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Differentially Flat learning-Based Model Predictive Control Using a Stability, State, and Input Constraining Safety Filter

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IEEE CONTROL SYSTEMS LETTERS 2023年 7卷 2191-2196页

作者： Hall, Adam W. Greeff, Melissa Schoellig, Angela P. Univ Toronto Learning Syst & Robot Lab Inst Aerosp Studies Toronto ON M3H 5T6 Canada Vector Inst Artificial Intelligence Toronto ON M5G 1M1 Canada Univ Toronto Learning Syst & Robot Lab Inst Aerosp Studies Toronto ON Canada Tech Univ Munich Learning Syst & Robot Lab D-80333 Munich Germany Tech Univ Munich Munich Inst Robot & Machine Intelligence D-80333 Munich Germany

learning-based optimal control algorithms control unknown systems using past trajectory data and a learned model of the system dynamics. These controllers use either a linear approximation of the learned dynamics, trading performance for faster computation, or nonlinear optimization methods, which typically perform better but can limit real-time applicability. In this letter, we present a novel nonlinear controller that exploits differential flatness to achieve similar performance to state-of-the-art learning-based controllers but with significantly less computational effort. Differential flatness is a property of dynamical systems whereby nonlinear systems can be exactly linearized through a nonlinear input mapping. Here, the nonlinear transformation is learned as a Gaussian process and is used in a safety filter that guarantees, with high probability, stability as well as input and flat state constraint satisfaction. This safety filter is then used to refine inputs from a flat model predictive controller to perform constrained nonlinear learning-based optimal control through two successive convex optimizations. We compare our method to state-of-the-art learning-based control strategies and achieve similar performance, but with significantly better computational efficiency, while also respecting flat state and input constraints, and guaranteeing stability.

关键词： Machine learning predictive control for nonlinear systems robotics

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robotic Kinesthesia: Estimating Object Geometry and Material With robot's Haptic Senses

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IEEE TRANSACTIONS ON HAPTICS 2024年第4期17卷 998-1005页

作者： Kim, Seung-Chan Ryu, Semin Sungkyunkwan Univ Dept Sport Interact Sci Machine Learning Syst Lab Suwon 16419 South Korea Hallym Univ Div Artificial Intelligence Convergence Intelligent Robot Lab Chunchon 24252 South Korea

Humans excel at determining the shape and material of objects through touch. Drawing inspiration from this ability, we propose a robotic system that incorporates haptic sensing capability into its artificial recognition system to jointly learn the shape and material types of an object. To achieve this, we employ a serially connected robotic arm and develop a supervised learning task that learns and classifies target surface geometry and material types using multivariate time-series data from joint torque sensors. Additionally, we propose a joint torque-to-position generation task to derive a one-dimensional surface profile based on torque measurements. Experimental results successfully validate the proposed torque-based classification and regression tasks, suggesting that a robotic system can employ haptic sensing (i.e., perceived force) from each joint to recognize material types and geometry, akin to human abilities.

关键词： robots robot sensing systems Shape Haptic interfaces Geometry Sensors Task analysis Kinesthetic sense robot perception joint torque geometry estimation material recognition sequence learning

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A Virtual Reality Framework for Human-robot Collaboration in Cloth Folding 22

A Virtual Reality Framework for Human-Robot Collaboration in...

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IEEE-RAS 22nd International Conference on Humanoid robots (Humanoids)

作者： Moletta, Marco Wozniak, Maciej K. Welle, Michael C. Kragic, Danica KTH Royal Inst Technol EECS Robot Percept & Learning Lab Stockholm Sweden

ISBN: (纸本)9798350303278

We present a virtual reality (VR) framework to automate the data collection process in cloth folding tasks. The framework uses skeleton representations to help the user define the folding plans for different classes of garments, allowing for replicating the folding on unseen items of the same class. We evaluate the framework in the context of automating garment folding tasks. A quantitative analysis is performed on three classes of garments, demonstrating that the framework reduces the need for intervention by the user. We also compare skeleton representations with RGB images in a classification task on a large dataset of clothing items, motivating the use of the proposed framework for other classes of garments.

