Autonomous systems are often required to operate in partially observable environments. They must reliably execute a specified objective even with incomplete information about the state of the environment. We propose a methodology to synthesize policies that satisfy a linear temporal logic formula in a partially observable Markov decision process (POMDP). By formulating a planning problem, we show how to use point-based value iteration methods to efficiently approximate the maximum probability of satisfying a desired logical formula and compute the associated belief state policy. We demonstrate that our method scales to large POMDP domains and provides strong bounds on the performance of the resulting policy.

Robotic grasping is a crucial area of research as it can result in the acceleration of the automation of several Industries utilizing robots ranging from manufacturing to healthcare. Reinforcement learning is the field of study where an agent learns a policy to execute an action by exploring and exploiting rewards from an environment. Reinforcement learning can thus be used by the agent to learn how to execute a certain task, in our case grasping an object. We have used the Pick and Place environment provided by OpenAI's Gym to engineer rewards. Hindsight Experience Replay (HER) has shown promising results with problems having a sparse reward. In the default configuration of the OpenAI baseline and environment the reward function is calculated using the distance between the target location and the robot end-effector. By weighting the cost based on the distance of the end-effector from the goal in the x,y and z-axes we were able to almost halve the learning time compared to the baselines provided by OpenAI, an intuitive strategy that further reduced learning time. In this project, we were also able to introduce certain user desired trajectories in the learnt policies (city-block / Manhattan trajectories). This helps us understand that by engineering the rewards we can tune the agent to learn policies in a certain way even if it might not be the most optimal but is the desired manner.

Fox News Flash top headlines for Jan. 10 are here. Check out what's clicking on Foxnews.com A Mississippi Army veteran who served in both Vietnam and Iraq says his prosthetic legs were repossessed and returned in an unusable state -- because the Department of Veterans Affairs (VA) refused to pay for them. Jerry Holliman, 69, told the Clarion-Ledger newspaper that prosthetics vender Hanger repossessed his artificial limbs two days before Christmas. Although he was encouraged to use Medicare to find replacement prosthetic legs, Holliman said he wanted the VA to pay for them.

Wildlife is flourishing in the exclusion zone around the disabled Fukushima Daichii nuclear reactor in Japan, images from remotely-operated cameras have revealed. Researchers spotted more than 20 species in areas around the reactor, including wild boar, macaques and fox-like raccoon dogs. The findings help reveal how wildlife populations respond in the wake of catastrophic nuclear disaster like those that occurred at Fukushima and Chernobyl. Humans were evacuated from certain zones around the the Fukushima reactor following radiation leaks caused by the Tōhoku earthquake and tsunami of 2011. Wildlife ecologist James Beasley of the University of Georgia, in the US, and colleagues used a network of 106 remote cameras to capture images of the wildlife in the area around the Fukushima Daiichi power plant over a four-month period.

In this episode, our interviewer Audrow Nash speaks to Gil Weinberg, Professor in Georgia Tech's School of Music and the founding director of the Georgia Tech Center for Music Technology. Weinberg leads a research lab called the Robotic Musicianship group, which focuses on developing artificial creativity and musical expression for robots and on augmented humans. Weinberg discusses several of his improvisational robots and how they work, including Shimon, a multi-armed robot marimba player, as well as his work in prosthetic devices for musicians. Below is a video that includes Shimon on marimba, Jason Barnes playing drums with a prostetic, and Prof. Gil Weinberg on bass guitar. Gil Weinberg is a professor in Georgia Tech's School of Music and the founding director of the Georgia Tech Center for Music Technology, where he leads the Robotic Musicianship group.

Engineers from Duke University and the Institut de Physique de Nice in France have developed a new method to identify objects using microwaves that improves accuracy while reducing the associated computing time and power requirements. The system could provide a boost to object identification and speed in fields where both are critical, such as autonomous vehicles, security screening and motion sensing. It also jointly determines optimal hardware settings that reveal the most important data while simultaneously discovering what the most important data actually is. In a proof-of-principle study, the setup correctly identified a set of 3D numbers using tens of measurements instead of the hundreds or thousands typically required. The results appear online on December 6 in the journal Advanced Science and are a collaboration between David R. Smith, the James B. Duke Distinguished Professor of Electrical and Computer Engineering at Duke, and Roarke Horstmeyer, assistant professor of biomedical engineering at Duke. "Object identification schemes typically take measurements and go to all this trouble to make an image for people to look at and appreciate," said Horstmeyer. "But that's inefficient because the computer doesn't need to'look' at an image at all." "This approach circumvents that step and allows the program to capture details that an image-forming process might miss while ignoring other details of the scene that it doesn't need," added Aaron Diebold, a research assistant in Smith's lab. "We're basically trying to see the object directly from the eyes of the machine."

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"Anyone who wants to discuss how artificial intelligence is shaping the world should read this book. Tegmark, a physicist by training, takes a scientific approach. He doesn't spend a lot of time saying we should do this or that, and as a result, Life 3.0 offers a terrific baseline of knowledge on the subject." Tegmark successfully gives clarity to the many faces of AI, creating a highly readable book that complements The Second Machine Age's economic perspective on the near-term implications of recent accomplishments in AI and the more detailed analysis of how we might get from where we are today to AGI and even the superhuman AI in Superintelligence. . . . At one point, Tegmark quotes Emerson: 'Life is a journey, not a destination.'

Are these hubots something human or some kind of machine? If Human intelligence can quickly tell the difference between the two, machine learning must rely on algorithms like artificial neural networks to make a prediction. Patterned after the structure of the human mind, do ANNs allow machines to think like humans? What exactly are ANNs, how do they work, how do they differ from other machine learning algorithms, and what are their use scenarios in data science? Computers were originally designed around algorithms composed of predetermined steps to calculate the right answer for a given case.

What lies ahead for Alzheimer's therapy development? While anti-amyloid antibodies are at last signaling some success, researchers agree that these expensive--and, thus far, at best modestly effective--biologic drugs can form only part of the arsenal needed to fight the disease. Researchers at the 12th Clinical Trials on Alzheimer's Disease conference, held December 4–7 in San Diego, California, broadly agreed that an array of therapeutic approaches will be needed to target symptomatic stages, or to combine with antibodies to boost efficacy. Speakers also discussed how to improve the dismal success rate of Alzheimer's clinical trials. In particular, there is a push to spend more time in Phase 2 to find the right dose and confirm physiological effects of the drug at hand.