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N-Dimensional Reduction Algorithm for learning from demonstration Path Planning

SENSORS 2025年第7期25卷 2145-2145页

作者： Manrique-Cordoba, Juliana de la Casa-Lillo, Miguel Angel Sabater-Navarro, Jose Maria Miguel Hernandez Univ Elche Bioengn Inst Elche 03202 Spain

This paper presents an n-dimensional reduction algorithm for learning from demonstration (LfD) for robotic path planning, addressing the complexity of high-dimensional data. The method extends the Douglas-Peucker algorithm by incorporating velocity and orientation alongside position, enabling more precise trajectory simplification. A magnitude-based normalization process preserves proportional relationships across dimensions, and the reduced dataset is used to train Hidden Markov Models (HMMs), where continuous trajectories are discretized into identifier sequences. The algorithm is evaluated in 2D and 3D environments with datasets combining position and velocity. The results show that incorporating additional dimensions significantly enhances trajectory simplification while preserving key information. Additionally, the study highlights the importance of selecting appropriate encoding parameters to achieve optimal resolution. The HMM-based models generated new trajectories that retained the patterns of the original demonstrations, demonstrating the algorithm's capacity to generalize learned behaviors for trajectory learning in high-dimensional spaces.

关键词： learning from demonstration hidden Markov models data reduction Douglas-Peucker algorithm high-dimensional data encoding

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Improved Gaussian mixture model and Gaussian mixture regression for learning from demonstration based on Gaussian noise scattering

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ADVANCED ENGINEERING INFORMATICS 2025年 65卷

作者： Feng, Chunhua Liu, Zhuang Li, Weidong Lu, Xin Jing, Yanguo Ma, Yongsheng Univ Shanghai Sci & Technol Sch Mech Engn Shanghai Peoples R China Leeds Trinity Univ Sch Comp Sci Leeds England Southern Univ Sci & Technol Dept Mech & Energy Engn Shenzhen Peoples R China

learning from demonstration (LfD) is an effectual approach for robots to acquire new skills by implementing intuitive learning through imitating human demonstration. As one of the mainstream learning models for LfD, Gaussian mixture modeling (GMM) and Gaussian mixture regression (GMR) exhibit the advantages of ease of use and robust learning capabilities. To further improve the learning and regression performance of GMM/GMR, in this paper, improved GMM/GMR based on a Gaussian noise scattering strategy is designed. The main contributions of this study include: 1) the Gaussian noise scattering strategy is developed to eliminate the requirement of creating multiple demonstrations and overcome the jitter and sharp-turning defects of the demonstration;2) based on a new evaluation criterion IBF and the sparrow search algorithm (SSA), GMM/GMR is optimized to achieve the balance of feature retention of the demonstration and the smoothness of the reproduced solution. Experimental results show that with the Gaussian noise scattering strategy, the geometric similarity of the reproduced solution and the demonstration increased for approximately 33.16 %, and the smoothness improved for 19.83 %. The challenges of underfitting and overfitting in GMM/GMR were effectively mitigated after incorporating the evaluation criterion IBF and leveraging SSA. This demonstrates the potential applicability of the improved GMM/GMR in practical industrial scenarios.

关键词： learning from demonstration Gaussian Mixture Modeling Gaussian Mixture Regression Sparrow Search Algorithm

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learning from demonstration for semi-autonomous teleoperation

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AUTONOMOUS ROBOTS 2019年第3期43卷 713-726页

作者： Havoutis, Ioannis Calinon, Sylvain Univ Oxford Oxford Robot Inst Dept Engn Sci 17 Parks Rd Oxford OX1 3PJ England Idiap Res Inst CH-1920 Martigny Switzerland

Teleoperation in domains such as deep-sea or space often requires the completion of a set of recurrent tasks. We present a framework that uses a probabilistic approach to learn from demonstration models of manipulation tasks. We show how such a framework can be used in an underwater ROV teleoperation context to assist the operator. The learned representation can be used to resolve inconsistencies between the operator's and the robot's space in a structured manner, and as a fall-back system to perform previously learned tasks autonomously when teleoperation is not possible. We evaluate our framework with a realistic ROV task on a teleoperation mock-up with a group of volunteers, showing a significant decrease in time to complete the task when our approach is used. In addition, we illustrate how the system can execute previously learned tasks autonomously when the communication with the operator is lost.

