Sony is halting sales of its Aibo robotic dog in Japan, ending an era for the interactive pet that became an instant hit and developed its own personality. Sony is halting sales of its Aibo robotic puppy in Japan, the company has said, eight years after the latest model of its interactive android pet became an instant hit. The Thursday announcement marks the end of an era for loyal fans of the high-tech toy, which develops its own personality and can perform tricks like waving and mimicking its owner. It was also a big comeback for Sony's robot dog. The first iteration of Aibo came out in 1999, followed by numerous models over the years -- from angular metallic-silver bots to more cuddly round-faced versions -- with more than 150,000 units sold. But by 2006, Sony, facing a tough business environment, pulled the plug on Aibo, seen as something of a frivolous luxury.

This paper introduces the Real-DRL framework for safety-critical autonomous systems, enabling runtime learning of a deep reinforcement learning (DRL) agent to develop safe and high-performance action policies in real plants (i.e., real physical systems to be controlled), while prioritizing safety! The Real-DRL consists of three interactive components: a DRL-Student, a PHY-Teacher, and a Trigger. The DRL-Student is a DRL agent that innovates in the dual self-learning and teaching-to-learn paradigm and the real-time safety-informed batch sampling. On the other hand, PHY-Teacher is a physics-model-based design of action policies that focuses solely on safety-critical functions. PHY-Teacher is novel in its realtime patch for two key missions: i) fostering the teaching-to-learn paradigm for DRL-Student and ii) backing up the safety of real plants. The Trigger manages the interaction between the DRL-Student and the PHY-Teacher. Powered by the three interactive components, the Real-DRL can effectively address safety challenges that arise from the unknown unknowns and the Sim2Real gap. Additionally, Real-DRL notably features i) assured safety, ii) automatic hierarchy learning (i.e., safety-first learning and then high-performance learning), and iii) safety-informed batch sampling to address the learning experience imbalance caused by corner cases. Experiments with a real quadruped robot, a quadruped robot in NVIDIA Isaac Gym, and a cart-pole system, along with comparisons and ablation studies, demonstrate the Real-DRL's effectiveness and unique features.

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG . Grandparents are identity theft's biggest payday Do not click fake'account recovery' Amazon email Americans need protection against'warrantless surveillance': Rep Chip Roy Spencer Pratt's use of AI to boost campaign sparks debate China approves world's first commercial brain chip Kurt Knutsson unveils his top Father's Day gift picks FBI releases list of'most wanted fraudsters' as crackdown continues Genesis AI's Eno robot skips legs for a practical design built for factories first and homes later Fox News Flash top headlines are here.

Technology Robots Do humanoids dream of becoming human? Humanoids seem to be evolving into a distinct form. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Breakthroughs, discoveries, and DIY tips sent six days a week. Stories of human-like dolls yearning to become real people turn up everywhere. Pinocchio wants to be a real boy. The robot child in Spielberg's wants to be loved like a human son.

Stay up to date with the Fox News AI Newsletter as the U.S. Navy plans to adopt robots that climb wall of warships and Dell announces plans to shrink its workforce.

Navy robots from Gecko Robotics will inspect U.S. warships in $71 million effort to reduce maintenance delays as only 60% of fleet remains operational amid China's naval expansion.

Exploration is a fundamental challenge in reinforcement learning (RL). Many current exploration methods for deep RL use task-agnostic objectives, such as information gain or bonuses based on state visitation. However, many practical applications of RL involve learning more than a single task, and prior tasks can be used to inform how exploration should be performed in new tasks. In this work, we study how prior tasks can inform an agent about how to explore effectively in new situations. We introduce a novel gradient-based fast adaptation algorithm - model agnostic exploration with structured noise (MAESN) - to learn exploration strategies from prior experience. The prior experience is used both to initialize a policy and to acquire a latent exploration space that can inject structured stochasticity into a policy, producing exploration strategies that are informed by prior knowledge and are more effective than random action-space noise. We show that MAESN is more effective at learning exploration strategies when compared to prior meta-RL methods, RL without learned exploration strategies, and task-agnostic exploration methods. We evaluate our method on a variety of simulated tasks: locomotion with a wheeled robot, locomotion with a quadrupedal walker, and object manipulation.

Some of the world's most advanced robots showed off their skills at tech shows and sporting events, doing everything from cooking shrimp to running half marathons This striking humanoid robot is the R1 from Robbyant, a company owned by Chinese tech giant Ant Group. The allure of humanoid robots is their versatility - you can imagine them doing any job that a human can, simply because they have the same appendages. But unlike wheeled robots, they have to deal with balancing on two legs, which is no mean feat. The R1 strikes a balance, with a stable wheeled base and a humanoid form from the waist up. The R1 certainly made an impressive entrance at the IFA 2025 tech show in Berlin, where it demonstrated its skills in the kitchen, cooking up shrimp - albeit at a very relaxed pace.

Claire chatted to Oluwami Dosunmu-Ogunbi from Ohio Northern University about bipedal robots that can walk and even climb stairs. Oluwami Dosunmu-Ogunbi (Wami) is an Assistant Professor in the Mechanical Engineering Department at Ohio Northern University. Her research focuses on controls with applications in bipedal locomotion and engineering education. She is the first Black woman to receive a PhD in Robotics at the University of Michigan. During her Ph.D., she developed the Biped Bootcamp technical document, which she is transforming into an undergraduate curriculum --introducing students to bipedal robotics while providing advanced coursework for juniors and seniors.

Learning the flows of hybrid systems that have both continuous and discrete time dynamics is challenging. The existing method learns the dynamics in each discrete mode, which suffers from the combination of mode switching and discontinuities in the flows. In this work, we propose CHyLL (Continuous Hybrid System Learning in Latent Space), which learns a continuous neural representation of a hybrid system without trajectory segmentation, event functions, or mode switching. The key insight of CHyLL is that the reset map glues the state space at the guard surface, reformulating the state space as a piecewise smooth quotient manifold where the flow becomes spatially continuous. Building upon these insights and the embedding theorems grounded in differential topology, CHyLL concurrently learns a singularity-free neural embedding in a higher-dimensional space and the continuous flow in it. We showcase that CHyLL can accurately predict the flow of hybrid systems with superior accuracy and identify the topological invariants of the hybrid systems. Finally, we apply CHyLL to the stochastic optimal control problem.