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RL + Model-Based Control: Using On-Demand Optimal Control to Learn Versatile Legged Locomotion

IEEE robotics and Automation Letters 2023年第10期8卷 6619-6626页

作者： Kang, Dongho Cheng, Jin Zamora, Miguel Zargarbashi, Fatemeh Coros, Stelian ETH Zurich Computational Robotics Lab in the Department of Computer Science Zurich8092 Switzerland

This letter presents a control framework that combines model-based optimal control and reinforcement learning (RL) to achieve versatile and robust legged locomotion. Our approach enhances the RL training process by incorporating on-demand reference motions generated through finite-horizon optimal control, covering a broad range of velocities and gaits. These reference motions serve as targets for the RL policy to imitate, leading to the development of robust control policies that can be learned with reliability. Furthermore, by utilizing realistic simulation data that captures whole-body dynamics, RL effectively overcomes the inherent limitations in reference motions imposed by modeling simplifications. We validate the robustness and controllability of the RL training process within our framework through a series of experiments. In these experiments, our method showcases its capability to generalize reference motions and effectively handle more complex locomotion tasks that may pose challenges for the simplified model, thanks to RL's flexibility. Additionally, our framework effortlessly supports the training of control policies for robots with diverse dimensions, eliminating the necessity for robot-specific adjustments in the reward function and hyperparameters. © 2016 IEEE.

关键词： Reinforcement learning

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Medical Recommendation System Based on Collaborative Filtering Algorithms

Medical Recommendation System Based on Collaborative Filteri...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Bogdanova, Diana Shakhmametova, Gyuzel Niiazgulov, Albert Ufa University of Science and Technology Department of Computer Science and Robotics Ufa Russia

ISBN: (纸本)9798350395044

The article deals with the issues of designing a medical recommendation system based on the collaborative filtering algorithm. The problem under consideration is a large number of medical errors, which are one of the most common causes of patient deaths. In Russia alone, more than 70 thousand people suffer severe complications every year for this reason. The aim of the research is to analyze the possibility of designing and implementing software that reduces the probability of occurrence of medical errors and allows to realize the automation of prescribing by a doctor of the optimal treatment for a particular patient. The article analyzes the subject area and problem situation, identifies the causes of a large number of medical errors in public medical institutions in Russia. The problem statement is considered, mathematical support of the recommendation system based on the algorithm of collaborative filtering is proposed. The analysis of methods for solving the problem is given. The implementation of the recommendation system in the Python programming language is considered, the results of research in the form of a computational experiment are analyzed and interpreted. According to the results of the research, it is concluded that the proposed implementation of the medical recommendation system based on the collaborative filtering algorithm can be used in the further design of a full-fledged system and then used in medical institutions as a part of the system. © 2024 IEEE.

关键词： Collaborative filtering

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A trajectory-level robot skill learning method based on fuzzy systems

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science China(Information sciences) 2025年第5期68卷 38-52页

作者： Chao ZENG Zhen DENG Boyang TI Yongsheng GAO Department of Computer Science University of Liverpool School of Mechanical Engineering and Automation Fuzhou University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics and System Harbin Institute of Technology

Learning from demonstration(LfD) allows for the effective transfer of human manipulation skills to a robot by building a model that represents these skills based on a limited number of demonstrated ***,a skilllearning model that can comprehensively satisfy multiple requirements,such as computational complexity,modeling accuracy,trajectory smoothness,and robustness,is still ***,this work aims to provide such a model by employing fuzzy ***,we introduce an LfD model named Takagi-Sugeno-Kang fuzzy system-based movement primitives(TSKFMPs),which exploits the advantages of the fuzzy theory for effective robotic imitation learning of human *** work formulates the TSK fuzzy system and gradient descent(GD) as imitation learning models,leveraging recent advancements in GD-based optimization for fuzzy *** study takes a two-step strategy.(ⅰ) The input-output relationships of the model are established using TSK fuzzy systems based on demonstration *** this way,the skill is encoded by the model parameter in the latent space.(ⅱ) GD is used to optimize the model parameter to increase the modeling accuracy and trajectory *** further explain how learned trajectories are adapted to new task scenarios through local *** conduct multiple tests using an open dataset to validate our method,and the results demonstrate performance comparable with those of other ***,we implement it in a real-world case study.

