For the development of autonomous robotic systems, Dynamic Movement Primitives (DMP) and Artificial Potential Fields (APF) are two well known techniques. DMPs are a reference algorithm in robotics for one shot learning as they enable learning complex movements and generating smooth trajectories, while APF are outstanding in navigation and obstacle avoidance tasks. By integrating DMPs and APF, the task automation capability can be significantly enhanced, as the precision of DMPs combined with the reactive nature of APF promises, in theory, adaptability and efficiency for the learning algorithm. Despite the numerous papers discussing and reviewing both techniques independently, there is a lack of an objective comparison of the investigations combining both approaches. This paper aims to provide such a comprehensive literature analysis, using a homogenized mathematical formulation. Moreover, a categorization based on their application scope, the robots used and their characteristics is provided. Finally, open challenges in the combination of DMP and APF are discussed, highlighting further works that are worth conducting for improving the integration of both approaches.

In this paper, an innovative algorithm for averaging a set of multivariate time series with different lengths based on Constrained Dynamic Time Warping (CDTW) is proposed. This approach relies on the CDTW to provide the non-linear alignment of the multivariate time series, and employs the proposed Minimum Cost Averaging (MCA) technique to identify the optimum matches and get equal-length time series. MCA-CDTW is a task-agnostic approach that after selecting a reference curve, transforms the rest of the demonstrations in the set to obtain new curves that are time-aligned with the reference. From these transformed curves, not only the mean but also the signal variability can be directly extracted. This technique provides smooth mean curves even when there are large deviations between the demonstrations in the set, and still the complexity of the proposed algorithm is significantly reduced compared to other averaging techniques from the literature. When learning techniques are used to teach a motion to a robotic system, obtaining smooth trajectories is important to achieve good robotic behaviors. The new algorithm MCA-CDTW is tested and compared on two different databases: a literature database where humans move a robotic arm with kinaesthetic teaching, and a set of recordings of a teleoperated robotic arm performing laboratory manipulation. On both datasets, it is demonstrated that the new approach is providing smooth average trajectories.

The construction industry faces significant challenges due to the physically demanding and hazardous nature of tasks such as manual filling of expansion joints with mastic. Automating mastic filling presents additional difficulties due to the variability of mastic density with temperature, which creates a constantly changing environment that requires adaptive control strategies to ensure consistent application quality. This pilot study focuses on testing a new human–robot collaborative approach for automating the mastic application in concrete expansion joints. The system learns the task from demonstrations performed by expert construction operators teleoperating the robot. This study evaluates the usability, efficiency, and adoption of robotic assistance in joint-filling tasks compared to traditional manual methods. The study analyzes execution time and joint quality measurements, psychophysiological signal analysis, and post-task user feedback. This multi-source approach enables a comprehensive assessment of task performance and both objective and subjective evaluations of technology acceptance. The findings underscore the effectiveness of automated systems in improving safety and productivity on construction sites, while also identifying key areas for technological improvement.

This study aims to evaluate different combinations of features and algorithms to be used in the control of a prosthetic hand wherein both the configuration of the fingers and the gripping forces can be controlled. This requires identifying machine learning algorithms and feature sets to detect both intended force variation and hand gestures in EMG signals recorded from upper-limb amputees. However, despite the decades of research into pattern recognition techniques, each new problem requires researchers to find a suitable classification algorithm, as there is no such thing as a universal ’best’ solution. Consideration of different techniques and data representation represents a fundamental practice in order to achieve maximally effective results. To this end, we employ a publicly-available database recorded from amputees to evaluate different combinations of features and classifiers. Analysis of data from 9 different individuals shows that both for classic features and for time-dependent power spectrum descriptors (TD-PSD) the proposed logarithmically scaled version of the current window plus previous window achieves the highest classification accuracy. Using linear discriminant analysis (LDA) as a classifier and applying a majority-voting strategy to stabilize the individual window classification, we obtain 88% accuracy with classic features and 89% with TD-PSD features.

Enabling robots to learn from few demonstrations is a crucial step toward task automation. This paper addresses a key challenge in robotic learning from demonstration: developing adaptable, constraint-aware systems that learn from a limited number of demonstrations, infer task-specific geometrical constraints autonomously, and ensure trajectory adherence to these constraints while remaining accessible to non-expert users. The proposed methodology integrates automatic task-constraint extraction from teleoperated demonstrations with a novel sigmoidal coupling term (SIG-CDMP) to enforce spatial constraints within Dynamic Movement Primitives (DMPs). By analyzing variability in demonstrations, the framework defines a tolerance zone for robot motion and ensures that generated trajectories remain within these bounds, even when adapting to new initial or goal positions. The efficacy of this approach is validated in two real-world industrial applications—sterility testing in pharmaceutical industry and cable wiring in electric vehicle batteries—demonstrating negligible increases in computational cost and smooth, constraint-compliant trajectories. By integrating task-constraint extraction and enforcement, this approach advances the development of constraint-aware robotic systems that learn from repeated demonstrations to teach control policies respecting inferred geometrical constraints, paving the way for safe and reliable task automation in complex environments.