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Post-hoc Explainable Reinforcement Learning Using Probabilistic Graphical models 1

引用

11th international Advanced Computing conference, IACC 2021

作者： Deshpande, Saurabh Walambe, Rahee Kotecha, Ketan Jakovljević, Marina Marjanović Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune412 115 India Faculty of Technical Sciences Singidunum University Belgrade Serbia

ISBN: (数字)9783030955021

ISBN: (纸本)9783030955014

Reinforcement learning (RL) has recently enjoyed significant success in games, robotics, bioinformatics, etc. Soon, it will not be uncommon to see AI models employing RL agents integrated with various hardware and software solutions. Due to its generality and robustness, RL is applied in several disciplines such as game theory, control theory, multi-agent systems, swarm intelligence, robotics, and NLP. Despite these advances and successes, reinforcement learning faces many challenges for real-world adoption. Some of the major difficulties being, operator’s trust and ability of an agent to explain the actions taken in a human-understandable manner. Traditionally the AI systems are black-box models. With the advent of various legal regulations worldwide, notably the European General Data Protection Regulation (GDPR) [29], it has started becoming mandatory that the AI models be transparent, interpretable, and secure. If an RL agent can effectively and accurately explain the actions carried out by the RL system to the observers/operators, it will be a tangible step towards developing the ART (accountable, reliable and trustworthy) RL agent. This can effectively facilitate the adoption of RL systems in real-world domains. Various explainable AI (XAI) methods have been reported in the literature. However, there is a considerable lacuna in the availability of Explainable RL (XRL) methods. This paper introduces a novel RL algorithm agnostic approach of generating human-understandable explanations using the probabilistic graphical model. This method is based on Probabilistic Graphical models (PGM) [36]. It is algorithm agnostic in that it is not dependent on any specific RL method and can be integrated with any RL algorithm. We also introduce a PGM model, which is learned along with an agent’s training via classic methods and used for generating explanations at run time. Specific case studies are considered, and results are presented which demonstrate our approach. Our exper

关键词： Reinforcement learning

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A survey of state-of-the-art U-space research

A survey of state-of-the-art U-space research

引用

international conference on Autonomous Robots and Agents, ICARA

作者： Jes Hundevadt Jepsen Kristian Husum Laursen Kjeld Jensen SDU UAS Center University of Southern Denmark Odense Denmark

ISBN: (数字)9798350394245

ISBN: (纸本)9798350394252

U-space, the EU implementation of air traffic management of drones, is one of the key enablers to realizing a safe integration of crewed and uncrewed aviation in shared airspace. The development and integration of U-space and its services will support a high level of digitalization and automation of drones and has been ongoing since 2017 when its blueprint was published by SESAR. Now with the Commission Implementing Regulation (EU) 2021/{ 664/665/666} in force, the establishment of U-space airspace in the EU is becoming a reality. This paper surveys available research and state-of-the-art on U-space. A total of 60 publications have been identified using the presented literature search method. Based on the insights gained from the literature search, eight subdomains have been defined. Each publication has been assigned to one primary subdomain and one or more secondary subdomain(s) within U-space. The subdomains are described and discussed and probable future research perspectives are extracted.

关键词： Surveys automation Search methods Force Regulation Air traffic control Robots

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Particle Swarm Optimization Based Control For a Controlled Floating Space Robot

Particle Swarm Optimization Based Control For a Controlled F...

引用

robotics, automation and Artificial Intelligence (RAAI), IEEE international conference on

作者： David Chaudhary Senthil Murugan Department of Aerospace Engineering Aero-electro(i) mechanics & systems lab Indian Institute of Technology Madras Chennai India

ISBN: (数字)9798350307566

ISBN: (纸本)9798350307573

This paper explores Particle Swarm Optimization (PSO) based control of a morphing robot arm mounted on a Controlled Floating Space Robot (CFSR). CFSR is a new study spacecraft model allowing controlled adjustments of the satellite base's pose. The dynamic model of the chaser spacecraft is derived relative to the target in an inertial frame, using the Clohessy- Wiltshire model or Hills equations for defining their relative motion. It is assumed that both spacecraft are in an Inplane-elliptical formation. The manipulator dynamics are obtained through Lagrangian formulation. The PSO algorithm optimizes controller gain values while a quintic polynomial tra-jectory is provided as input to the arm joints. Results show that the PSO-based controller effectively reduces tracking error and power requirements, and it is also compared with a conventional proportional-derivative (PD) controller.

关键词： Space vehicles Satellites Trajectory tracking Arms Aerospace electronics Mathematical models Polynomials

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Design and Real-World Application of a Flexible Mobile Robot System for Urban Logistics

Design and Real-World Application of a Flexible Mobile Robot...