关键词： learning from demonstration Teleoperation Shared autonomy Manipulation in ROVs

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learning from demonstration using products of experts: Applications to manipulation and task prioritization

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INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH 2022年第2期41卷 163-188页

作者： Pignat, Emmanuel Silverio, Joao Calinon, Sylvain Idiap Res Inst Rue Marconi 19 CH-1920 Martigny Switzerland Ecole Polytech Fed Lausanne Lausanne Switzerland

Probability distributions are key components of many learning from demonstration (LfD) approaches, with the spaces chosen to represent tasks playing a central role. Although the robot configuration is defined by its joint angles, end-effector poses are often best explained within several task spaces. In many approaches, distributions within relevant task spaces are learned independently and only combined at the control level. This simplification implies several problems that are addressed in this work. We show that the fusion of models in different task spaces can be expressed as products of experts (PoE), where the probabilities of the models are multiplied and renormalized so that it becomes a proper distribution of joint angles. Multiple experiments are presented to show that learning the different models jointly in the PoE framework significantly improves the quality of the final model. The proposed approach particularly stands out when the robot has to learn hierarchical objectives that arise when a task requires the prioritization of several sub-tasks (e.g. in a humanoid robot, keeping balance has a higher priority than reaching for an object). Since training the model jointly usually relies on contrastive divergence, which requires costly approximations that can affect performance, we propose an alternative strategy using variational inference and mixture model approximations. In particular, we show that the proposed approach can be extended to PoE with a nullspace structure (PoENS), where the model is able to recover secondary tasks that are masked by the resolution of tasks of higher-importance.

关键词： product of experts learning from demonstration task prioritization nullspace learning and control

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learning from demonstration Based on Environmental Constraints

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第4期7卷 10938-10945页

作者： Li, Xing Brock, Oliver Tech Univ Berlin Robot & Biol Lab D-10587 Berlin Germany Tech Univ Berlin Sci Intelligence SCIoI D-10587 Berlin Germany Tech Univ Berlin Cluster Excellence D-10587 Berlin Germany

We present a novel learning from demonstration approach which uses environmental constraints as the underlying representation to interpret and reproduce demonstrations. This representation based on environmental constraints separates the information that facilitates generalization from the information specific to object instances. Combined with adaptive controllers which fill in the instance-specific details during execution through explorative interaction, our approach generalizes from a single demonstration on an articulated object to different instances of the same object type. We test our approach in real-world experiments on contact-rich manipulation, using a series of mechanical locks as well as drawers and doors. The high success rate of 95% across all of these experiments provides strong evidence that environmental constraints are a powerful inductive bias for general and robust learning from demonstration.

关键词： learning from demonstration perception-action coupling

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learning from demonstration Based on a Classification of Task Parameters and Trajectory Optimization

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2020年第2期99卷 261-275页

作者： Vidakovic, Josip Jerbic, Bojan Sekoranja, Bojan Svaco, Marko Suligoj, Filip Univ Zagreb Fac Mech Engn & Naval Architecture Dept Robot & Prod Syst Automat Zagreb Croatia

learning from demonstration involves the extraction of important information from demonstrations and the reproduction of robot action sequences or trajectories with generalization capabilities. Task parameters represent certain dependencies observed in demonstrations used to constrain and define a robot action because of the infinite nature of the state-space environment. We present the methodology for learning from demonstration based on a classification of task parameters. The classified task parameters are used to construct a cost function, responsible for describing the demonstration data. For reproduction we propose a novel trajectory optimization that is able to generate a simplified version of the trajectory for different configurations of the task parameters. As the last step before reproduction on a real robotic arm we approximate this trajectory with a Dynamic movement primitive (DMP) - based system to retrieve a smooth trajectory. Results obtained for trajectories with three degrees of freedom (two translations and one rotation) show that the system is able to encode multiple task parameters from a low number of demonstrations and generate trajectories that are collision free.

关键词： learning from demonstration Task parameterized movement Trajectory optimization Robot trajectory

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learning from demonstration and adaptation of biped locomotion

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ROBOTICS AND AUTONOMOUS SYSTEMS 2004年第2-3期47卷 79-91页

作者： Nakanishi, J Morimoto, J Endo, G Cheng, G Schaal, S Kawato, M ATR Computat Neurosci Labs Seika 6190288 Japan Japan Sci & Technol Agcy ICORP Seika 6190288 Japan Sony Corp Shinagawa Ku Tokyo 1410001 Japan Univ So Calif Dept Comp Sci Los Angeles CA 90089 USA

In this paper, we introduce a framework for learning biped locomotion using dynamical movement primitives based on non-linear oscillators. Our ultimate goal is to establish a design principle of a controller in order to achieve natural human-like locomotion. We suggest dynamical movement primitives as a central pattern generator (CPG) of a biped robot, an approach we have previously proposed for learning and encoding complex human movements. Demonstrated trajectories are learned through movement primitives by locally weighted regression, and the frequency of the learned trajectories is adjusted automatically by a novel frequency adaptation algorithm based on phase resetting and entrainment of coupled oscillators. Numerical simulations and experimental implementation on a physical robot demonstrate the effectiveness of the proposed locomotion controller. (C) 2004 Elsevier B.V. All rights reserved.

