A new Science Robotics study highlights a reinforcement learning–based... framework that integrates perceptual uncertainty into policy learning to equip humanoid robots with reactive soccer skills and dynamic behaviors.show more

A new Science Robotics study highlights a reinforcement learning-based,... zero-shot approach that uses motion capture and animation to train humanoid and four-legged robots to perform acrobatics.show more

NVIDIA announces new Isaac tools for agents as Agile... Robots unveils Agile ONE SN, a reference humanoid for robotics research. 🦾 Built on NVIDIA Isaac GR00T and powered by Jetson Thor, Agile ONE SN gives researchers and developers an integrated platform to develop, train and validate humanoid skills. The Isaac expansion also introduces new datasets, a runtime, and robot learning framework updates to accelerate development from data to deployment. Read the #NVIDIAGTC announcement:show more

A new Science Robotics study highlights exoskeletons that allow... physical therapists and patients recovering from stroke to “feel” each other’s movements as they walk on separate treadmills, enabling nuanced gait correction.show more

A new approach uses diffused orientation fields to help... robots transfer skills across curved objects of diverse shapes and sizes, enabling them to peel and slice varied fruits and vegetables. Learn more in Science Robotics:show more

Generalist robots need multimodal learning—fusing vision, touch, and body... motion to handle unstructured worlds. 🦾 NVIDIA Isaac Lab is a GPU-accelerated, open-source simulation framework for robot learning that unifies physics, rendering, sensing, and parallel computing to train robot policies at scale. Learn more 🔗 #NVIDIAResearchshow more

Current Vision-Language-Action (VLA) paradigms in autonomous driving primarily rely... on Imitation Learning (IL), which introduces inherent challenges such as distribution shift and causal confusion. Online Reinforcement Learning offers a promising pathway to address these issues through trial-and-error learning. However, applying online reinforcement learning to VLA models in autonomous driving is hindered by inefficient exploration in continuous action spaces. MindDrive, a VLA framework comprising a large language model (LLM) with two distinct sets of LoRA parameters. The one LLM serves as a Decision Expert for scenario reasoning and driving decision-making, while the other acts as an Action Expert that dynamically maps linguistic decisions into feasible trajectories. Paper Title: MindDrive: A Vision-Language-Action Model for Autonomous Driving via Project: Link:show more

🚨Current scalable RL algos train a policy w/o value... func, which is limiting with learning in open-ended, non-stationary, dynamic environments. But, how to scale value-based RL with more data/compute is unclear... Not anymore: presenting scaling laws for value-based RL 🧵⬇️show more

Anyways..... The main problem with tracing a 3D model... is that your skills are still at zero. The similarity between you and the AI dudes is that instead of learning actual skills, you resort to a simple one‑click solution. Go study anatomy and perspective, you lazy ass🤷‍♀️show more

Robora Sim: A PyBullet-Powered Environment for Learning Robotic Physical... Intelligence We are currently building our Robora simulation environment setup for our sim based learning, leveraging PyBullet, an industry-standard physics engine widely used in AI-driven robotics research and development. The environment is optimized with GPU-accelerated learning algorithms, enabling high-speed imitation learning and reinforcement learning within a safe and controlled virtual setup before shipping out to real world. This simulation platform allows our models to learn, adapt, and generalize across different robot morphologies, terrain types and task objectives - all before deployment to the real world. At it's core, the system combines a VLA-powered high-level planner with low-level motion control algorithms, working cohesively to produce emergent, physically intelligent behaviors. This synergy between simulation, learning, and real-world transfer marks a major step forward in our pursuit of adaptive and intelligent robotic systems. Through advanced domain randomization and synthetic data generation, the Robora Simulation Environment ensures that policies trained in simulation transfer effectively to real-world robots, minimizing the sim-to-real gap. Moreover, users will be able to test and integrate their own hardware kits within selected simulation environments in the Robora Dapp, ensuring seamless compatibility and safer real-world implementation.show more

Unveiling Babbel's new look! With a focus on tailoring... learning experiences to meet your needs, our new brand experience shows that learning a new language is a journey. One that leads to transformation in yourself and the world around you as you learn to speak with others.show more