引用

international conference on Emerging Technologies and Factory automation (ETFA)

作者： Lukas Müller Hao Pang Lars Ohnemus Maximilian Winter Kai Furmans Institute for Material Handling and Logistics Karlsruhe Institute for Technology Karlsruhe Germany SEW-EURODRIVE GmbH & Co KG Bruchsal Germany

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

As the demand for online retail and food delivery services continues to grow, it becomes increasingly important to find efficient last-mile delivery solutions that can support the sustainability of urban areas. Autonomous Delivery Robots (ADRs) could potentially offer a solution to these challenges. However, long-term real-world studies are still required. This work presents a concept for a flexible ADR-based logistics system for urban areas, with a focus on the design of a robust ADR with detachable transport boxes and accompanying infrastructure. Here, we draw on well-established methods from autonomous mobile robotics in intralogistics. We present a two-mode delivery concept that removes the need for the recipient to be present at the time of delivery. This concept is currently being tested in the ongoing efeuCampus living lab in Bruchsal, Germany. We provide first findings and recommendations for further research in this area.

关键词： Urban areas Mobile robots Sustainable development Manufacturing automation Logistics

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Differentially Encoded Observation Spaces for Perceptive Reinforcement Learning

Differentially Encoded Observation Spaces for Perceptive Rei...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Lev Grossman Brian Plancher Berkshire Grey Bedford MA USA Barnard College Columbia University New York NY USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Perceptive deep reinforcement learning (DRL) has lead to many recent breakthroughs for complex AI systems leveraging image-based input data. Applications of these results range from super-human level video game agents to dexterous, physically intelligent robots. However, training these perceptive DRL-enabled systems remains incredibly compute and memory intensive, often requiring huge training datasets and large experience replay buffers. This poses a challenge for the next generation of field robots that will need to be able to learn on the edge in order to adapt to their environments. In this paper, we begin to address this issue through differentially encoded observation spaces. By reinterpreting stored imagebased observations as a video, we leverage lossless differential video encoding schemes to compress the replay buffer without impacting training performance. We evaluate our approach with three state-of-the-art DRL algorithms and find that differential image encoding reduces the memory footprint by as much as 14.2× and 16.7× across tasks from the Atari 2600 benchmark and the DeepMind Control Suite (DMC) respectively. These savings also enable large-scale perceptive DRL that previously required paging between flash and RAM to be run entirely in RAM, improving the latency of DMC tasks by as much as 32%.

关键词： Training Video games Image coding Monte Carlo methods Memory management Random access memory Streaming media

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On Improving Boundary Quality of Instance Segmentation in Cluttered and Chaotic Scenarios

On Improving Boundary Quality of Instance Segmentation in Cl...

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IEEE international conference on robotics and automation (ICRA)

作者： Biqi Yang Xiaojie Gao Xianzhi Li Yun-Hui Liu Chi-Wing Fu Pheng-Ann Heng Department of Computer Science and Engineering The Chinese University of Hong Kong Department of Mechanical and Automation Engineering The Chinese University of Hong Kong School of Computer Science and Technology Huazhong University of Science and Technology Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology

Instance segmentation is a long-standing task for supporting robotic bin picking. However, objects of diverse classes can be closely packed with occlusions in cluttered and chaotic scenes, hence, even recent methods could have difficulty in locating clear and precise boundaries to distinguish nearby objects. In this work, we aim to improve the boundary quality of the instance masks for robust and precise instance segmentation in these challenging scenarios. Technical-wise, we first formulate an IoU-based Boundary-aware Mask head (IBM head) for predicting the instance-level mask, boundary, and their corresponding IoU scores. With this core module, we then follow the coarse-to-fine strategy and design our pipeline with two stages: an 1IoUNet to learn localization-based objectness cue and a hierarchical mask refiner to produce sharper and cleaner boundaries. We deploy the IBM head throughout the framework. Extensive experimental results on three grasping benchmarks manifest that our method attains the best instance segmentation performance, compared with the state-of-the-art approaches. Practically, we conduct real-world picking tests to show that with the objectness and boundary IoU scores as guidance, we are able to filter invalid (occluded) instances and select high-fidelity (exposed) instances for grasping.

