Recent advancements in robotics have increased the possibilities for integrating robotic systems into human-involved workplaces, highlighting the need to examine and optimize human-robot coordination in collaborative settings. This study explores human-robot interactions during handover tasks using Virtual Reality (VR) to investigate differences in human motor performance across various task dynamics and robot kinematics. A VR-based robot handover simulation afforded safe and controlled assessments of human-robot interactions. In separate experiments, four potential influences on human performance were examined (1) control over task initiation and robot movement synchrony (temporal and spatiotemporal); (2) partner appearance (human versus robotic); (3) robot velocity profiles (minimum jerk, constant velocity, constant acceleration, and biphasic); and (4) the timing of rotational object motion. Findings across experiments emphasize humans benefit from robots providing early and salient visual information about task-relevant object motion, and advantages of human-like smooth robot trajectories. To varying degrees, these manipulations improved predictive accuracy and synchronization during interaction. This suggests that human-robot interactions should be designed to allow humans to leverage their natural capabilities for detecting biological motion, which conversely may reduce the need for costly robotic computations or added cognitive adaptation on the human side.

Data scarcity continues to be a major challenge in the field of robotic manipulation. Although diffusion models provide a promising solution for generating robotic manipulation videos, existing methods largely depend on 2D trajectories, which inherently face issues with 3D spatial ambiguity. In this work, we present a novel framework named ManipDreamer3D for generating plausible 3D-aware robotic manipulation videos from the input image and the text instruction. Our method combines 3D trajectory planning with a reconstructed 3D occupancy map created from a third-person perspective, along with a novel trajectory-to-video diffusion model. Specifically, ManipDreamer3D first reconstructs the 3D occupancy representation from the input image and then computes an optimized 3D end-effector trajectory, minimizing path length while avoiding collisions. Next, we employ a latent editing technique to create video sequences from the initial image latent and the optimized 3D trajectory. This process conditions our specially trained trajectory-to-video diffusion model to produce robotic pick-and-place videos. Our method generates robotic videos with autonomously planned plausible 3D trajectories, significantly reducing human intervention requirements. Experimental results demonstrate superior visual quality compared to existing methods.

With the increasing integration of intelligent driving functions into serial-produced vehicles, ensuring their functionality and robustness poses greater challenges. Compared to traditional road testing, scenario-based virtual testing offers significant advantages in terms of time and cost efficiency, reproducibility, and exploration of edge cases. We propose a Transformer-enhanced Conditional Variational Autoencoder (CVAE-T) model for generating multi-agent traffic scenarios in roundabouts, which are characterized by high vehicle dynamics and complex layouts, yet remain relatively underexplored in current research. The results show that the proposed model can accurately reconstruct original scenarios and generate realistic, diverse synthetic scenarios. Besides, two Key-Performance-Indicators (KPIs) are employed to evaluate the interactive behavior in the generated scenarios. Analysis of the latent space reveals partial disentanglement, with several latent dimensions exhibiting distinct and interpretable effects on scenario attributes such as vehicle entry timing, exit timing, and velocity profiles. The results demonstrate the model's capability to generate scenarios for the validation of intelligent driving functions involving multi-agent interactions, as well as to augment data for their development and iterative improvement.

--Autonomous highway driving presents a high collision risk due to fast-changing environments and limited reaction time, necessitating reliable and efficient trajectory planning. This paper proposes a hybrid trajectory planning framework that integrates the adaptability of learning-based methods with the formal safety guarantees of optimization-based approaches. The framework features a two-layer architecture: an upper layer employing a graph neural network (GNN) trained on real-world highway data to predict human-like longitudinal velocity profiles, and a lower layer utilizing path optimization formulated as a mixed-integer quadratic programming (MIQP) problem. The primary contribution is the lower-layer path optimization model, which introduces a linear approximation of discretized vehicle geometry to substantially reduce computational complexity, while enforcing strict spatiotemporal non-overlapping constraints to formally guarantee collision avoidance throughout the planning horizon. Experimental results demonstrate that the planner generates highly smooth, collision-free trajectories in complex real-world emergency scenarios, achieving success rates exceeding 97% with average planning times of 54 ms, thereby confirming real-time capability. HE trajectory planning module plays a central role in ensuring driving safety in the modern autonomous driving system. It generates an optimal continuous trajectory for autonomous vehicles (A Vs) over a future time horizon based on environmental information. This environmental information is provided by the perception module, which performs multi-sensor data fusion and feature extraction to produce real-time structured data through object detection and semantic segmentation. The control system then executes the planned trajectory by minimizing the deviation between the actual and intended vehicle behavior. Highway scenarios are constrained within structured environments characterized by high-speed operation, low-curvature roadways, and standardized traffic regulations, typically involving only rule-compliant motorized vehicles.

This paper introduces a SSSUMO, semi-supervised deep learning approach for submovement decomposition that achieves state-of-the-art accuracy and speed. While submovement analysis offers valuable insights into motor control, existing methods struggle with reconstruction accuracy, computational cost, and validation, due to the difficulty of obtaining hand-labeled data. We address these challenges using a semi-supervised learning framework. This framework learns from synthetic data, initially generated from minimum-jerk principles and then iteratively refined through adaptation to unlabeled human movement data. Our fully convolutional architecture with differentiable reconstruction significantly surpasses existing methods on both synthetic and diverse human motion datasets, demonstrating robustness even in high-noise conditions. Crucially, the model operates in real-time (less than a millisecond per input second), a substantial improvement over optimization-based techniques. This enhanced performance facilitates new applications in human-computer interaction, rehabilitation medicine, and motor control studies. We demonstrate the model's effectiveness across diverse human-performed tasks such as steering, rotation, pointing, object moving, handwriting, and mouse-controlled gaming, showing notable improvements particularly on challenging datasets where traditional methods largely fail. Training and benchmarking source code, along with pre-trained model weights, are made publicly available at https://github.com/dolphin-in-a-coma/sssumo.