BaBot: The Ball-Balancing Robot - Real-time PID control on... a 2-axis platform, powered by a microcontroller using the same chip as an Arduino. - Precision servos and IR sensors track the ball with speed and accuracy. - Perfect for learning control systems, teaching robotics, or showing off your engineering skills. - Fully open-source & ready to build at Video Credit: Johan Link #engineering #technology #robots #roboticsshow more

teleoperator kicking himself over not programming Asimov's Laws of... Robotics... Worth considering if we should base today's reality on the science fiction musings from the past. Testing with humanoid robots is a very interesting new challenge. Historically robots have been confined to keep out zones with rigid walls and laser barriers, lock-out/tag-out, etc. This is the easy way to ensure people don't get hurt by very capable machines. But the more we integrate humanoid robotics into collaborative roles with humans the more we need to remove those barriers by nature of the work.show more

As a newly appointed 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗼𝗿 at Imperial College... London, I'm thrilled to announce the 𝗦𝗮𝗳𝗲 𝗪𝗵𝗼𝗹𝗲-𝗯𝗼𝗱𝘆 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀 𝗟𝗮𝗯 (𝗦𝗪𝗜𝗥𝗟) at 𝗜𝗺𝗽𝗲𝗿𝗶𝗮𝗹 𝗖𝗼𝗹𝗹𝗲𝗴𝗲 𝗟𝗼𝗻𝗱𝗼𝗻. 𝗦𝗮𝗳𝗲 𝗪𝗵𝗼𝗹𝗲-𝗯𝗼𝗱𝘆 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀 𝗟𝗮𝗯 (𝗦𝗪𝗜𝗥𝗟) ( is a new research lab focused on the intersection of safety and intelligence in next-generation robotics. We're hiring exceptional PhD students who are passionate about pushing the boundaries of robot learning. 𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗦𝗪𝗜𝗥𝗟 𝘂𝗻𝗶𝗾𝘂𝗲? We operate at the exciting convergence of: • Online & offline reinforcement learning • Imitation learning & human demonstrations • Sample-efficient learning methods • Whole-body and soft robotics systems We're 𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝗳𝗼𝗿 𝗽𝗿𝗼𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲 𝗣𝗵𝗗 𝘀𝘁𝘂𝗱𝗲𝗻𝘁𝘀 interested in: • Developing safe exploration algorithms for robotic systems • Creating sample-efficient learning methods that minimize real-world trials • Building foundation models for robotics with safety guarantees • Advancing soft robotics and compliant human-robot interaction • Bridging theory and practice in embodied AI Why now? As robots become more capable and work closer with humans, we need systems that are both intelligent enough to handle complex tasks 𝗔𝗡𝗗 safe enough for real-world deployment. Traditional approaches treat safety and intelligence as competing priorities, we believe they're synergistic. If you're a motivated researcher who wants to develop the theoretical foundations and practical algorithms for tomorrow's safe, intelligent robots, I'd love to hear from you. Want to join? Apply viashow more

Memo is a robot that uses AI to perform... household tasks effectively. Today Sunday announced its Series B, and we’re proud to be investors. Training robots for the home is hard — the environment is messy, dynamic, and full of edge cases. So Sunday is training robots directly on real households. Founders Tony Zhao and Cheng Chi built a glove-based system that lets hundreds of contributors record everyday tasks in their own homes, creating high-fidelity demonstrations that feed directly into robot learning. Home robotics will be defined by the companies that learn fastest from real homes. Sunday is building that loop. More here: Aaref Hilaly Amanda Huangshow more

7. Learning new skills or mastering a new subject... Mega prompt: You are an expert educator specializing in [SUBJECT AREA]. Create a personalized learning plan for mastering [SKILL] in [TIMEFRAME]. My current level: [BEGINNER/INTERMEDIATE/ADVANCED] My goal: [WHAT I WANT TO ACHIEVE] Time available: [HOURS PER WEEK] Learning style: [HANDS-ON/READING/VIDEO/MIXED] Provide: 1. Learning roadmap with clear milestones 2. Week-by-week curriculum 3. Resources (free and paid) with links 4. Practice projects that build real skills 5. Common pitfalls and how to avoid them 6. Ways to validate learning (tests, projects, certifications) 7. 5 specific exercises I can do today Make it practical. I want to DO things, not just consume content. Context: [WHY YOU'RE LEARNING THIS, YOUR BACKGROUND]show more

💫 Are your ready to teach in the digital... age? 📘 Modern PBL: Project-Based Learning in the Digital Age' by Dan Jones is a guide that equips educators with the tools and strategies needed to thrive in a digital learning environment. #ModernPBLshow more