关键词： robot skill learning movement primitives TSK fuzzy systems gradient descent robotics

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A Synchronized Task Formulation for Robotic Convoy Operations

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IEEE robotics and Automation Letters 2025年第7期10卷 6808-6815页

作者： Noren, Charles Vundurthy, Bhaskar Scherer, Sebastian Choset, Howie Travers, Matthew Carnegie Mellon University Robotics Institute School of Computer Science PittsburghPA United States

Future ground logistics missions will require multiple robots to travel in a convoy between locations. As each location may require a different number of robots (e.g. resupply vehicles), these missions will require a mutable convoy formation structure that may be divided to meet operational needs at each location. We model this mission type by modifying the vehicle routing problem with multiple synchronizations (VRPMS) to enforce convoy constraints (VRPMS-CC). This centralized approach to organizing and routing convoys is represented as a graph-based routing problem and then solved as a mixed integer program. A solution of the VRPMS-CC forms convoys by ensuring that agents participating in the same convoy remain spatially and temporally coupled, traversing the same edge of the graph simultaneously. We demonstrate our approach through numerical studies, where we route up to six simulated agents through twenty convoying tasks, and on robotic hardware. These demonstrations motivate two further contributions to specialize our approach to robotic systems. We introduce: 1) a warm-starting heuristic that improves solver times by up to eighty-nine percent and 2) an online multi-depot variant of the VRPMS-CC that responds to a priori unknown impassable environmental obstacles. © 2016 IEEE.

关键词： Multipurpose robots

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A Novel Assistive Imaging Device for Colon Map Reconstruction

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IEEE robotics and Automation Letters 2024年第12期9卷 10780-10787页

作者： Dei, Neri Niccolo Bano, Sophia Vasconcelos, Francisco Consumi, Vanni Stoyanov, Danail Stilli, Agostino Ciuti, Gastone Scuola Superiore sant'Anna Bio Robotics Institute Department of Excellence in Robotics and AI Pisa Italy University College London Hawkes Institute Department of Computer Science London United Kingdom

Colonoscopy, recognised as the gold standard for diagnosing and treating colorectal cancer, faces limitations that may result in overlooking some colonic regions. This can lead to missed lesions and interval cancer, leading to incomplete treatment. Addressing this challenge, the authors present a novel Assistive Imaging Device for Colon Map Reconstruction (AID-CMR) to support clinicians in visualising the entire colonic mucosa and identifying areas that may have been missed during the procedure. The device is comprised of an overtube tooled with an imaging module on its distal end, which can be moved about a threaded silicone sleeve. The imaging module houses inflatable silicone chambers to distend the lumen, a side-viewing miniaturised full-HD camera with LEDs, a magnetic encoder, and an irrigation channel for camera cleansing. Before intervention, the device can be installed on standard colonoscopes or other tethered endoscopes. During the intervention, the device can be operated to (1) reconstruct assistive maps of the organ, (2) visualise the full anatomy, and (3) increase disease detection. Moreover, these maps may be employed for the quantification of missed regions during standard inspection. This paper introduces a proof of concept device and discusses locomotion and reconstruction tests conducted in two in-vitro conditions. These tests reveal overall procedural feasibility in terms of locomotion, exerted forces and colon map reconstruction, preserving polyp morphology. © 2024 IEEE.

关键词： Medical robotics

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Beyond NeRF Underwater: Learning Neural Reflectance Fields for True Color Correction of Marine Imagery

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IEEE robotics and Automation Letters 2023年第10期8卷 6467-6474页

作者： Zhang, Tianyi Johnson-Roberson, Matthew Carnegie Mellon University Robotics Institute School of Computer Science PittsburghPA15213 United States

Underwater imagery often exhibits distorted coloration as a result of light-water interactions, which complicates the study of benthic environments in marine biology and geography. In this research, we propose an algorithm to restore the true color (albedo) in underwater imagery by jointly learning the effects of the medium and neural scene representations. Our approach models water effects as a combination of light attenuation with distance and backscattered light. The proposed neural scene representation is based on a neural reflectance field model, which learns albedos, normals, and volume densities of the underwater environment. We introduce a logistic regression model to separate water from the scene and apply distinct light physics during training. Our method avoids the need to estimate complex backscatter effects in water by employing several approximations, enhancing sampling efficiency and numerical stability during training. The proposed technique integrates underwater light effects into a volume rendering framework with end-to-end differentiability. Experimental results on both synthetic and real-world data demonstrate that our method effectively restores true color from underwater imagery, outperforming existing approaches in terms of color consistency. © 2016 IEEE.

