Search Results (13)

Human motion provides a valuable source of information for robotic skill acquisition, and Learning from Demonstration (LfD) has been widely adopted as an intuitive paradigm for enabling robots to learn tasks from human demonstrations. However, the lack of an explicit representation of transferable motion knowledge significantly limits the adaptability of LfD when tasks involve varying spatial constraints or environmental configurations. To address this challenge, this paper proposes a motion representation framework based on two fundamental properties of motion and introduces a novel Motion Knowledge Transferring Gaussian Mixture Model (MKT-GMM) for trajectory generalization across related tasks. In the proposed framework, demonstration trajectories from a source task are first collected through kinesthetic teaching and encoded using a Gaussian Mixture Model (GMM), where each Gaussian component represents a local motion primitive. Transferable motion knowledge is captured by jointly preserving the statistical characteristics of individual motion primitives and the geometric relationships between adjacent primitives. For a target task in which only task constraints are specified, the learned motion knowledge is transferred by adapting the GMM parameters through affine transformations combined with constraint-error minimization, enabling feasible trajectories to be generated without additional demonstrations or model retraining. The final motions are reconstructed using Gaussian Mixture Regression (GMR), ensuring smooth and consistent trajectory generation. To further improve the robustness of trajectory transfer, a pseudo via-point mechanism is introduced to automatically generate intermediate constraints when explicit via-points are unavailable. Experiments conducted on a robotic manipulation platform, including handwriting motion learning and pick-and-place tasks under varying task configurations, demonstrate that the proposed method effectively captures transferable motion knowledge and achieves reliable trajectory generalization for previously unseen tasks. Full article

To address the unique gait characteristics of individuals, this paper proposes a personalized trajectory planning method for exoskeleton robots. Gait trajectory data is collected using an inertial motion capture system, and personalized musculoskeletal models are built via OpenSim 4.5 to calculate joint angle data. The Trainable Time Warping (TTW) algorithm is used to align data from different time series, followed by Gaussian Mixture Model and Gaussian Mixture Regression (GMM-GMR) to fit multiple data sequences. The fitted joint angle curves are expanded using Fourier series to obtain Fourier coefficients. A Gaussian Process Regression (GPR) model is then established to map anthropometric parameters (thigh length, calf length, weight) to Fourier coefficients, which are used to plan gait trajectories. Experiments conducted with a lower limb exoskeleton robot demonstrate that this trajectory planning method achieves accurate trajectory tracking, with a root mean square error (RMSE) of 1.82° for the hip joint and 1.89° for the knee joint. The method also yielded a high user satisfaction rate of 90%, confirming its effectiveness in generating personalized and comfortable gait patterns. Full article

Learning from demonstration with multiple executions must contend with time warping, sensor noise, and alternating quasi-stationary and transition phases. We propose a label-free pipeline that couples unsupervised segmentation, duration-explicit alignment, and probabilistic encoding. A dimensionless multi-feature saliency (velocity, acceleration, curvature, direction-change rate) yields scale-robust keyframes via persistent peak–valley pairs and non-maximum suppression. A hidden semi-Markov model (HSMM) with explicit duration distributions is jointly trained across demonstrations to align trajectories on a shared semantic time base. Segment-level probabilistic motion models (GMM/GMR or ProMP, optionally combined with DMP) produce mean trajectories with calibrated covariances, directly interfacing with constrained planners. Feature weights are tuned without labels by minimizing cross-demonstration structural dispersion on the simplex via CMA-ES. Across UAV flight, autonomous driving, and robotic manipulation, the method reduces phase-boundary dispersion by 31% on UAV-Sim and by 30–36% under monotone time warps, noise, and missing data (vs. HMM); improves the sparsity–fidelity trade-off (higher time compression at comparable reconstruction error) with lower jerk; and attains nominal 2σ coverage (94–96%), indicating well-calibrated uncertainty. Ablations attribute the gains to persistence plus NMS, weight self-calibration, and duration-explicit alignment. The framework is scale-aware and computationally practical, and its uncertainty outputs feed directly into MPC/OMPL for risk-aware execution. Full article