关键词： Virtual reality

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Unified Guidance and Jerk-Level Dynamic Inversion for Accurate Position Control of Hybrid UAVs

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IEEE TRANSACTIONS ON robotICS 2025年 41卷 708-728页

作者： Rohr, David Andersson, Olov Lawrance, Nicholas Stastny, Thomas Siegwart, Roland Swiss Fed Inst Technol Autonomous Syst Lab CH-8092 Zurich Switzerland KTH Royal Inst Technol Div Robot Percept & Learning S-10044 Stockholm Sweden CSIRO Data61 Robot Percept & Auton Grp Pullenvale Qld 4069 Australia

By combining rotary- and fixed-wing flight, hybrid uncrewed aerial vehicles (H-UAVs) can uniquely address missions combining long-range aerial transport and precise ground-relative tasks, such as the placement or retrieval of payloads. However, to leverage their full maneuverability, first, the fundamentally different operating modes of rotary- and fixed-wing vehicles need to be unified and second, the system be controlled precisely despite complex aerodynamic effects. This work presents a general and lightweight, cascaded control formulation for such versatile and accurate operation of H-UAVs. First, a novel guidance law unifies ground- and air-relative position control modes typical for the individual flight regimes. Second, we formulate a jerk-level feedback-linearization to accurately track the guidance outputs despite model errors and disturbances. In extensive real flight tests with a tiltwing H-UAV, we demonstrate the versatile allocation of (hybrid) flight states and the overall accuracy enabled by the control system. Position errors remain below 0.5 m (one quarter of the wingspan) in the full flight envelope, including accelerated maneuvers up to 10 ms(2) and gusting wind reaching 12 m/s. Finally, the control system demonstrates exploiting hybrid flight for transport-related missions with a precise, in-flight pickup of a payload.

关键词： Payloads Kinematics Position control Control systems Aerodynamics Accuracy Computational modeling Vehicle dynamics Velocity control Trajectory tracking Aerial systems aircraft guidance convertible and hybrid uncrewed aerial vehicles (H-UAV) dynamic inversion mechanics and control path following trajectory tracking

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What Is the Impact of Releasing Code With Publications?: Statistics from the Machine learning, robotics, and Control Communities

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IEEE CONTROL SYSTEMS MAGAZINE 2024年第4期44卷 38-46页

作者： Zhou, Siqi Brunke, Lukas Tao, Allen Hall, Adam W. Bejarano, Federico Pizarro Panerati, Jacopo Schoellig, Angela P. Tech Univ Munich Learning Syst & Robot lab D-80333 Munich Germany Univ Toronto Inst Aerosp Studies Toronto ON Canada Univ Toronto Inst Aerosp Studies Learning Syst & Robot Lab N York ON M3H 5T6 Canada Technol Innovat Inst machine learning control Masdar City Abu Dhabi U Arab Emirates Tech Univ Munich Robot & Artificial Intelligence D-80333 Munich Germany Univ Toronto Inst Aerosp Studies Toronto ON Canada Vector Inst Toronto Toronto ON Canada

Open-sourcing research publications is a key enabler for the reproducibility of studies and the collective scientific progress of a research community. As all fields of science develop more advanced algorithms, we become more dependent on complex computational toolboxes-sharing research ideas solely through equations and proofs is no longer sufficient to communicate scientific developments. Over the past years, several efforts have highlighted the importance and challenges of transparent and reproducible research;code sharing is one of the key necessities in such efforts.

关键词： Codes Machine learning Reproducibility of results robots

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V3D-SLAM: Robust RGB-D SLAM in Dynamic Environments with 3D Semantic Geometry Voting

V3D-SLAM: Robust RGB-D SLAM in Dynamic Environments with 3D ...

