via-points, and velocity and acceleration constraints. Using beam search, we present a method for further adaption of our motion generator to avoid obstacles. We show that our model successfully generates motion from different initial and target points and that is capable of generating trajectories that navigate complex tasks across different robotic platforms.

关键词： Robot motion Visualization Robot kinematics Humanoid robots Color Transformers Robustness Trajectory Decoding Collision avoidance

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Prior Visual Relationship Reasoning For Visual Question Answering

Prior Visual Relationship Reasoning For Visual Question Answ...

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IEEE International Conference on Image Processing

作者： Zhuoqian Yang Zengchang Qin Jing Yu Tao Wan Robotics Institute Carnegie Mellon University Pittsburgh PA USA Intelligent Computing and Machine Learning Lab School of ASEE Beihang University China Institute of Information Engineering CAS China School of Biological Science and Medical Engineering Beihang University Beijing China

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Visual Question Answering (VQA) is a representative task of cross-modal reasoning where an image and a free-form question in natural language are presented and the correct answer needs to be determined using both visual and textual information. One of the key issues of VQA is to reason with semantic clues in the visual content under the guidance of the question. In this paper, we propose Scene Graph Convolutional Network (SceneGCN) to jointly reason the object properties and their semantic relations for the correct answer. The visual relationship is projected into a deep learned semantic space constrained by visual context and language priors. Based on comprehensive experiments on two challenging datasets: GQA and VQA 2.0, we demonstrate the effectiveness and interpretability of the new model.

关键词： Visualization Semantics Convolution Cognition Knowledge discovery Benchmark testing Measurement

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Context-Based Meta Reinforcement learning for Robust and Adaptable Peg-in-Hole Assembly Tasks

arXiv

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arXiv 2024年

作者： Shokry, Ahmed Gomaa, Walid Zaenker, Tobias Dawood, Murad Maged, Shady A. Awad, Mohammed I. Bennewitz, Maren Humanoid Robots Lab University of Bonn the Center for Robotics Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence Bonn Germany Cyber Physical Systems Lab Egypt Japan University of Science and Technology Alexandria Egypt Faculty of Engineering Alexandria University Alexandria Egypt Mechatronics Department Ain Shams University Cairo Egypt

Peg-in-hole assembly in unknown environments is a challenging task due to onboard sensor errors, which result in uncertainty and variations in task parameters such as the hole position and orientation. Meta Reinforcement learning (Meta RL) has been proposed to mitigate this problem as it learns how to quickly adapt to new tasks with different parameters. However, previous approaches either depend on a sample-inefficient procedure or human demonstrations to perform the task in the real world. Our work modifies the data used by the Meta RL agent and uses simple features that can be easily measured in the real world even with an uncalibrated camera. We further adapt the Meta RL agent to use data from a force/torque sensor, instead of the camera, to perform the assembly, using a small amount of training data. Finally, we propose a fine-tuning method that consistently and safely adapts to out-of-distribution tasks with parameters that differ by a factor of 10 from the training tasks. Our results demonstrate that the proposed data modification significantly enhances the training and adaptation efficiency and enables the agent to achieve 100% success in tasks with different hole positions and orientations. Experiments on a real robot confirm that both camera- and force/torque sensor-equipped agents achieve 100% success in tasks with unknown hole positions, matching their simulation performance and validating the approach’s robustness and applicability. Compared to the previous work with sample-inefficient adaptation, our proposed methods are 10 times more sample-efficient in the real-world tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Preventing Unconstrained CBF Safety Filters Caused by Invalid Relative Degree Assumptions

arXiv

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arXiv 2024年

作者： Brunke, Lukas Zhou, Siqi Schoellig, Angela P. Learning Systems and Robotics Lab The Munich Institute of Robotics and Machine Intelligence Technical University of Munich Munich80333 Germany University of Toronto Institute for Aerospace Studies North YorkONM3H 5T6 Canada University of Toronto Robotics Institute TorontoONM5S 1A4 Canada Vector Institute for Artificial Intelligence TorontoONM5G 0C6 Canada

