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1. Feedforward compensation of the pose-dependent vibration of a silicon wafer handling robot

   https://doi.org/10.1016/j.cirp.2024.04.081  

   Chou, Cheng-Hao; Qian, Chen; Lin, Yung-Chun; Awtar, Shorya; Okwudire, Chinedum E (January 2024, CIRP Annals)

   Frog-legged robots are commonly used for silicon wafer handling in semiconductor manufacturing. However, their precision, speed and versatility are limited by vibration which varies with their position in the workspace. This paper proposes a methodology for modelling the pose-dependent vibration of a frog-legged robot as a function of its changing inertia, and its experimentally-identified joint stiffness and damping. The model is used to design a feedforward tracking controller for compensating the pose-dependent vibration of the robot. In experiments, the proposed method yields 65–73% reduction in RMS tracking error compared to a baseline controller designed for specific poses of the robot. 

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2. CONFIDANT: A Privacy Controller for Social Robots

   https://doi.org/10.1109/HRI53351.2022.9889540  

   Tang, Brian; Sullivan, Dakota; Cagiltay, Bengisu; Chandrasekaran, Varun; Fawaz, Kassem; Mutlu, Bilge (January 2022, Proceedings of the 2022 ACM/IEEE International Conference on Human-Robot Interaction)

   As social robots become increasingly prevalent in day-to-day environments, they will participate in conversations and appropriately manage the information shared with them. However, little is known about how robots might appropriately discern the sensitivity of information, which has major implications for human-robot trust. As a first step to address a part of this issue, we designed a privacy controller, CONFIDANT, for conversational social robots, capable of using contextual metadata (e.g., sentiment, relationships, topic) from conversations to model privacy boundaries. Afterwards, we conducted two crowdsourced user studies. The first study (n = 174) focused on whether a variety of human-human interaction scenarios were perceived as either private/sensitive or non-private/non-sensitive. The findings from our first study were used to generate association rules. Our second study (n = 95) evaluated the effectiveness and accuracy of the privacy controller in human-robot interaction scenarios by comparing a robot that used our privacy controller against a baseline robot with no privacy controls. Our results demonstrate that the robot with the privacy controller outperforms the robot without the privacy controller in privacy-awareness, trustworthiness, and social-awareness. We conclude that the integration of privacy controllers in authentic human-robot conversations can allow for more trustworthy robots. This initial privacy controller will serve as a foundation for more complex solutions. 

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3. Hierarchical Adaptive Control for Collaborative Manipulation of a Rigid Object by Quadrupedal Robots

   https://doi.org/10.1109/IROS55552.2023.10341700  

   Sombolestan, Mohsen; Nguyen, Quan (October 2023, IEEE)

   Despite the potential benefits of collaborative robots, effective manipulation tasks with quadruped robots remain difficult to realize. In this paper, we propose a hierarchical control system that can handle real-world collaborative manipulation tasks, including uncertainties arising from object properties, shape, and terrain. Our approach consists of three levels of controllers. Firstly, an adaptive controller computes the required force and moment for object manipulation without prior knowledge of the object's properties and terrain. The computed force and moment are then optimally distributed between the team of quadruped robots using a Quadratic Programming (QP)-based controller. This QP-based controller optimizes each robot's contact point location with the object while satisfying constraints associated with robot-object contact. Finally, a decentralized loco-manipulation controller is designed for each robot to apply manipulation force while maintaining the robot's stability. We successfully validated our approach in a high-fidelity simulation environment where a team of quadruped robots manipulated an unknown object weighing up to 18 kg on different terrains while following the desired trajectory. 

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4. Admittance Trajectory Tracking using a Challenge-Based Rehabilitation Robot with Functional Electrical Stimulation

   https://doi.org/10.23919/ACC.2018.8431750  

   Cousin, Christian A.; Duenas, Victor H.; Rouse, Courtney A.; Dixon, Warren E. (June 2018, American Control Conference)

   In an effort to combine two rehabilitation strategies, Functional Electrical Stimulation (FES) and robotic therapy, a rehabilitation robot was developed to challenge an arm during bicep curls elicited by closed-loop control of FES. The robot is designed to act as an admittance and its robust, sliding mode controller is proven to be passive with respect to the human. The FES controller utilizes a robust, sliding mode control design to then dominate the robot effects and obtain global exponential stability as demonstrated by a Lyapunov-based stability analysis. The two interacting controllers yield arm position and velocity regulation, where the robot challenges this movement with a desired admittance. 

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5. Inverse Kinematic Control of a Delta Robot Using Neural Networks in Real-Time

   https://doi.org/10.3390/robotics10040115  

   Gholami, Akram; Homayouni, Taymaz; Ehsani, Reza; Sun, Jian-Qiao (December 2021, Robotics)

   This paper presents an inverse kinematic controller using neural networks for trajectory controlling of a delta robot in real-time. The developed control scheme is purely data-driven and does not require prior knowledge of the delta robot kinematics. Moreover, it can adapt to the changes in the kinematics of the robot. For developing the controller, the kinematic model of the delta robot is estimated by using neural networks. Then, the trained neural networks are configured as a controller in the system. The parameters of the neural networks are updated while the robot follows a path to adaptively compensate for modeling uncertainties and external disturbances of the control system. One of the main contributions of this paper is to show that updating the parameters of neural networks offers a smaller tracking error in inverse kinematic control of a delta robot with consideration of joint backlash. Different simulations and experiments are conducted to verify the proposed controller. The results show that in the presence of external disturbance, the error in trajectory tracking is bounded, and the negative effect of joint backlash in trajectory tracking is reduced. The developed method provides a new approach to the inverse kinematic control of a delta robot. 

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