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2024 International Conference on Intelligent robots and Systems

作者： Dang, Tuan Nguyen, Khang Huber, Manfred Univ Texas Arlington Dept Comp Sci & Engn Learning & Adapt Robot Lab Arlington TX 76013 USA

ISBN: (纸本)9798350377712;9798350377705

Simultaneous localization and mapping (SLAM) in highly dynamic environments is challenging due to the correlation complexity between moving objects and the camera pose. Many methods have been proposed to deal with this problem;however, the moving properties of dynamic objects with a moving camera remain unclear. Therefore, to improve SLAM's performance, minimizing disruptive events of moving objects with a physical understanding of 3D shapes and dynamics of objects is needed. In this paper, we propose a robust method, V3D-SLAM, to remove moving objects via two lightweight re-evaluation stages, including identifying potentially moving and static objects using a spatial-reasoned Hough voting mechanism and refining static objects by detecting dynamic noise caused by intra-object motions using Chamfer distances as similarity measurements. Through our experiment on the TUM RGB-D benchmark on dynamic sequences with ground-truth camera trajectories, the results show that our methods outperform most other recent state-of-the-art SLAM methods. Our source code is available at https://***/tuantdang/v3d-slam.

关键词： Object detection

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Volumetric Mapping with Panoptic Refinement using Kernel Density Estimation for Mobile robots

Volumetric Mapping with Panoptic Refinement using Kernel Den...

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2024 International Conference on Intelligent robots and Systems

作者： Khang Nguyen Tuan Dang Huber, Manfred Univ Texas Arlington Dept Comp Sci & Engn Learning & Adapt Robot Lab Arlington TX 76013 USA

ISBN: (纸本)9798350377712;9798350377705

Reconstructing three-dimensional (3D) scenes with semantic understanding is vital in many robotic applications. robots need to identify which objects, along with their positions and shapes, to manipulate them precisely with given tasks. Mobile robots, especially, usually use lightweight networks to segment objects on RGB images and then localize them via depth maps;however, they often encounter out-of-distribution scenarios where masks over-cover the objects. In this paper, we address the problem of panoptic segmentation quality in 3D scene reconstruction by refining segmentation errors using non-parametric statistical methods. To enhance mask precision, we map the predicted masks into a depth frame to estimate their distribution via kernel densities. The outliers in depth perception are then rejected without the need for additional parameters in an adaptive manner to out-of-distribution scenarios, followed by 3D reconstruction using projective signed distance functions (SDFs). We validate our method on a synthetic dataset, which shows improvements in both quantitative and qualitative results for panoptic mapping. Through real-world testing, the results furthermore show our method's capability to be deployed on a real-robot system. Our source code is available at: https://***/mkhangg/refined_panoptic mapping.

关键词： Mobile robots

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Adaptive Control of Quadrotor under Actuator Loss and Unknown State-dependent Dynamics 20

Adaptive Control of Quadrotor under Actuator Loss and Unknow...

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IEEE 20th International Conference on Automation Science and Engineering (CASE)

作者： Gupta, Saksham Sharma, Amitabh Mulgundkar, Aditya Yadav, Rishabh Dev Roy, Spandan Int Inst Informat Technol Hyderabad IIIT H Robot Res Ctr Hyderabad India Univ Manchester Robot Learning & Control RLC Lab Manchester Lancs England

ISBN: (纸本)9798350358513;9798350358520

This paper examines the enhancement of quadrotor efficiency through adaptive Fault-Tolerant Control (FTC) in quadrotors amidst operational uncertainties and component inefficiency. State-of-the-art adaptive FTC strategies often assume the uncertainties to be bounded by a constant a priori, leading to ignorance of state-dependent uncertainties. Unattended state-dependent uncertainties that persist can lead to instability, especially under actuator faults. The proposed adaptive FTC offers actuator fault mitigation while tackling unknown (state-dependent) uncertainties via suitably designed adaptive laws. In addition, real-time fault detection and control allocation are used simultaneously to avoid conservative control application. The closed-loop system stability is studied analytically and the effectiveness of the proposed solution is verified on a realistic simulator in comparison to the state of the art.

