Animal welfare education could greatly benefit from customized robots to help children learn about animals and their behavior, and thereby promote positive, safe child-animal interactions. To this end, we ran Participatory Design workshops with animal welfare educators and children to identify key requirements for zoomorphic robots from their perspectives. Our findings encompass a zoomorphic robot's appearance, behavior, and features, as well as concepts for a narrative surrounding the robot. Through comparing and contrasting the two groups, we find the importance of: negative reactions to undesirable behavior from children; using the facial features and tail to provide cues signaling an animal's internal state; and a natural, furry appearance and texture. We also contribute some novel activities for Participatory Design with children, including branching storyboards inspired by thematic apperception tests and interactive narratives, and reflect on some of the key design challenges of achieving consensus between the groups, despite much overlap in their design concepts.

We study the well-motivated problem of online distribution shift in which the data arrive in batches and the distribution of each batch can change arbitrarily over time. Since the shifts can be large or small, abrupt or gradual, the length of the relevant historical data to learn from may vary over time, which poses a major challenge in designing algorithms that can automatically adapt to the best ``attention span'' while remaining computationally efficient. We propose a meta-algorithm that takes any network architecture and any Online Learner (OL) algorithm as input and produces a new algorithm which provably enhances the performance of the given OL under non-stationarity. Our algorithm is efficient (it requires maintaining only $O(\log(T))$ OL instances) and adaptive (it automatically chooses OL instances with the ideal ``attention'' length at every timestamp). Experiments on various real-world datasets across text and image modalities show that our method consistently improves the accuracy of user specified OL algorithms for classification tasks. Key novel algorithmic ingredients include a \emph{multi-resolution instance} design inspired by wavelet theory and a cross-validation-through-time technique. Both could be of independent interest.

A powerful new open-source artificial intelligence model created by Chinese startup DeepSeek has shaken Silicon Valley over the past few days. Packed with cutting-edge capabilities and developed on a seemingly tiny budget, DeepSeek's R1 is prompting talk of an impending upheaval in the tech industry. To some people, DeepSeek's rise signals that the US has lost its edge in AI. But a number of experts, including executives at companies that build and customize some of the world's most powerful frontier AI models, say it's a sign of a different kind of technological transition underway. Instead of trying to create larger and larger models that require increasingly exorbitant amounts of computing resources, AI companies are now focusing more on developing advanced capabilities, like reasoning.

While imitation learning methods have seen a resurgent interest for robotic manipulation, the well-known problem of compounding errors continues to afflict behavioral cloning (BC). Waypoints can help address this problem by reducing the horizon of the learning problem for BC, and thus, the errors compounded over time. However, waypoint labeling is underspecified, and requires additional human supervision. Can we generate waypoints automatically without any additional human supervision? Our key insight is that if a trajectory segment can be approximated by linear motion, the endpoints can be used as waypoints. We propose Automatic Waypoint Extraction (AWE) for imitation learning, a preprocessing module to decompose a demonstration into a minimal set of waypoints which when interpolated linearly can approximate the trajectory up to a specified error threshold. AWE can be combined with any BC algorithm, and we find that AWE can increase the success rate of state-of-the-art algorithms by up to 25% in simulation and by 4-28% on real-world bimanual manipulation tasks, reducing the decision making horizon by up to a factor of 10. Videos and code are available at https://lucys0.github.io/awe/

NASA announced that the launch of the Utah State University Space Dynamics Laboratory and College of Science-led Atmospheric Waves Experiment, or AWE, is scheduled for December 2023. The NASA-funded instrument will launch from Cape Canaveral Space Force Station to the International Space Station. AWE Principal Investigator Dr. Michael Taylor from USU's College of Science leads a team of scientists that will provide new details about how the weather on Earth interacts with, and affects, space weather. To do that, the AWE instrument, measuring about 54 centimeters by one meter and weighing less than 57 kilograms, will peer into Earth's upper atmosphere from an orbit of about 400 kilometers above to provide unprecedented images of Earth's gravity waves as they rise through the mesopause, the mesosphere's upper boundary, and into other parts of the ionosphere. Atmospheric gravity waves are generated by weather events on Earth, including strong winds that shoot upward as they collide with large mountains, hurricanes that create gravity waves directly through high winds and indirectly by interacting with underlying topography, and seismic activities such as earthquakes and volcanic eruptions. Impacts from atmospheric gravity waves and space weather can adversely affect satellites that provide seemingly ubiquitous services across the globe and for human spaceflight missions.

