W e present a dataset for force-grounded, cross-view articulated manipulation that couples what is seen with what is done and what is felt during real human interaction. The dataset contains 3048 sequences across 381 articulated objects in 38 environments. Each object is operated under four embodiments - (i) human hand, (ii) human hand with a wrist-mounted camera, (iii) handheld UMI gripper, and (iv) a custom Hoi! gripper - where the tool embodiment provide synchronized end-effector forces and tactile sensing. Our dataset offers a holistic view of interaction understanding from video, enabling researchers to evaluate how well methods transfer between human and robotic viewpoints, but also investigate underexplored modalities such as force sensing and prediction.

In multi UAV scenarios,the traditional Artificial Potential Field (APF) method often leads to redundant flight paths and frequent abrupt heading changes due to unreasonable obstacle avoidance path planning,and is highly prone to inter UAV collisions during the obstacle avoidance process.To address these issues,this study proposes a novel hybrid algorithm that combines the improved Multi-Robot Formation Obstacle Avoidance (MRF IAPF) algorithm with an enhanced APF optimized for single UAV path planning.Its core ideas are as follows:first,integrating three types of interaction forces from MRF IAPF obstacle repulsion force,inter UAV interaction force,and target attraction force;second,incorporating a refined single UAV path optimization mechanism,including collision risk assessment and an auxiliary sub goal strategy.When a UAV faces a high collision threat,temporary waypoints are generated to guide obstacle avoidance,ensuring eventual precise arrival at the actual target.Simulation results demonstrate that compared with traditional APF based formation algorithms,the proposed algorithm achieves significant improvements in path length optimization and heading stability,can effectively avoid obstacles and quickly restore the formation configuration,thus verifying its applicability and effectiveness in static environments with unknown obstacles.

Electromyography (EMG) signals are widely used for predicting body joint angles through machine learning (ML) and deep learni ng (DL) methods. However, these approaches often face challenges such as limited real - time applicability, non - representative test c onditions, and the need for large datasets to achieve optimal performance. This paper presents a transfer - learning framework for knee joint angle prediction that requires only a few gait cycles from new subjects. Three datasets - Georgia Tech, the Universi ty of California Irvine (UCI), and the Sharif Mechatronic Lab Exoskeleton (SMLE) - containing four EMG channels relevant to knee motion were utilized. A lightweight attention - based CNN - LSTM model was developed and pre - trained on the Georgia Tech dataset, t hen transferred to the UCI and SMLE datasets. The proposed model achieved Normalized Mean Absolute Errors (NMAE) of 6.8 percent and 13.7 percent for one - step and 50 - step predictions on abnormal subjects using EMG inputs alone. Incorporating historical knee angles reduced the NMAE to 3.1 percent and 3.5 percent for normal subjects, and to 2.8 percent and 7.5 percent for abnormal subjects. When f urther adapted to the SMLE exoskeleton with EMG, kinematic, and interaction force inputs, the model achieved 1.09 p ercent and 3.1 percent NMAE for one - and 50 - step predictions, respectively. These results demonstrate robust performance and strong generalization for both short - and long - term rehabilitation scenarios . Keywords: EMG, Transfer Learning, Knee Angle Prediction, Attention Mechanism, Rehabilitation, Exoskeleton . 1 - Introduction Electromyography (EMG) measures electrical signals generated by contracting muscle fibers, reflecting neuromuscular activity. EMG is typically measured using electrodes placed on the skin's surface (surface Electromyography (sEMG)). Alternatively, electrodes may be inserted into the muscle tissue [2] . The frequency range of EMG signals is generally reported to be from 6 to 500 Hz, with most power concentrated between 20 and 250 Hz [3] . Analyzing EMG signals provides valuable information about muscle activation patterns, coordination, and fatigue levels.

Atrial fibrillation (AF) increases the risk of thromboembolic events due to impaired function of the left atrial appendage (LAA). Left atrial appendage closure (LAAC) is a minimally invasive intervention designed to reduce stroke risk by sealing the LAA with an expandable occluder device. Current deployment relies on manual catheter control and imaging modalities like fluoroscopy and transesophageal echocardiography, which carry limitations including radiation exposure and limited positioning precision. In this study, we leverage a previously developed force-sensing delivery sheath integrating fiber Bragg gratings (FBGs) at the interface between the catheter and the occluder. Combined with electromagnetic (EM) tracking, this setup enables real-time measurement of interaction forces and catheter tip position during robot-assisted LAAC deployment in an anatomical phantom. We present a novel force-displacement profiling method that characterizes occluder deployment dynamics and identifies key procedural steps without relying on ionizing radiation. The force profiles reveal low-magnitude interaction forces, suggesting minimal mechanical stress on the surrounding anatomy. This approach shows promise in providing clinicians with enhanced intraoperative feedback, improving deployment outcome. Future work will focus on automating deployment steps classification and validating the sensing strategy in dynamic, realistic environments.

