Despite recent advancements in physical humanrobot collaboration, measuring and distinguishing between forces applied by humans and robots remains challenging, limiting our understanding of force dynamics during collaboration. Our proposed solution addresses this gap with a low-cost, lightweight design that integrates directly at the robot endeffector level. The interface employs a three-ring mechanical structure with strategically positioned load cells and a Sarrus mechanism to constrain movement to the z-axis only, enabling tool mounting for real-world collaborative tasks such as blending or sanding operations. Validation experiments demonstrate excellent force decoupling capabilities with minimal crossinterference, achieving Weighted Root Mean Squared Errors of 0.14 N for robot-applied forces and 0.08 N for human-applied forces compared to ground truth measurements in steadystate for loads ranging from 0 N up to 23 N. The Maximum Absolute Error in these experiments is 0.33 N, confirming high measurement accuracy. This affordable and integrated solution lowers the threshold for employing decoupled force sensing in collaborative tasks, making it more accessible for investigating force dynamics and developing adaptive control strategies in both research and practical applications of physical humanrobot collaboration.

This paper presents a multi-modal dynamic workspace re-indexing method for addressing operator ergonomics and workspace limitations. The proposed method has two interactive modes: pose-to-pose mode, which is active when the operator is within an ergonomic workspace of comfortable arm postures, and ergonomic workspace drift mode, which activates after the operator makes an excursion beyond the boundaries of the ergonomic workspace when trying to reach more distant targets with the remote robot. In the ergonomic workspace drift mode, the operator temporarily stays slightly outside these boundaries, while the offset between the local and remote workspace drifts with a velocity proportional to the excursion distance. This dynamically re-indexes the remote workspace toward the distant target, and the operator can remain in a comfortable posture while the remote robot moves toward the intended target where the task is. To construct the ergonomic workspace, we employed the Rapid Upper Limb Assessment method. To validate the proposed method, we conducted experiments on a teleoperation setup involving a Force Dimension Sigma7 haptic device controlling a Kuka LBR iiwa robotic arm. The results show that the proposed controller successfully addresses workspace limitations by dynamically reindexing the follower's workspace towards target objects, while maintaining good operator ergonomics.

Robotic Ultrasound Systems (RUSS) provide a promising solution to reduce operator dependency, alleviate physical strain, and meet the growing demand for ultrasound procedures. However, their clinical applicability remains limited by their inability to adapt to dynamic patient movements and tissue deformations during scans. This work introduces a novel framework that leverages Laplacian Trajectory Editing (LTE) for real-time adaptation of scan trajectories in response to both rigid and non-rigid patient movements. it integrates a RGB-D camera to capture surface point clouds, which are processed to estimate displacements between consecutive frames. These displacements define anchor points for LTE-based trajectory adaptations, ensuring smooth motion while preserving local trajectory properties. This approach is validated through experiments spanning rigid phantom movements, generalization across differently shaped phantoms, and non-rigid human arm motion. Adaptation accuracy is quantified by comparing adapted trajectories to a ground-truth reference, with root mean squared errors averaging 0.026 0.012 m in non-rigid scenarios. Real-time trajectory adaptation is achieved, with an average LTE adaptation processing time of 373 ms per trial. Furthermore, our implementation achieved low tracking errors across all conditions while maintaining a high success rate in diverse movement scenarios. These results demonstrate the feasibility of LTE for real-time trajectory adaptation in ultrasound scanning, offering a pathway to more autonomous and clinically viable RUSS implementations.

In this paper, we propose a closed-loop force sensor based nested admittance/impedance control strategy to actively estimate and minimize the effects of geometric misalignment that naturally occur during assembly tasks with compliant robots. The method allows the robot to be used with a stiff impedance control setting, which is beneficial for free air motion performance, yet allows to adjust for large misalignment errors between parts that need be assembled.