关键词： Reflection

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Existing Approaches & A Proposal to Content Analysis of Pilots' Speech Activities

Existing Approaches & A Proposal to Content Analysis of Pilo...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Bogdanova, Diana Giniiatullin, Azamat Batth, Jaspreet Singh Ufa University of Science and Technology Institute of Computer Science Mathematics and Robotics Ufa Russia Chandigarh University Computer Science and Engineering Chandigarh India

ISBN: (纸本)9798350395044

Content analysis of pilot speech is a key tool for improving flight safety as it allows to identify potential problems in communication between pilots, dispatchers and crew. The development of efficient algorithms for analyzing pilot speech contributes to improving communication skills, preventing incidents and improving overall airline safety. The research focuses on the possibility of applying machine learning techniques and deep neural networks to analyze speech data, which opens new perspectives for information processing and interpretation. The results of the study emphasize the interdisciplinary nature of the topic and suggest new methodological approaches, including the use of fuzzy set theory and fuzzy logic systems to handle uncertainty in speech data, which in turn helps to improve communication performance in complex and stressful flight conditions. The finalization of the work includes the evaluation of the developed methods and approaches in terms of their applicability and impact on improving flight safety, incorporating model training results and addressing testing errors. © 2024 IEEE.

关键词： Deep neural networks

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Pose Estimation of an Autonomous Drilling Excavator

Pose Estimation of an Autonomous Drilling Excavator

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Joint International Conference of the 14th International Conference on Mechanisms and Mechanical Transmissions and 26th International Conference on robotics, MTM and robotics 2024

作者： Suiker, Marcel Husemann, Jörg Berns, Karsten Robotics Research Lab Department of Computer Science University of Kaiserslautern-Landau Kaiserslautern Germany

ISBN: (纸本)9783031875366

Due to the increase of automation in the field of construction, more and more automation approaches for excavators have been developed. A special case of excavators are drilling excavators. Therefore, a precise pose estimation is an essential task to achieve automation of the drilling process and has not been the subject of interest in research until now. This paper presents a new approach to calculating a pose of the excavator’s undercarriage for a given drilling position. The positioning uses information on the excavator’s workspace considering its kinematics, the terrain, and obstacles at the construction site. Essential to this approach is the computation of the terrain gradient, as well as the computation of the largest inscribed rectangle inside a computed candidate area. Experiments showed that the presented solution yields good results but takes a long time to find them. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Excavators

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Non-rigid Relative Placement through 3D Dense Diffusion 8

Non-rigid Relative Placement through 3D Dense Diffusion

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8th Conference on Robot Learning, CoRL 2024

作者： Cai, Eric Donca, Octavian Eisner, Ben Held, David Robotics Institute School of Computer Science Carnegie Mellon University United States

The task of "relative placement" is to predict the placement of one object in relation to another, e.g. placing a mug onto a mug rack. Through explicit object-centric geometric reasoning, recent methods for relative placement have made tremendous progress towards data-efficient learning for robot manipulation while generalizing to unseen task variations. However, they have yet to represent deformable transformations, despite the ubiquity of non-rigid bodies in real world settings. As a first step towards bridging this gap, we propose "cross-displacement" - an extension of the principles of relative placement to geometric relationships between deformable objects - and present a novel vision-based method to learn cross-displacement through dense diffusion. To this end, we demonstrate our method's ability to generalize to unseen object instances, out-of-distribution scene configurations, and multimodal goals on multiple highly deformable tasks (both in simulation and in the real world) beyond the scope of prior works. Supplementary information and videos can be found at our website. © 2024 Proceedings of Machine Learning Research.

关键词： Geometry

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FlowBotHD: History-Aware Diffuser Handling Ambiguities in Articulated Objects Manipulation 8

FlowBotHD: History-Aware Diffuser Handling Ambiguities in Ar...

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8th Conference on Robot Learning, CoRL 2024

作者： Li, Yishu Leng, Wen Hui Fang, Yiming Eisner, Ben Held, David Robotics Institute School of Computer Science Carnegie Mellon University United States

We introduce a novel approach for manipulating articulated objects which are visually ambiguous, such doors which are symmetric or which are heavily occluded. These ambiguities can cause uncertainty over different possible articulation modes: for instance, when the articulation direction (e.g. push, pull, slide) or location (e.g. left side, right side) of a fully closed door are uncertain, or when distinguishing features like the plane of the door are occluded due to the viewing angle. To tackle these challenges, we propose a history-aware diffusion network that can model multi-modal distributions over articulation modes for articulated objects;our method further uses observation history to distinguish between modes and make stable predictions under occlusions. Experiments and analysis demonstrate that our method achieves state-of-art performance on articulated object manipulation and dramatically improves performance for articulated objects containing visual ambiguities. Our project website is available at https://***/. © 2024 Proceedings of Machine Learning Research.

关键词： Ambiguity Articulated Objects Diffusion

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