关键词： biped locomotion learning from demonstration dynamical movement primitives phase resetting frequency adaptation

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learning from demonstration for Autonomous Navigation in Complex Unstructured Terrain

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INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH 2010年第12期29卷 1565-1592页

作者： Silver, David Bagnell, J. Andrew Stentz, Anthony Carnegie Mellon Univ Pittsburgh PA 15201 USA

Rough terrain autonomous navigation continues to pose a challenge to the robotics community. Robust navigation by a mobile robot depends not only on the individual performance of perception and planning systems, but on how well these systems are coupled. When traversing complex unstructured terrain, this coupling (in the form of a cost function) has a large impact on robot behavior and performance, necessitating a robust design. This paper explores the application of learning from demonstration to this task for the Crusher autonomous navigation platform. Using expert examples of desired navigation behavior, mappings from both online and offline perceptual data to planning costs are learned. Challenges in adapting existing techniques to complex online planning systems and imperfect demonstration are addressed, along with additional practical considerations. The benefits to autonomous performance of this approach are examined, as well as the decrease in necessary designer effort. Experimental results are presented from autonomous traverses through complex natural environments.

关键词： Field robotics mobile robotics autonomous navigation learning from demonstration imitation learning inverse reinforcement learning

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learning from demonstration in robots: Experimental comparison of neural architectures

Learning from demonstration in robots: Experimental comparis...

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20th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM)

作者： Suleman, Muhammad Umar Awais, Mian M. Lahore Univ Management Sci Dept Comp Sci Sch Sci & Engn DHA Lahore 54792 Pakistan

Robots have played an important role in the automation of computer aided manufacturing. The classical robot control implementation involves an expensive key step of model-based programming. An intuitive way to reduce this expensive exercise is to replace programming with machine learning of robot actions from demonstration where a (learner) robot learns an action by observing a demonstrator robot performing the same. To achieve this learning from demonstration (LFD) different machine learning techniques such as Artificial Neural Networks (ANN), Genetic Algorithms, Hidden Markov Models, Support Vector Machines, etc. can be used. This piece of work focuses exclusively on ANNs. Since ANNs have many standard architectural variations divided into two basic computational categories namely the recurrent networks and feed-forward networks, representative networks from each have been selected for study, i.e. Feed Forward Multilayer Perceptron (FF) network for feed-forward networks category and Elman (EL), and Nonlinear Autoregressive Exogenous Model (NARX) networks for the recurrent networks category. The main objective of this work is to identify the most suitable neural architecture for application of LFD in learning different robot actions. The sensor and actuator streams of demonstrated action are used as training data for ANN learning. Consequently, the learning capability is measured by comparing the error between demonstrator and corresponding learner streams. To achieve fairness in comparison three steps have been taken. First, Dynamic Time Warping is used to measure the error between demonstrator and learner streams, which gives resilience against translation in time. Second, comparison statistics are drawn between the best, instead of weight-equal, configurations of competing architectures so that learning capability of any architecture is not forced handicap. Third, each configuration's error is calculated as the average of ten trials of all possible learning sequ

关键词： learning from demonstration Artificial neural network Machine learning Robot learning

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learning from demonstration Using Variational Bayesian Inference 28th

Learning from Demonstration Using Variational Bayesian Infer...

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28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE)

作者： Hussein, Mostafa Mohammed, Yasser Ali, Samia A. Assiut Univ Asyut Egypt

ISBN: (纸本)9783319190662;9783319190655

learning from demonstration (LFD) is an active area of research in robotics. There are many approaches to LFD. One of the most widely used approaches is the combination of Gaussian Mixture Model learning for modeling and Gaussian Mixture Regression for behavior generation (GMM/GMR) due to its advantages including easy learning using Expectation Maximization and the simplicity of serializing learned behaviors as well as the ability to model internal correlations and constraints within the task. A critical parameter that affects the accuracy of learned behavior in GMM/GMR is the number of components in the mixture. A handful of approaches for selecting this number can be found in the literature including classical model selection methods like Bayesian Information Criteria and Akaik Information Criteria and more advanced methods including Dirichlet Process modeling. These approaches are either wasteful of computational resources or hard to implement. This paper introduces a LfD approach which uses GMM with a variational Bayesian Inference (VB) approach to select the number of Gaussians that best fit the data. The proposed method is compared to classical model selection approaches and a recently proposed symbolization based method and is shown to provide an appropriate balance between execution speed and model accuracy.

关键词： learning from demonstration Model selection Variational bayesian inference

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