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Abstract This paper aims to develop a distributed layered control framework for the navigation, planning, and control of multi-agent quadrupedal robots subject to environments with uncertain obstacles and various disturbances. At the highest layer of the proposed layered control, a reference path for all agents is calculated, considering artificial potential fields (APF) under a priori known obstacles. Second, in the middle layer, we employ a distributed nonlinear model predictive control (NMPC) scheme with a one-step delay communication protocol (OSDCP) subject to reduced-order and linear inverted pendulum (LIP) models of agents to ensure the feasibility of the gaits and collision avoidance, addressing the degree of uncertainty in real-time. Finally, low-level nonlinear whole-body controllers (WBCs) impose the full-order locomotion models of agents to track the optimal and reduced-order trajectories. The proposed controller is validated for effectiveness and robustness on up to four A1 quadrupedal robots in simulations and two robots in the experiments.1 Simulations and experimental validations demonstrate that the proposed approach can effectively address the real-time planning and control problem. In particular, multiple A1 robots are shown to navigate various environments, maintaining collision-free distances while being subject to unknown external disturbances such as pushes and rough terrain. 

Developing a dexterous robotic hand that mimics natural human hand movements is challenging due to complicated hand anatomy. Such a practical design should address several requirements, which are often conflicting and force the designer to prioritize the main design characteristics for a given application. Therefore, in the existing designs the requirements are only partially satisfied, leading to complicated and bulky solutions. To address this gap, a novel single-actuated, cable-driven, and self-contained robotic hand is presented in this work. This five-fingered robotic hand supports 19 degrees of freedom (DOFs) and can perform a wide range of precision and power grasps. The external structure of fingers and the thumb is inspired by Pisa/IIT SoftHand, while major modifications are implemented to significantly decrease the number of parts and the effect of friction. The cable configuration is inspired by the tendon structure of the hand anatomy. Furthermore, a novel power transmission system is presented in this work. This mechanism addresses compactness and underactuation, while ensuring proper force distribution through the fingers and the thumb. Moreover, this power transmission system can achieve adaptive grasps of objects with unknown geometries, which significantly simplifies the sensory and control systems. A 3D-printed prototype of the proposed design is fabricated and its base functionality is evaluated through simulations and experiments. 

Emergency response (ER) workers perform extremely demanding physical and cognitive tasks that can result in serious injuries and loss of life. Human augmentation technologies have the potential to enhance physical and cognitive work-capacities, thereby dramatically transforming the landscape of ER work, reducing injury risk, improving ER, as well as helping attract and retain skilled ER workers. This opportunity has been significantly hindered by the lack of high-quality training for ER workers that effectively integrates innovative and intelligent augmentation solutions. Hence, new ER learning environments are needed that are adaptive, affordable, accessible, and continually available for reskilling the ER workforce as technological capabilities continue to improve. This article presents the research considerations in the design and integration of use-inspired exoskeletons and augmented reality technologies in ER processes and the identification of unique cognitive and motor learning needs of each of these technologies in context-independent and ER-relevant scenarios. We propose a human-centered artificial intelligence (AI) enabled training framework for these technologies in ER. Finally, how these human-centered training requirements for nascent technologies are integrated in an intelligent tutoring system that delivers across tiered access levels, covering the range of virtual, to mixed, to physical reality environments, is discussed.