This paper presents a real-time framework for bilateral gait reconstruction and adaptive joint control using sparse inertial sensing. The system estimates full lower-limb motion from a single-side inertial measurement unit (IMU) by applying a pipeline that includes signal smoothing, temporal alignment via Dynamic Time Warping (DTW), and motion modeling using Gaussian Mixture Models with Regression (GMM-GMR). Contralateral leg trajectories are inferred using both ideal and adaptive symmetry-based models to capture inter-limb variations. The reconstructed motion serves as reference input for joint-level control. A classical Proportional–Integral–Derivative (PID) controller is first evaluated, demonstrating satisfactory results under simplified dynamics but notable performance loss when virtual stiffness and gravity compensation are introduced. To address this, an adaptive fuzzy PID controller is implemented, which dynamically adjusts control gains based on real-time tracking error through a fuzzy inference system. This approach enhances control stability and motion fidelity under varying conditions. The combined estimation and control framework enables accurate bilateral gait tracking and smooth joint control using minimal sensing, offering a practical solution for wearable robotic systems such as exoskeletons or smart prosthetics. Full article

Robot learning from human demonstration pioneers an effective mapping paradigm for endowing robots with human-like operational capabilities. This paper proposes a bio-signal-guided robot adaptive stiffness learning framework grounded in the conclusion that muscle activation of the human arm is positively correlated with the endpoint stiffness. First, we propose a human-teleoperated demonstration platform enabling real-time modulation of robot end-effector stiffness by human tutors during operational tasks. Second, we develop a dual-stage probabilistic modeling architecture employing the Gaussian mixture model and Gaussian mixture regression to model the temporal–motion correlation and the motion–sEMG relationship, successively. Third, a real-world experiment was conducted to validate the effectiveness of the proposed skill transfer framework, demonstrating that the robot achieves online adaptation of Cartesian impedance characteristics in contact-rich tasks. This paper provides a simple and intuitive way to plan the Cartesian impedance parameters, transcending the classical method that requires complex human arm endpoint stiffness identification before human demonstration or compensation for the difference in human–robot operational effects after human demonstration. Full article

Improving the autonomy of underwater interventions by remotely operated vehicles (ROVs) can help mitigate the impact of communication delays on operational efficiency. Currently, underwater interventions for ROVs usually rely on real-time teleoperation or preprogramming by operators, which is not only time-consuming and increases the cognitive burden on operators but also requires extensive specialized programming. Instead, this paper uses the intuitive learning from demonstrations (LfD) approach that uses operator demonstrations as inputs and models the trajectory characteristics of the task through the dynamic movement primitive (DMP) approach for task reproduction as well as the generalization of knowledge to new environments. Unlike existing applications of DMP-based robot trajectory learning methods, we propose the underwater DMP (UDMP) method to address the problem that the complexity and stochasticity of underwater operational environments (e.g., current perturbations and floating operations) diminish the representativeness of the demonstrated trajectories. First, the Gaussian mixture model (GMM) and Gaussian mixture regression (GMR) are used for feature extraction of multiple demonstration trajectories to obtain typical trajectories as inputs to the DMP method. The UDMP method is more suitable for the LfD of underwater interventions than the method that directly learns the nonlinear terms of the DMP. In addition, we improve the commonly used homomorphic-based teleoperation mode to heteromorphic mode, which allows the operator to focus more on the end-operation task. Finally, the effectiveness of the developed method is verified by simulation experiments. Full article