Control barrier function (CBF)-based safety filters are used to certify and modify potentially unsafe control inputs to a system such as those provided by a reinforcement learning agent or a non-expert user. In this context, safety is defined as the satisfaction of state constraints. Originally designed for continuous-time systems, CBF safety filters typically assume that the system’s relative degree is well-defined and is constant across the domain;however, this assumption is restrictive and rarely verified—even linear system dynamics with a quadratic CBF candidate may not satisfy this assumption. In real-world applications, continuous-time CBF safety filters are implemented in discrete time, exacerbating issues related to violating the condition on the relative degree. These violations can lead to the safety filter being unconstrained (any control input may be certified) for a finite time interval and result in chattering issues and constraint violations. We propose an alternative formulation to address these challenges. Specifically, we present a theoretically sound method that employs multiple CBFs to generate bounded control inputs at each state within the safe set, thereby preventing incorrect certification of arbitrary control inputs. Using this approach, we derive conditions on the maximum sampling time to ensure safety in discrete-time implementations. We demonstrate the effectiveness of our proposed method through simulations and real-world quadrotor experiments, successfully preventing chattering and constraint violations. Finally, we discuss the implications of violating the relative degree condition on CBF synthesis and learning-based CBF methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Continuous time systems

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Semantically Safe Robot Manipulation: From Semantic Scene Understanding to Motion Safeguards

arXiv

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arXiv 2024年

作者： Brunke, Lukas Zhang, Yanni Römer, Ralf Naimer, Jack Staykov, Nikola Zhou, Siqi Schoellig, Angela P. The Learning Systems and Robotics Lab The Munich Institute of Robotics and Machine Intelligence Technical University of Munich Munich80333 Germany University of Toronto Institute for Aerospace Studies North YorkONM3H 5T6 Canada University of Toronto Robotics Institute TorontoONM5S 1A4 Canada Vector Institute for Artificial Intelligence TorontoONM5G 0C6 Canada

Ensuring safe interactions in human-centric environments requires robots to understand and adhere to constraints recognized by humans as "common sense" (e.g., "moving a cup of water above a laptop is unsafe as the water may spill" or "rotating a cup of water is unsafe as it can lead to pouring its content"). Recent advances in computer vision and machine learning have enabled robots to acquire a semantic understanding of and reason about their operating environments. While extensive literature on safe robot decision-making exists, semantic understanding is rarely integrated into these formulations. In this work, we propose a semantic safety filter framework to certify robot inputs with respect to semantically defined constraints (e.g., unsafe spatial relationships, behaviours, and poses) and geometrically defined constraints (e.g., environment-collision and self-collision constraints). In our proposed approach, given perception inputs, we build a semantic map of the 3D environment and leverage the contextual reasoning capabilities of large language models to infer semantically unsafe conditions. These semantically unsafe conditions are then mapped to safe actions through a control barrier certification formulation. We evaluated our semantic safety filter approach in teleoperated tabletop manipulation tasks and pick-and-place tasks, demonstrating its effectiveness in incorporating semantic constraints to ensure safe robot operation beyond collision avoidance. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Grasp Approach Under Positional Uncertainty Using Compliant Tactile Sensing Modules and Reinforcement learning

Grasp Approach Under Positional Uncertainty Using Compliant ...

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Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Viral Rasik Galaiya Thiago Eustaquio Alves De Oliveira Xianta Jiang Vinicius Prado Da Fonseca Department of Computer Science Robotics and AI Lab Memorial University of Newfoundland and Labrador St. John’s Canada Department of Computer Science Ubiquitous Computing and Machine Learning Lab Memorial University of Newfoundland and Labrador St. John’s Canada Department of Computer Science Haptics and Robots Research Group Lakehead University Thunder Bay Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

Object grasping is a complex task that requires high environmental awareness. While vision generally provides highly detailed environmental information, light changes, object transparency, camera resolution, and other factors such as occlusion and clutter affect its perception of object pose. Due to these limitations, there may be some deviation between the estimated and actual object pose in unstructured environments. The use of compliant tactile sensors relaxes the requirement of strict finger position planning while providing essential information regarding contact with the target object. Therefore, under positional uncertainty, the robotic system may use compliant tactile sensors to perform multiple attempts before a successful grasp. In the present paper, we investigate using reinforcement learning and compliant tactile sensors to provide adaptive grasping under pose uncertainty. Here, we identify a policy that models an object position estimation error while minimizing the exploratory sensor contact before obtaining a grasp. Our method was able to perform a successful grasp while reducing the number of attempts from an average of five to an average of two per episode.

关键词： Estimation error Uncertainty Contacts Fingers Tactile sensors Reinforcement learning Grasping

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