-- Urban driving with connected and automated vehicles (CA Vs) offers potential for energy savings, yet most eco-driving strategies focus solely on longitudinal speed control within a single lane. T o address this gap, we propose a novel energy-aware motion planning framework that jointly optimizes longitudinal speed and lateral lane-change decisions using vehicle-to-infrastructure (V2I) communication. Our approach estimates long-term energy costs using a graph-based approximation and solves short-horizon optimal control problems under traffic constraints. Using a data-driven energy model calibrated to an actual battery electric vehicle, we demonstrate with vehicle-in-the-loop experiments that our method reduces motion energy consumption by up to 24% compared to a human driver, highlighting the potential of connectivity-enabled planning for sustainable urban autonomy. Connected and Automated V ehicles (CA Vs) provide benefits in road safety, traffic efficiency, and energy efficiency [1]. Using vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications, CA Vs can coordinate with traffic signals and neighboring vehicles to optimize their motion in ways human drivers are incapable of [2]. Prior studies have shown that by optimizing longitudinal behavior using Signal Phase and Timing (SPaT) data from connected traffic lights, a single CA V can adjust its cruising speed to avoid unnecessary stops, yielding substantial energy savings (11.35 % to 16.4%) [3], [4].

Rigid link flapping mechanisms remain the most practical choice for flapping wing micro-aerial vehicles (MAVs) to carry useful payloads and onboard batteries for free flight due to their long-term durability and reliability. However, to achieve high agility and maneuverability-like insects-MAVs with these mechanisms require significant weight reduction. One approach involves using single-DOF planar rigid linkages, which are rarely optimized dimensionally for high lift and low power so that smaller motors and batteries could be used. We integrated a mechanism simulator based on a quasistatic nonlinear finite element method with an unsteady vortex lattice method-based aerodynamic analysis tool within an optimization routine. We optimized three different mechanism topologies from the literature. As a result, significant power savings were observed up to 42% in some cases, due to increased amplitude and higher lift coefficients resulting from optimized asymmetric sweeping velocity profiles. We also conducted an uncertainty analysis that revealed the need for high manufacturing tolerances to ensure reliable mechanism performance. The presented unified computational tool also facilitates the optimal selection of MAV components based on the payload and flight time requirements.

The deployment of autonomous virtual avatars (in extended reality) and robots in human group activities - such as rehabilitation therapy, sports, and manufacturing - is expected to increase as these technologies become more pervasive. Designing cognitive architectures and control strategies to drive these agents requires realistic models of human motion. However, existing models only provide simplified descriptions of human motor behavior. In this work, we propose a fully data-driven approach, based on Long Short-Term Memory neural networks, to generate original motion that captures the unique characteristics of specific individuals. We validate the architecture using real data of scalar oscillatory motion. Extensive analyses show that our model effectively replicates the velocity distribution and amplitude envelopes of the individual it was trained on, remaining different from other individuals, and outperforming state-of-the-art models in terms of similarity to human data.

Full-waveform inversion (FWI) is an advanced technique for reconstructing high-resolution subsurface physical parameters by progressively minimizing the discrepancy between observed and predicted seismic data. However, conventional FWI encounters challenges in real data applications, primarily due to its conventional objective of direct measurements of the data misfit. Accurate estimation of the source wavelet is essential for effective data fitting, alongside the need for low-frequency data and a reasonable initial model to prevent cycle skipping. Additionally, wave equation solvers often struggle to accurately simulate the amplitude of observed data in real applications. To address these challenges, we introduce a correlation-based source-independent objective function for FWI that aims to mitigate source uncertainty and amplitude dependency, which effectively enhances its practicality for real data applications. We develop a deep-learning framework constrained by this new objective function with a velocity-distribution supported deep image prior, which reparameterizes velocity inversion into trainable parameters within an autoencoder, thereby reducing the nonlinearity in the conventional FWI's objective function. We demonstrate the superiority of our proposed method using synthetic data from benchmark velocity models and, more importantly, two real datasets. These examples highlight its effectiveness and practicality even under challenging conditions, such as missing low frequencies, a crude initial velocity model, and an incorrect source wavelet.

FWI seeks to achieve a high-resolution model of the subsurface through the application of multi-variate optimization to the seismic inverse problem. Although now a mature technology, FWI has limitations related to the choice of the appropriate solver for the forward problem in challenging environments requiring complex assumptions, and very wide angle and multi-azimuth data necessary for full reconstruction are often not available. Deep Learning techniques have emerged as excellent optimization frameworks. These exist between data and theory-guided methods. Data-driven methods do not impose a wave propagation model and are not exposed to modelling errors. On the contrary, deterministic models are governed by the laws of physics. Application of seismic FWI has recently started to be investigated within Deep Learning. This has focussed on the time-domain approach, while the pseudo-spectral domain has not been yet explored. However, classical FWI experienced major breakthroughs when pseudo-spectral approaches were employed. This work addresses the lacuna that exists in incorporating the pseudo-spectral approach within Deep Learning. This has been done by re-formulating the pseudo-spectral FWI problem as a Deep Learning algorithm for a data-driven pseudo-spectral approach. A novel DNN framework is proposed. This is formulated theoretically, qualitatively assessed on synthetic data, applied to a two-dimensional Marmousi dataset and evaluated against deterministic and time-based approaches. Inversion of data-driven pseudo-spectral DNN was found to outperform classical FWI for deeper and over-thrust areas. This is due to the global approximator nature of the technique and hence not bound by forward-modelling physical constraints from ray-tracing.