Robotic meat cutting is increasingly in demand in meat industries due to safety issues, labor shortages, and inefficiencies. This paper proposes a multi-demonstration human–robot skill transfer framework to address the flexible and generalized cutting of sheep hindquarters with complex 3D anatomy structures by imitating humans. To improve the generalization with meat sizes and demonstrations and extract target cutting behaviors, multi-demonstrations of cutting are encoded into low-dimension latent space through principal components analysis (PCA), Gaussian mixture model (GMM), and Gaussian mixture regression (GMR). To improve the robotic cutting flexibility and the cutting behavior reproducing accuracy, this study combines a modified dynamic movement primitive (DMP) high-level behavior generator with the low-level joints admittance control (AC) through real-time inverse velocity (IV) kinematics solving and constructs the IVAC-DMP control module. The experimental results show that the maximum residual meat thickness in the sheep hindquarter cutting of sample 1 is 3.1 mm, and sample 2 is 3.8 mm. The residual rates of samples 1 and 2 are 5.6% and 4.8%. Both meet the requirements for sheep hindquarter separation. The proposed framework is advantageous for harvesting high-value meat products and providing a reference technique for robot skill learning in interaction tasks. Full article

A robot screwing skill learning framework based on teaching–learning is proposed to improve the generalization ability of robots for different scenarios and objects, combined with the experience of a human operation. This framework includes task-based teaching, learning, and summarization. We teach a robot to twist and gather the operation’s trajectories, define the obstacles with potential functions, and counter the twisting of the robot using a skill-learning-based dynamic movement primitive (DMP) and Gaussian mixture model–Gaussian mixture regression (GMM-GMR). The hole-finding and screwing stages of the process are modeled. In order to verify the effectiveness of the robot tightening skill learning model and its adaptability to different tightening scenarios, obstacle avoidance trends and tightening experiments were conducted. Obstacle avoidance and tightening experiments were conducted on the robot tightening platform for bolts, plastic bottle caps, and faucets. The robot successfully avoided obstacles and completed the twisting task, verifying the effectiveness of the robot tightening skill learning model and its adaptability to different tightening scenarios. Full article

Learning from demonstration (LfD) is a practical method for transferring skill knowledge from a human demonstrator to a robot. Several studies have shown the effectiveness of LfD in robotic grasping tasks to improve the success rate of grasping and to accelerate the development of new robotic grasping tasks. A well-designed demonstration device can effectively represent human grasping motion to transfer grasping skills to robots. In this paper, an improved gripper-like exoskeleton with a data collection system is proposed. First, we present the mechatronic details of the exoskeleton and its motion-tracking system, considering the manipulation flexibility and data acquisition requirements. We then present the capabilities of the device and its data collection system, which collects the position, pose and displacement of the gripper on the exoskeleton. The collected data is further processed by the data acquisition and processing software. Next, we describe the principles of Gaussian mixture model (GMM) and Gaussian mixture regression (GMR) in robot skill learning, which are used to transfer the raw data from demonstrations to robot motions. In the experiment, an optimized trajectory was learned from multiple demonstrations and reproduced on a robot. The results show that the GMR complemented with GMM is able to learn a smooth trajectory from demonstration trajectories with noise. Full article

There are a huge number of harmonics in the railway power supply system. Accurately estimating the harmonic impedance of the system is the key to evaluating the harmonic emission level of the power supply system. A harmonic impedance estimation method is proposed in this paper, which takes the Gaussian mixture regression (GMR) as the main idea, and is dedicated to calculating the harmonic impedance when the load changes or the background harmonic changes in the traction power supply system. First, the harmonic voltages and currents are measured at the point of common coupling (PCC); secondly, a Gaussian mixture model (GMM) is established and optimized parameters are obtained through the EM algorithm; finally, a Gaussian mixture regression is performed to obtain the utility side harmonic impedance. In the simulation study, different harmonic impedance estimation models with uniform distribution and Gaussian distribution are established, respectively, and the harmonic impedance changes caused by different system structures in the railway power supply system are simulated. At the same time, the error is compared with the existing method to judge the accuracy and robustness of this method. In the case analysis, the average value, average error, standard deviation and other indicators are used to evaluate this method. Among them, the average error and standard deviation of this method are about one-fifth to one-third of those of the binary linear regression (BLR) method and the independent random vector (IRV) method. At the same time, its index is slightly better than that of the support vector machine (SVM) method. Full article

