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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.

Science Magazine

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56,716 次观看 • 12 天前 •via X (Twitter)

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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.

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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:

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:

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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 🔗 #NVIDIAResearch

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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:

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🚨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 🧵⬇️

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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.

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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 via

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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.

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.

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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]

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These are not CGI. Reinforcement learning is so back. When operating on strings, it gives us o3. When operating on physical motors, it gives us a perfect humanoid backflip and a robot creature that out-maneuvers almost every animal on earth. RL is one of the only learning algorithms that can master both the world of bits and the world of atoms. Give me a reward function, and I shall move the world. 2025, Year of RL.

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