Gaylord is home to its own herd of 60 elk and one of the largest wild herds in the United States. Breakthroughs, discoveries, and DIY tips sent every weekday. When winter's bitter winds blow and snow falls, it can be hard for some of us to muster up the will and energy to actually spend time out in nature. Still, connecting with nature is important for our health, even in cold weather. Now, viewers around the world can take advantage of Gaylord, Michigan's elk cam and get a taste of the outdoors from the comfort of home.

Nonprehensile manipulation, such as pushing objects across cluttered environments, presents a challenging control problem due to complex contact dynamics and long-horizon planning requirements. In this work, we propose HeRD, a hierarchical reinforcement learning-diffusion policy that decomposes pushing tasks into two levels: high-level goal selection and low-level trajectory generation. We employ a high-level reinforcement learning (RL) agent to select intermediate spatial goals, and a low-level goal-conditioned diffusion model to generate feasible, efficient trajectories to reach them. This architecture combines the long-term reward maximizing behaviour of RL with the generative capabilities of diffusion models. We evaluate our method in a 2D simulation environment and show that it outperforms the state-of-the-art baseline in success rate, path efficiency, and generalization across multiple environment configurations. Our results suggest that hierarchical control with generative low-level planning is a promising direction for scalable, goal-directed nonprehensile manipulation. Code, documentation, and trained models are available: https://github.com/carosteven/HeRD.

Inside cowboy Charles Jesse "Buffalo" Jones's get-rich-quick scheme to restore the plains 100 years ago. By 1888, Charles Jesse "Buffalo" Jones had succeeded in crossbreeding a buffalo with cow, a hybrid he claimed would be as tasty as beef and as hardy as buffalo. Breakthroughs, discoveries, and DIY tips sent every weekday. The "cattalo" was a homely creature--stocky and shaggy, with a slight buffalo's hump and a cow's docile face. Charles "Buffalo" Jones invented the cow-buffalo hybrid in 1888.

Breakthroughs, discoveries, and DIY tips sent every weekday. Roboticists in China have developed a life-sized, furry, AI-enabled antelope designed to monitor the migration patterns of its real-life counterpart. This "bionic" antelope is part of a growing arsenal of somewhat convincing-looking robots used to observe wildlife in up close and personal ways human researchers often can't. The robot was first reported on by Chinese news agency Xinhua and was reportedly co-designed by DEEP Robotics and the Chinese Academy of Sciences. It was built to fill a gap in current efforts to monitor the once-endangered Tibetan antelope (Pantholops hodgsonii).

Tech expert Kurt Knutsson discusses how robots can milk, feed and clean cows on dairy farms, boosting efficiency and comfort. Picture this: A dairy barn full of cows being milked, fed and cleaned up after, but there's no farmer in sight. Sounds a bit unusual, right? Well, it's not as far-fetched as you might think. Thanks to cutting-edge agricultural robotics, this kind of scene is becoming more common.

This paper introduces a novel control methodology designed to guide a collective of robotic-sheep in a cluttered and unknown environment using robotic-dogs. The dog-agents continuously scan the environment and compute a safe trajectory to guide the sheep to their final destination. The proposed optimization-based controller guarantees that the sheep reside within a desired distance from the reference trajectory through the use of Control Barrier Functions (CBF). Additional CBF constraints are employed simultaneously to ensure inter-agent and obstacle collision avoidance. The efficacy of the proposed approach is rigorously tested in simulation, which demonstrates the successful herding of the robotic-sheep within complex and cluttered environments.

In a remarkable experiment of artificial intelligence meets elephants, researchers have successfully demonstrated how the giant mammals call to each other using individual names. According to a new study published in Nature Ecology and Evolution, African savannah elephants in Kenya were observed and listened to, using machine learning software called Elephant Voices which analysed calls being made between two herds of elephants. The research took place in Samburu National Reserve and Amboseli National Park over four years including 14 months of fieldwork, in which elephants were tracked and observed and their "calls" recorded. Some 469 unique calls or "rumbles" were captured from the African elephants in the experiment. It has long been known that elephants are highly social animals.

In the 2023 blockbuster, Robots, Shailene Woodley and Jack Whitehall star as singletons who send robot'doubles' of themselves out on dates. While this might sound far-fetched, it could soon become a reality. Speaking at the Bloomberg Tech Summit, Herd, 34, claimed that daters could soon use an'AI dating concierge' to go out on hundreds of dates for them. 'If you want to get really out there, there is a world where your [AI] dating concierge could go and date for you with other dating concierge,' she said. In the 2023 blockbuster, Robots, Shailene Woodley and Jack Whitehall star as singletons who send robot'doubles' of themselves out on dates.

Therefore, to transfer a policy on the source robot to multiple target robots, they must launch multiple independent runs for each target robot. We investigate the problem of transferring an expert policy from a source robot to multiple different robots. To solve this problem, we propose a method named Meta-Evolve that uses continuous robot evolution to efficiently transfer the policy to each target robot through a set of tree-structured evolutionary robot sequences. The robot evolution tree allows the robot evolution paths to be shared, so our approach can significantly outperform naive one-to-one policy transfer. We present a heuristic approach to determine an optimized robot evolution tree. Experiments have shown that our method is able to improve the efficiency of one-to-three transfer of manipulation policy by up to 3.2 and one-to-six transfer of agile locomotion policy by 2.4 in terms of simulation cost over the baseline of launching multiple independent one-to-one policy transfers. The robotics industry has designed and developed a large number of commercial robots deployed in various applications. How to efficiently learn robotic skills on diverse robots in a scalable fashion? A popular solution is to train a policy for every new robot on every new task from scratch. This is not only inefficient in terms of sample efficiency but also impractical for complex robots due to a large exploration space. Inter-robot imitation by statistic matching methods that optimize to match the distribution of actions (Ross et al., 2011), transitioned states (Liu et al., 2019; Radosavovic et al., 2020), or reward (Ng et al., 2000; Ho & Ermon, 2016) could be possible solutions. However, they can only be applied to robots with similar dynamics to yield optimal performance. Recent advances in evolution-based imitation learning (Liu et al., 2022a;b) inspire us to view this problem from the perspective of policy transferring from one robot to another. The core idea is to interpolate two different robots by producing a large number of intermediate robots between them which gradually evolve from the source robot toward the target robot.

Diagnostic tests which can detect pre-clinical or sub-clinical infection, are one of the most powerful tools in our armoury of weapons to control infectious diseases. Considerable effort has been therefore paid to improving diagnostic testing for human, plant and animal diseases, including strategies for targeting the use of diagnostic tests towards individuals who are more likely to be infected. Here, we follow other recent proposals to further refine this concept, by using machine learning to assess the situational risk under which a diagnostic test is applied to augment its interpretation . We develop this to predict the occurrence of breakdowns of cattle herds due to bovine tuberculosis, exploiting the availability of exceptionally detailed testing records. We show that, without compromising test specificity, test sensitivity can be improved so that the proportion of infected herds detected by the skin test, improves by over 16 percentage points. While many risk factors are associated with increased risk of becoming infected, of note are several factors which suggest that, in some herds there is a higher risk of infection going undetected, including effects that are correlated to the veterinary practice conducting the test, and number of livestock moved off the herd.