To explore the potential relationship between environmental and socioeconomic factors and the risk of gastric cancer (GC) in the Huai River Basin, the GC incidence rate (GIR) and GC mortality rate (GMR) data from 2014 to 2018 in 14 counties of the Huai River Basin were collected from the Chinese Cancer Registration Annual Report. Environmental and socioeconomic parameters were collected through the Statistical Yearbook. The 14 counties were classified into three groups with low, moderate, and high risk of GC according to the point density of environmental factors (PDF) and index of socioeconomic factors (ISF). Significant differences in GIR and GMR were found among the counties with PDF (χ² = 21.36, p < 0.01) and ISF (χ² = 11.37, p < 0.05) levels. Meanwhile, significant differences in mortality rate were observed among counties with different PDF (χ² = 11.25, p < 0.01) and ISF (χ² = 18.74, p < 0.01), and the results showed that the ISF and PDF were increased while the GIR and GMR were decreased. Meanwhile, there was a lag effect between them, and we used two models to explore the lag effects between ISF, PDF and GIR and GMR; the coefficient influence between the ISF lag phase and GIR was −2.9768, and the coefficient influence between PDF and the lag phase on the GIR was −0.9332, and there were both significant impact when there was a probability of more than 95%. The results showed that the higher the ISF and PDF that lags in one stage, the more GIR was reduced, while the impact of the ISF and PDF on lag stage on mortality was not obvious. We used differential GMM to test the results, and also research results were relatively robust. Overall, GIR and GMR decreased with increasing point density of environmental factors and index of socioeconomic factors. Full article

Autonomous, flexible, and human–robot collaboration are the key features of the next-generation robot. Such unstructured and dynamic environments bring great challenges in online adaptive path planning. The robots have to avoid dynamic obstacles and follow the original task path as much as possible. A robust and efficient online path planning method is required accordingly. A method based on the Gaussian Mixture Model (GMM), Gaussian Mixture Regression (GMR), and the Probabilistic Roadmap (PRM) is proposed to overcome the above difficulties. During the offline stage, the GMM was used to model teaching data, and it can represent the offline-demonstrated motion and constraints. The optimal solution was encoded in the mean value, while the environmental constraints were encoded in the variance value. The GMR generated a smooth path with variance as the resample space according to the GMM of the teaching data. This representation isolated the old environment model with the novel obstacle. During the online stage, a Modified Probabilistic Roadmap (MPRM) was used to plan the motion locally. Because the GMM provides the distribution of all the feasible motion, the sampling space of the MPRM was generated by the variable density resampling method, and then, the roadmap was constructed according to the Euclidean and Probability Distance (EPD). The Dijkstra algorithm was used to search for the feasible path between the starting point and the target point. Finally, shortcut pruning and B-spline interpolation were used to generate a smooth path. During the simulation experiment, two obstacles were added to the recurrent scene to indicate the difference from the teaching scene, and the GMM/GMR-MPRM algorithm was used for path planning. The result showed that it can still plan a feasible path when the recurrent scene is not the same as the teaching scene. Finally, the effectiveness of the algorithm was verified on the IRB1200 robot experiment platform. Full article

In manufacturing, traditional task pre-programming methods limit the efficiency of human–robot skill transfer. This paper proposes a novel task-learning strategy, enabling robots to learn skills from human demonstrations flexibly and generalize skills under new task situations. Specifically, we establish a markerless vision capture system to acquire continuous human hand movements and develop a threshold-based heuristic segmentation algorithm to segment the complete movements into different movement primitives (MPs) which encode human hand movements with task-oriented models. For movement primitive learning, we adopt a Gaussian mixture model and Gaussian mixture regression (GMM-GMR) to extract the optimal trajectory encapsulating sufficient human features and utilize dynamical movement primitives (DMPs) to learn for trajectory generalization. In addition, we propose an improved visuo-spatial skill learning (VSL) algorithm to learn goal configurations concerning spatial relationships between task-relevant objects. Only one multioperation demonstration is required for learning, and robots can generalize goal configurations under new task situations following the task execution order from demonstration. A series of peg-in-hole experiments demonstrate that the proposed task-learning strategy can obtain exact pick-and-place points and generate smooth human-like trajectories, verifying the effectiveness of the proposed strategy. Full article