Energy management systems (EMS) are becoming increasingly important in order to utilize the continuously growing curtailed renewable energy. Promising energy storage systems (ESS), such as batteries and green hydrogen should be employed to maximize the efficiency of energy stakeholders. However, optimal decision-making, i.e., planning the leveraging between different strategies, is confronted with the complexity and uncertainties of large-scale problems. Here, we propose a sophisticated deep reinforcement learning (DRL) methodology with a policy-based algorithm to realize the real-time optimal ESS planning under the curtailed renewable energy uncertainty. A quantitative performance comparison proved that the DRL agent outperforms the scenario-based stochastic optimization (SO) algorithm, even with a wide action and observation space. Owing to the uncertainty rejection capability of the DRL, we could confirm a robust performance, under a large uncertainty of the curtailed renewable energy, with a maximizing net profit and stable system. Action-mapping was performed for visually assessing the action taken by the DRL agent according to the state. The corresponding results confirmed that the DRL agent learns the way like what a human expert would do, suggesting reliable application of the proposed methodology.

India is flourishing with all the successes in the field of robotics with multi-functional as well as humanoid robots. The Indian domestic market has already started using these innovative and coolest robots across all industries to boost productivity and enhance customer engagement. Robotics in India has successfully left the world in awe. Let's explore some of the top coolest robots in India that have impressed the global tech market in these few years. Kempa is one of the coolest robots in India that meet lakhs of passengers including foreigners at the Kempegowda International Airport, Bengaluru.

At the World Economic Forum in Davos this year, Alibaba co-founder and chairman Jack Ma made the case for investing in our emotional capacities and even proposed a "love quotient." Management thinkers believe that socio-emotional skills are going to be a key asset in tomorrow's marketplace, simply because tasks requiring operational excellence and efficiency are likely to be performed much more effectively by AI and robots. Emotions, however, remain a human bastion. Our very weakness is our strength. In a 2016 survey, the World Economic Forum ranked socio-emotional skills as increasingly critical for future career success.

The 9th annual Augmented World Expo at the Santa Clara Convention Center, May 29th to June 1st, 2018, was a celebration of AR's progress. Watershed events, like the introduction of ARKit from Apple in September 2017, have spurred innovation. Mobile AR is very hot. Most of the glasses look dorky, though some are slimming down. The dorky ones were by far the most popular. The bigger story, however, is how fast the enterprise segment is growing as applications as straightforward as schematics on a head-mounted monocular microdisplay are transforming manufacturing, assembly, and warehousing. Tom Emrich, Programmer of AWE and a partner in Super Ventures, delivered his dramatic keynote AWE using motion capture technology. For AWE's co-founder and Executive Producer, Ori Inbar, the Conference was nothing less than a victory lap. With Microsoft and Qualcomm among the Gold Sponsors, there was a palpable smell of vindication in the air.

The task of representing entire graphs has seen a surge of prominent results, mainly due to learning convolutional neural networks (CNNs) on graph-structured data. While CNNs demonstrate state-of-the-art performance in graph classification task, such methods are supervised and therefore steer away from the original problem of network representation in task-agnostic manner. Here, we coherently propose an approach for embedding entire graphs and show that our feature representations with SVM classifier increase classification accuracy of CNN algorithms and traditional graph kernels. For this we describe a recently discovered graph object, anonymous walk, on which we design task-independent algorithms for learning graph representations in explicit and distributed way. Overall, our work represents a new scalable unsupervised learning of state-of-the-art representations of entire graphs.