In the control task of mobile manipulators(MM), achieving efficient and agile obstacle avoidance in dynamic environments is challenging. In this letter, we present a safe expeditious whole-body(SEWB) control for MMs that ensures both external and internal collision-free. SEWB is constructed by a two-layer optimization structure. Firstly, control barrier functions(CBFs) are employed for a MM to establish initial safety constraints. Moreover, to resolve the pseudo-equilibrium problem of CBFs and improve avoidance agility, we propose a novel sub-optimization called adaptive cyclic inequality(ACI). ACI considers obstacle positions, velocities, and predefined directions to generate directional constraints. Then, we combine CBF and ACI to decompose safety constraints alongside an equality constraint for expectation control. Considering all these constraints, we formulate a quadratic programming(QP) as our primary optimization. In the QP cost function, we account for the motion accuracy differences between the base and manipulator, as well as obstacle influences, to achieve optimized motion. We validate the effectiveness of our SEWB control in avoiding collision and reaching target points through simulations and real-world experiments, particularly in challenging scenarios that involve fast-moving obstacles. SEWB has been proven to achieve whole-body collision-free and improve avoidance agility, similar to a "martial arts dodge".

Generating long-term texts such as novels using artificial intelligence has always been a challenge. A common approach is to use large language models (LLMs) to construct a hierarchical framework that first plans and then writes. Despite the fact that the generated novels reach a sufficient length, they exhibit poor logical coherence and appeal in their plots and deficiencies in character and event depiction, ultimately compromising the overall narrative quality. In this paper, we propose a method named Extracting Excelsior and Expanding. Ex3 initially extracts structure information from raw novel data. By combining this structure information with the novel data, an instruction-following dataset is meticulously crafted. This dataset is then utilized to fine-tune the LLM, aiming for excelsior generation performance. In the final stage, a tree-like expansion method is deployed to facilitate the generation of arbitrarily long novels. Evaluation against previous methods showcases Ex3's ability to produce higher-quality long-form novels.

Assassin's Creed and Total War have proven that video games can be better than any tattered textbook at bringing history alive – though they do tend to retread the same old battlegrounds of western Europe. China's Everstone Studio is hoping to change that, letting players loose on an open world 10th-century China in its debut game, Where Winds Meet. Here, we are put into the sandals of a nameless young martial artist and transported back to the dramatic fall of the Southern Tang dynasty, where the sudden poisoning of Emperor Li Yu thrusts our hero into a dangerous new world. Despite its indie origins, Where Winds Meet looks like a game with a big budget behind it, drawing comparisons to Sucker Punch's multimillion dollar samurai epic Ghosts of Tsushima. Its sprawling depiction of southern China is a sight to behold; comb through the gameplay videos and you'll see its hero roaming across a luscious countryside one minute, stumbling upon a serene wildlife-filled pond the next and then being pursued by bandits after dark, dodging arrows on rain-soaked rooftops.

The first sports robot which Tanaka created, was one that reproduced particular techniques found in Japanese martial arts. Traditional Japanese martial arts feature a variety of body control exercises, within which exist some truly amazing techniques. One such technique allows you to "defeat your opponent without having to use any force at all." During his time studying for his postgraduate degree, Tanaka met Kunihiro Ogata - a senior member of the Intelligent Systems and Informatics Laboratory (currently at the National Institute of Advanced Industrial Science and Technology). After studying these martial arts, Ogata decided to incorporate motion-capture technology in an attempt to measure and scientifically analyze the body control exercises found in martial arts.The aim was to see if he could gain some clear understanding of the principles behind these techniques and discover if they could be incorporated into the design and manipulation of robots.

A few years ago, when I was in the early stages of learning the Russian martial art called Systema, my trainer pinned me to the floor. I felt immobilized and gradually gave up. The will to fight seeped out of me. He then told me that my focus had been on what I could not do. He suggested I should instead consider what I could do.

A longstanding goal in character animation is to combine data-driven specification of behavior with a system that can execute a similar behavior in a physical simulation, thus enabling realistic responses to perturbations and environmental variation. We show that well-known reinforcement learning (RL) methods can be adapted to learn robust control policies capable of imitating a broad range of example motion clips, while also learning complex recoveries, adapting to changes in morphology, and accomplishing user-specified goals. Our method handles keyframed motions, highly-dynamic actions such as motion-captured flips and spins, and retargeted motions. By combining a motion-imitation objective with a task objective, we can train characters that react intelligently in interactive settings, e.g., by walking in a desired direction or throwing a ball at a user-specified target. This approach thus combines the convenience and motion quality of using motion clips to define the desired style and appearance, with the flexibility and generality afforded by RL methods and physics-based animation. We further explore a number of methods for integrating multiple clips into the learning process to develop multi-skilled agents capable of performing a rich repertoire of diverse skills. We demonstrate results using multiple characters (human, Atlas robot, bipedal dinosaur, dragon) and a large variety of skills, including locomotion, acrobatics, and martial arts.

One of the most important things that ever happened to Dan Faggella was a collapsed roof. The then 25-year-old had been building his own business in his hometown of Wakefield, Rhode Island: a martial arts gym focusing on his specialty, Brazilian Jiu Jitsu. "I was 17 or so when I first learned Brazilian Jiu Jitsu, and I really fell in love with the'chess game' aspect of the martial art," Faggella says. When his teacher's studio closed, he used the money he'd earned from teaching private lessons and working at a local beach to open his own studio, which grew from a rented space in someone else's gym to a 500-square-foot training area to 2,500 square feet of his own. At the same time, he was using his earnings from the studio to pursue what might seem like a very different path: a one-year Masters of Applied Positive Psychology (MAPP) at the University of Pennsylvania, studying under well-known author and positive psychology expert Martin Seligman.