America's solar farms have a bird problem. Utility companies have been finding bird carcasses littering the ground at their facilities for years, a strange and unexpected consequence of the national solar boom. No one was quite sure why this was happening, but it was clearly a problem for a type of energy that was billed as being environmentally friendly. So in 2013, a group of utilities, academics, and environmental organizations came together to form the Avian Solar Working Group to develop strategies to mitigate avian deaths at solar facilities around the US. "There was very little research about the impacts of solar on birds," says Misti Sporer, the lead environmental scientist at Duke Energy, an electric utility in North Carolina, and the coordinator of the working group.

Tegmark, president of the Future of Life Institute at MIT, made this rather grandiose statement: "In creating AI [artificial intelligence], we're birthing a new form of life with unlimited potential for good or ill." A study by Sir Nigel Shadbolt and Roger Hampson entitled The Digital Ape carries the subtitle How to Live (in Peace) with Smart Machines. They are optimistic that humans will still be in charge, provided we approach the process sensibly. But is this optimism justified? The director of Cambridge University's Centre for the Study of Existential Risk said: "We live in a world that could become fraught with . . .

Three friends were having morning tea on a farm in the Northern Rivers region in New South Wales (NSW), Australia, when they noticed a drilling rig setting up in a neighbor's property on the opposite side of the valley. They had never heard of the coal seam gas (CSG) industry, nor had they previously considered activism. That drilling rig, however, was enough to push them into action. The group soon became instrumental in establishing the anti-CSG movement, a movement whose activism resulted in the NSW government suspending gas exploration licenses in the area in 2014.2 By 2015, the government had bought back a petroleum exploration license covering 500,000 hectares across the region.3 Mining companies, like companies in many industries, have been struggling with the difference between having a legal license to operate and a moral4 one. The colloquial version of this is the distinction between what one could do and what one should do--just because something is technically possible and economically feasible doesn't mean that the people it affects will find it morally acceptable. Without the acceptance of the community, firms find themselves dealing with "never-ending demands" from "local troublemakers" hearing that "the company has done nothing for us"--all resulting in costs, financial and nonfinancial,5 that weigh projects down. A company can have the best intentions, investing in (what it thought were) all the right things, and still experience opposition from within the community. It may work to understand local mores and invest in the community's social infrastructure--improving access to health care and education, upgrading roads and electricity services, and fostering economic activity in the region resulting in bustling local businesses and a healthy employment market--to no avail. Without the community's acceptance, without a moral license, the mining companies in NSW found themselves struggling. This moral license is commonly called a social license, a phrase coined in the '90s, and represents the ongoing acceptance and approval of a mining development by a local community. Since then, it has become increasingly recognized within the mining industry that firms must work with local communities to obtain, and then maintain, a social license to operate (SLO).6 The concept of a social license to operate has developed over time and been adopted by a range of industries that affect the physical environment they operate in, such as logging or pulp and paper mills. What has any of this to do with artificial intelligence (AI)?

This work adds on to the on-going efforts to provide more autonomy to space robots. Here the concept of programming by demonstration or imitation learning is used for trajectory planning of manipulators mounted on small spacecraft. For greater autonomy in future space missions and minimal human intervention through ground control, a robot arm having 7-Degrees of Freedom (DoF) is envisaged for carrying out multiple tasks like debris removal, on-orbit servicing and assembly. Since actual hardware implementation of microgravity environment is extremely expensive, the demonstration data for trajectory learning is generated using a model predictive controller (MPC) in a physics based simulator. The data is then encoded compactly by Probabilistic Movement Primitives (ProMPs). This offline trajectory learning allows faster reproductions and also avoids any computationally expensive optimizations after deployment in a space environment. It is shown that the probabilistic distribution can be used to generate trajectories to previously unseen situations by conditioning the distribution. The motion of the robot (or manipulator) arm induces reaction forces on the spacecraft hub and hence its attitude changes prompting the Attitude Determination and Control System (ADCS) to take large corrective action that drains energy out of the system. By having a robot arm with redundant DoF helps in finding several possible trajectories from the same start to the same target. This allows the ProMP trajectory generator to sample out the trajectory which is obstacle free as well as having minimal attitudinal disturbances thereby reducing the load on ADCS.