关键词： Adaptive control systems

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Policy Optimization for Waste Crane Automation From Human Preferences

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IEEE ACCESS 2023年 11卷 126524-126541页

作者： Kwon, Yuhwan Sasaki, Hikaru Hirabayashi, Terushi Kawabata, Kaoru Matsubara, Takamitsu Nara Inst Sci & Technol Div Informat Sci Robot Learning Lab Nara 6300192 Japan Hitachi Zosen Corp Osaka 5598559 Japan

This research introduces a novel approach to optimizing control policies for waste cranes operating at waste-to-energy plants. Although previous methods forced people to define evaluation functions for automation, such design works in actual environments can often be challenging due to limited sensors and design difficulties. This paper aims to establish a methodology that achieves automation by having people respond to interactive pairwise comparison queries, which is relatively simple compared to design work. On the other hand, considering such automation, it becomes imperative to address the increased sample cost associated with slow crane operation and the complexities of decision-making due to waste inhomogeneity. Our proposed Preferential Bayesian Policy Optimization (PBPO) optimizes control policies with a small number of queries using Preference-based Bayesian optimization (PbBO) and mitigates the difficulty of decision-making by providing human evaluators who have an option to skip queries. We also incorporate a query synthesis mechanism to enhance query efficiency that generates a new preference relation from the skipped queries. PBPO's effectiveness was validated with a scattering task employed in previous studies. Experimental results with simulated evaluators show the effectiveness of the PBPO and query synthesis. Furthermore, results with actual human evaluators indicate that our proposed method performs as well as the Bayesian optimization (BO) method, which requires an evaluation function.

关键词： Cranes Optimization Bayes methods Automation Task analysis Linear programming Videos Human in the loop Waste management Bayesian methods cranes humans in the loop interactive systems optimization methods waste handling

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Exploring Task-Level Contingent Mediations for Vocabulary Instruction across robot, Virtual, and Human Teachers 33

Exploring Task-Level Contingent Mediations for Vocabulary In...

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33rd IEEE International Conference on robot and Human Interactive Communication (IEEE RO-MAN) - Embracing Human-Centered HRI

作者： Louie, Wing-Yue Geoffrey Christ, Tanya Shahverdi, Pourya Rousso, Katelyn Dallas, Evan Tyshka, Alexander Wowra, Amanda Barnett, Kendra Bakhoda, Iman Oakland Univ Intelligent Robot Lab Rochester MI 48063 USA Oakland Univ Dept Teaching & Learning Rochester MI 48063 USA

ISBN: (纸本)9798350375039;9798350375022

Social robots are being introduced in a variety of educational domains with great success. These social robots are often designed by drawing inspiration from practices held by human teachers. Contingent mediations are a prime example of a high-quality teaching practice that supports better outcomes in human-human teaching. This can inform the design of social robots. Current research on robot use in education has focused on curriculum-level contingent mediations where the difficulty level of subsequent tasks are adjusted to the current capabilities of a learner. However, task-level contingent mediations that provide support for students' learning during a specific task remain unexplored. This research investigates whether patterns of task-level mediations differ between a robot, virtual, and human agent, as well as their effects on learning outcomes. To investigate these research questions, we designed instruction that utilizes contingent mediational flows, based on formative assessment data, to be delivered by a human, robot, and virtual agent to teach grade 3-5 children science words. We identified 23 unique instructional patterns. Then, we compared these patterns across teaching agents and their effects on learning outcomes. Overall, our study demonstrated that high-quality contingent mediations support children's science vocabulary in learning regardless of the teaching agent.

关键词： Chatbots

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