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As autonomous systems begin to operate amongst humans, methods for safe interaction must be investigated. We consider an example of a small autonomous vehicle in a pedestrian zone that must safely maneuver around people in a free-form fashion. We investigate two key questions: How can we effectively integrate pedestrian intent estimation into our autonomous stack? Can we develop an online monitoring framework to give rigorous assurances on the safety of such human-robot interactions? We present a pedestrian intent estimation framework that can accurately predict future pedestrian trajectories given multiple possible goal locations. We integrate this into a reachability-based online monitoring and decision making scheme that formally assesses the safety of these interactions with nearly real-time performance (approximately 0.1s). These techniques are both tested in simulation and integrated on a test vehicle with a complete in-house autonomous stack, demonstrating safe interaction in real-world experiments. 

Programming languages, libraries, and development tools have transformed the application development processes for mobile computing and machine learning. This paper introduces CyPhyHouse-a toolchain that aims to provide similar programming, debugging, and deployment benefits for distributed mobile robotic applications. Users can develop hardware-agnostic, distributed applications using the high-level, event driven Koord programming language, without requiring expertise in controller design or distributed network protocols. The modular, platform-independent middleware of CyPhyHouse implements these functionalities using standard algorithms for path planning (RRT), control (MPC), mutual exclusion, etc. A high-fidelity, scalable, multi-threaded simulator for Koord applications is developed to simulate the same application code for dozens of heterogeneous agents. The same compiled code can also be deployed on heterogeneous mobile platforms. The effectiveness of CyPhyHouse in improving the design cycles is explicitly illustrated in a robotic testbed through development, simulation, and deployment of a distributed task allocation application on in-house ground and aerial vehicles. 

Background—Inhibitory-excitatory (I-E) imbalance has increasingly been proposed as a fundamental mechanism giving rise to many schizophrenia-related pathophysiology. The integrity of I-E functions should require precise and rapid electrical signal transmission. Objective/Hypothesis—We hypothesized that part of the I-E abnormality in schizophrenia may originate from their known abnormal white matter connectivity that may interfere the I-E functions. Methods—We test this using short-interval intracortical inhibition (SICI) vs. intracortical facilitation (ICF) which is a non-invasive measurement of I-E signaling. SICI-ICF from left motor cortex and white matter microstructure were assessed in schizophrenia patients and healthy controls. Results—Schizophrenia patients showed significantly reduced SICI but not ICF. White matter microstructure as measured by fraction anisotropy (FA) in diffusion tensor imaging had a significant effect on SICI in patients, such that weaker SICI was associated with lower FA in several white matter tracts, most strongly with left corona radiata (r=−0.68, p=0.0002) that contains the fibers connecting with left motor cortex. Left corticospinal tract, which carries the motor fibers to peripheral muscular output, also showed significant correlation with SICI (r=−0.54, p=0.005). Mediation analysis revealed that much of the schizophrenia disease effect on SICI can be accounted for by mediation through left corona radiata. SICI was also significantly associated with the performance of processing speed in patients.