A desirable property in fault-tolerant controllers is adaptability to system changes as they evolve during systems operations. An adaptive controller does not require optimal control policies to be enumerated for possible faults. Instead it can approximate one in real-time. We present two adaptive fault-tolerant control schemes for a discrete time system based on hierarchical reinforcement learning. We compare their performance against a model predictive controller in presence of sensor noise and persistent faults. The controllers are tested on a fuel tank model of a C-130 plane. Our experiments demonstrate that reinforcement learning-based controllers perform more robustly than model predictive controllers under faults, partially observable system models, and varying sensor noise levels.

We propose a novel adaptive reinforcement learning control approach for fault tolerant control of degrading systems that is not preceded by a fault detection and diagnosis step. Therefore, \textit{a priori} knowledge of faults that may occur in the system is not required. The adaptive scheme combines online and offline learning of the on-policy control method to improve exploration and sample efficiency, while guaranteeing stable learning. The offline learning phase is performed using a data-driven model of the system, which is frequently updated to track the system's operating conditions. We conduct experiments on an aircraft fuel transfer system to demonstrate the effectiveness of our approach.

While the gold standard for measuring airflow is to use a spirometer with an occlusive seal, this is not practical for ambulatory monitoring of patients. Advances in sensor technology have made measurement of motion of the thorax and abdomen feasible with small inexpensive devices, but estimation of airflow from these time series is challenging. We propose to use the nonlinear-type time-frequency analysis tool, synchrosqueezing transform, to properly represent the thoracic and abdominal movement signals as the features, which are used to recover the airflow by the locally stationary Gaussian process. We show that, using a dataset that contains respiratory signals under normal sleep conditions, an accurate prediction can be achieved by fitting the proposed model in the feature space both in the intra-and inter-subject setups. We also apply our method to a more challenging case, where subjects under general anesthesia underwent transitions from pressure support to unassisted ventilation to further demonstrate the utility of the proposed method. Keyword: high-frequency physiological data; Gaussian process regression; time-frequency analysis; synchrosqueezing transform.

In this paper, we present an online adaptive robust control framework for underactuated brachiating robots traversing flexible cables. Since the dynamic model of a flexible body is unknown in practice, we propose an indirect adaptive estimation scheme to approximate the unknown dynamic effects of the flexible cable as an external force with parametric uncertainties. A boundary layer-based sliding mode control is then designed to compensate for the residual unmodeled dynamics and time-varying disturbances, in which the control gain is updated by an auxiliary direct adaptive control mechanism. Stability analysis and derivation of adaptation laws are carried out through a Lyapunov approach, which formally guarantees the stability and tracking performance of the robot-cable system. Simulation experiments and comparison with a baseline controller show that the combined direct-indirect adaptive robust control framework achieves reliable tracking performance and adaptive system identification, enabling the robot to traverse flexible cables in the presence of unmodeled dynamics, parametric uncertainties and unstructured disturbances.

Matt Carlson is the Vice President of Business Development at WiBotic Inc, a company that provides reliable wireless power solutions to charge aerial, mobile and aquatic robot systems. Why are wireless charging solutions so important to the future of robotics? Robots need the ability to autonomously charge for most applications. It simply isn't cost effective to hire a staff of workers to manage battery charging or battery swapping. However, most autonomous charging today is done using docking stations that require physical mating of electrical contacts.

This paper investigates the optimization problem of an infinite stage discrete time Markov decision process (MDP) with a long-run average metric considering both mean and variance of rewards together. Such performance metric is important since the mean indicates average returns and the variance indicates risk or fairness. However, the variance metric couples the rewards at all stages, the traditional dynamic programming is inapplicable as the principle of time consistency fails. We study this problem from a new perspective called the sensitivity-based optimization theory. A performance difference formula is derived and it can quantify the difference of the mean-variance combined metrics of MDPs under any two different policies. The difference formula can be utilized to generate new policies with strictly improved mean-variance performance. A necessary condition of the optimal policy and the optimality of deterministic policies are derived. We further develop an iterative algorithm with a form of policy iteration, which is proved to converge to local optima both in the mixed and randomized policy space. Specially, when the mean reward is constant in policies, the algorithm is guaranteed to converge to the global optimum. Finally, we apply our approach to study the fluctuation reduction of wind power in an energy storage system, which demonstrates the potential applicability of our optimization method.