This paper presents an Impedance Primitive-augmented hierarchical reinforcement learning framework for efficient robotic manipulation in sequential contact tasks. We leverage this hierarchical structure to sequentially execute behavior primitives with variable stiffness control capabilities for contact tasks. Our proposed approach relies on three key components: an action space enabling variable stiffness control, an adaptive stiffness controller for dynamic stiffness adjustments during primitive execution, and affordance coupling for efficient exploration while encouraging compliance. Through comprehensive training and evaluation, our framework learns efficient stiffness control capabilities and demonstrates improvements in learning efficiency, compositionality in primitive selection, and success rates compared to the state-of-the-art. The training environments include block lifting, door opening, object pushing, and surface cleaning. Real world evaluations further confirm the framework's sim2real capability. This work lays the foundation for more adaptive and versatile robotic manipulation systems, with potential applications in more complex contact-based tasks.

Dexterous robotic hands enable versatile interactions due to the flexibility and adaptability of multi-fingered designs, allowing for a wide range of task-specific grasp configurations in diverse environments. However, to fully exploit the capabilities of dexterous hands, access to diverse and high-quality grasp data is essential -- whether for developing grasp prediction models from point clouds, training manipulation policies, or supporting high-level task planning with broader action options. Existing approaches for dataset generation typically rely on sampling-based algorithms or simplified force-closure analysis, which tend to converge to power grasps and often exhibit limited diversity. In this work, we propose a method to synthesize large-scale, diverse, and physically feasible grasps that extend beyond simple power grasps to include refined manipulations, such as pinches and tri-finger precision grasps. We introduce a rigorous, differentiable energy formulation of force closure, implicitly defined through a Quadratic Program (QP). Additionally, we present an adjusted optimization method (MALA*) that improves performance by dynamically rejecting gradient steps based on the distribution of energy values across all samples. We extensively evaluate our approach and demonstrate significant improvements in both grasp diversity and the stability of final grasp predictions. Finally, we provide a new, large-scale grasp dataset for 5,700 objects from DexGraspNet, comprising five different grippers and three distinct grasp types. Dataset and Code:https://graspqp.github.io/

This paper presents a scheme to enhance payload manipulation using a robot collaborating with an overhead crane. In the current industrial practice, when the crane's payload has to be accurately manipulated and located in a desired position, the task becomes laborious and risky since the operators have to guide the fine motions of the payload by hand. In the proposed collaborative scheme, the crane lifts the payload while the robot's end-effector guides it toward the desired position. The only link between the robot and the crane is the interaction force produced during the guiding of the payload. Two admittance transfer functions are considered to accomplish harmless and smooth contact with the payload. The first is used in a position-based admittance control integrated with the robot. The second one adds compliance to the crane by processing the interaction force through the admittance transfer function to generate a crane's velocity command that makes the crane follow the payload. Then the robot's end-effector and the crane move collaboratively to guide the payload to the desired location. A method is presented to design the admittance controllers that accomplish a fluent robot-crane collaboration. Simulations and experiments validating the scheme potential are shown.

In many applications, robots are increasingly deployed in unstructured and natural environments where they encounter various types of vegetation. Vegetation presents unique challenges as a traversable obstacle, where the mechanical properties of the plants can influence whether a robot can safely collide with and overcome the obstacle. A more nuanced approach is required to assess the safety and traversability of these obstacles, as collisions can sometimes be safe and necessary for navigating through dense or unavoidable vegetation. This paper introduces a novel sensor designed to directly measure the applied forces exerted by vegetation on a robot: by directly capturing the push-back forces, our sensor provides a detailed understanding of the interactions between the robot and its surroundings. We demonstrate the sensor's effectiveness through experimental validations, showcasing its ability to measure subtle force variations. This force-based approach provides a quantifiable metric that can inform navigation decisions and serve as a foundation for developing future learning algorithms.

Laparoscopic surgery constrains instrument motion around a fixed pivot point at the incision into a patient to minimize tissue trauma. Surgical robots achieve this through either hardware to software-based remote center of motion (RCM) constraints. However, accurate RCM alignment is difficult due to manual trocar placement, patient motion, and tissue deformation. Misalignment between the robot's RCM point and the patient incision site can cause unsafe forces at the incision site. This paper presents a sensorless force estimation-based framework for dynamically assessing and optimizing RCM misalignment in robotic surgery. Our experiments demonstrate that misalignment exceeding 20 mm can generate large enough forces to potentially damage tissue, emphasizing the need for precise RCM positioning. For misalignment $D\geq $ 20 mm, our optimization algorithm estimates the RCM offset with an absolute error within 5 mm. Accurate RCM misalignment estimation is a step toward automated RCM misalignment compensation, enhancing safety and reducing tissue damage in robotic-assisted laparoscopic surgery.

In recent years, deep learning techniques have made significant strides in molecular generation for specific targets, driving advancements in drug discovery. However, existing molecular generation methods present significant limitations: those operating at the atomic level often lack synthetic feasibility, drug-likeness, and interpretability, while fragment-based approaches frequently overlook comprehensive factors that influence protein-molecule interactions. To address these challenges, we propose a novel fragment-based molecular generation framework tailored for specific proteins. Our method begins by constructing a protein subpocket and molecular arm concept-based neural network, which systematically integrates interaction force information and geometric complementarity to sample molecular arms for specific protein subpockets. Subsequently, we introduce a diffusion model to generate molecular backbones that connect these arms, ensuring structural integrity and chemical diversity. Our approach significantly improves synthetic feasibility and binding affinity, with a 4% increase in drug-likeness and a 6% improvement in synthetic feasibility. Furthermore, by integrating explicit interaction data through a concept-based model, our framework enhances interpretability, offering valuable insights into the molecular design process.