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Comparison of Rating Scale and Pairwise Comparison methods for Measuring Human Co-worker Subjective Impression of Robot during Physical Human-Robot Collaboration

Comparison of Rating Scale and Pairwise Comparison Methods f...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Qiao Wang Ziqi Wang Marc G. Carmichael Dikai Liu Chin-Teng Lin Robotics Institute Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW Australia Australian Artificial Intelligence Institute School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW Australia

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The Rating Scale method has been long deemed the standard for measuring subjective perceptions. However, in the field of physical human-robot collaboration (pHRC), its aptness should be put under scrutiny due to inherent challenges such as response bias, between-subject variations, and the granularity *** variances can introduce significant bias in the rating scale results. A high granularity in the scale could overwhelm participants, leading to unclear and biased responses, while a low granularity may gloss over the fine nuances of human feelings. Additionally, there’s a notable risk of receiving careless responses, which compromise data reliability. Recognizing these challenges, this paper proposes the application of Pairwise Comparison (PC) in pHRC — an alternative survey technique that emphasizes direct comparisons between items on the defined criteria. By using the NASA Task Load Index (NASA-TLX) as a template, RS and PC questionnaires are designed and used in a series of pHRC experiments. Our preliminary findings suggest that PC is more precise and robust than the rating scale method. Compared to RS, PC fosters authentic participant interests in the experiment by intuitive question design and reducing the experimental duration. Besides, the accuracy and reliability of PC are also found to be consistent regardless of the variations in our experimental procedure design.

关键词： Surveys Accuracy NASA Collaboration Reliability engineering Particle measurements Task analysis

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Visual recognition incorporating features of self-supervised models for the use of unlabelled data

Visual recognition incorporating features of self-supervised...

引用

IEEE IFAC international conference on automation (ICA) / 24th Congress of the Chilean-Association-of-Automatic-Control (ACCA)

作者： Diaz, Gabriel Nicolis, Orietta Peralta, Billy Univ Andres Bello Dept Ciencias Ingn Santiago Chile

ISBN: (纸本)9781665401272

Automatic visual object recognition has gained great popularity in the world and is successfully applied to various areas such as robotics, security or commerce using deep learning techniques. Training in machine learning models based on deep learning requires an enormous amount of supervised data, which is expensive to obtain. An alternative is to use semi-supervised models as co-training where the views given by deep networks are differentiated using models that incorporate lateral information from each training object. In this document, we describe and test a co-training model for deep networks, adding as auxiliary inputs to self-supervised network features. The results show that the proposed model managed to converge using a few dozen iterations, exceeding 2 % in precision compared to recent models. This model, despite its simplicity, manages to be competitive with more complex recent works. As future work, we plan to modify deep self-supervised networks to increase diversity in co-training learning.

关键词： Co-training Deep Learning Semi-Supervised learning Self-supervised learning

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Performance Analysis of ANFIS and DRL Based Speed Controllers for Sensorless Drives

Performance Analysis of ANFIS and DRL Based Speed Controller...

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IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Kasim M. Al-Aubidy Abdullah F. Al-Saoudi Mechatronics Eng. Dept. Tishk International University Erbil Iraq

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Permanent magnet synchronous machines (PMSMs) are of great interest in automation and robotics applications due to their small size, high efficiency, and low maintenance requirements. However, these motors face challenges in controlling their speed and position, such as precise rotor detection. This situation is essential in many Industrial applications. Conventional controllers such as PID controllers are commonly used, but their performance is often affected by changes in system dynamics, necessitating updating of controller parameters based on the accurate mathematical model of the motor. This research aims to investigate several intelligent control strategies that do not rely on the mathematical model of the motor. The performance of the drive system will be evaluated using three intelligent controllers: a neural network controller, a neural fuzzy inference system (ANFIS) controller, and a deep reinforcement learning (DRL) controller. The performance of these smart controllers will be compared with that of the PID controller. The results presented in this paper demonstrate the ability of the proposed smart controllers to regulate the speed of PMSMs compared to a PID controller. Specifically, the ANFIS-based controller shows the best performance in terms of maximum overshoot, while the DRL controller demonstrates the best settling time.

关键词： automation Service robots System dynamics Velocity control Motors Robot sensing systems Control systems Mathematical models Maintenance Time factors

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AirNeRF: 3D Reconstruction of Human with Drone and NeRF for Future Communication Systems

AirNeRF: 3D Reconstruction of Human with Drone and NeRF for ...

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IEEE international conference on automation Science and Engineering (CASE)

作者： Alexey Kotcov Maria Dronova Vladislav Cheremnykh Sausar Karaf Dzmitry Tsetserukou Intelligent Space Robotics Laboratory CDE Skolkovo Institute of Science and Technology Moscow Russia

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In the rapidly evolving landscape of digital content creation, the demand for fast, convenient, and autonomous methods of crafting detailed 3D reconstructions of humans has grown significantly. Addressing this pressing need, our AirNeRF system presents an innovative pathway to the creation of a realistic 3D human avatar. Our approach leverages Neural Radiance Fields (NeRF) with an automated drone-based video capturing method. The acquired data provides a swift and precise way to create high-quality human body reconstructions following several stages of our system. The rigged mesh derived from our system proves to be an excellent foundation for freeview synthesis of dynamic humans, particularly well-suited for the immersive experiences within gaming and virtual reality.

关键词： Three-dimensional displays Computer aided software engineering automation Communication systems Avatars Pressing Neural radiance field Drones

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