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Robots struggle with strict action rules…memory and symbols help them learn fast. [Project + Full video link ⬇️] Robots struggle when tasks require specific steps in a fixed order. What if memory helped them think symbolically and learn faster? Solving tasks like unlocking a door then opening it is...

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Ilir Aliu - eu/acc

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10,241 просмотров • 1 год назад •via X (Twitter)

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Can robots learn without training❓ [𝗜𝘁'𝘀 𝗼𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲𝗱 ⬇ ] Teaching robots to do complex tasks WITHOUT spending hours training them. Sounds cool, right? That's exactly what DIAL-MPC does! The first training-free method for whole-body torque control using full-order dynamics: ✅ Instantly checks if a robot's moves are right or wrong ✅ Adapts quickly to new tasks without needing extra training ✅ Could work hand-in-hand with other robot learning methods Robots are getting smarter AND faster without the need for long training sessions. Website: Paper: Code: Saw this first Haoru Xue ✈️ CVPR 🙏
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Can robots learn without training❓ [𝗜𝘁'𝘀 𝗼𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲𝗱 ⬇ ] Teaching robots to do complex tasks WITHOUT spending hours training them. Sounds cool, right? That's exactly what DIAL-MPC does! The first training-free method for whole-body torque control using full-order dynamics: ✅ Instantly checks if a robot's moves are right or wrong ✅ Adapts quickly to new tasks without needing extra training ✅ Could work hand-in-hand with other robot learning methods Robots are getting smarter AND faster without the need for long training sessions. Website: Paper: Code: Saw this first Haoru Xue ✈️ CVPR 🙏

Ilir Aliu

71,502 просмотров • 1 год назад

Placing objects sounds simple… until robots have to do it. This method makes it simple, fast & reliable. [Github ⬇️] Robotic object placement is tough, especially with stacking, hanging, or insertion. AnyPlace is a new two-stage method that uses only synthetic data and a vision-language model to teach robots where and how to place objects; even in the real world. Why this works ✅ Finds the right spot with help from vision-language models ✅ Handles stacking, insertion, and hanging with no real-world training ✅ Trained on synthetic data using Blender and IsaacSim ✅ Works in the real world without fine-tuning It shows that smart use of simulation and language models can make robotic placement tasks easier, faster, and more reliable. Github: Paper: Thank you for sharing Animesh Garg !
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Placing objects sounds simple… until robots have to do it. This method makes it simple, fast & reliable. [Github ⬇️] Robotic object placement is tough, especially with stacking, hanging, or insertion. AnyPlace is a new two-stage method that uses only synthetic data and a vision-language model to teach robots where and how to place objects; even in the real world. Why this works ✅ Finds the right spot with help from vision-language models ✅ Handles stacking, insertion, and hanging with no real-world training ✅ Trained on synthetic data using Blender and IsaacSim ✅ Works in the real world without fine-tuning It shows that smart use of simulation and language models can make robotic placement tasks easier, faster, and more reliable. Github: Paper: Thank you for sharing Animesh Garg !

Ilir Aliu - eu/acc

22,843 просмотров • 1 год назад

Robotics keeps hitting the same wall. Single task RL works, but... it does not scale to hundreds of tasks or new embodiments. This new paper looks like a real step toward fixing that. The team introduces MMBench, a benchmark with 200 tasks across many domains and robots, and Newt, a language conditioned world model trained online across all 200 tasks at once. The simple idea behind Newt: The model learns from demos to get the right priors It trains across many tasks through online interaction It uses language to ground the goal It adapts fast when a new task shows up What stood out to me: ✅ One model trained on 200 tasks at the same time ✅ Language conditioned control for both states and RGB ✅ Better data efficiency than strong baselines ✅ Strong open loop control ✅ Fast adaptation to new tasks and embodiments ✅ Full release of 200 checkpoints, 4000 demos, code, and benchmark This is a good push toward general control instead of one model per task. If you want the full paper: Project page: —- Weekly robotics and AI insights. Subscribe free:
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Robotics keeps hitting the same wall. Single task RL works, but... it does not scale to hundreds of tasks or new embodiments. This new paper looks like a real step toward fixing that. The team introduces MMBench, a benchmark with 200 tasks across many domains and robots, and Newt, a language conditioned world model trained online across all 200 tasks at once. The simple idea behind Newt: The model learns from demos to get the right priors It trains across many tasks through online interaction It uses language to ground the goal It adapts fast when a new task shows up What stood out to me: ✅ One model trained on 200 tasks at the same time ✅ Language conditioned control for both states and RGB ✅ Better data efficiency than strong baselines ✅ Strong open loop control ✅ Fast adaptation to new tasks and embodiments ✅ Full release of 200 checkpoints, 4000 demos, code, and benchmark This is a good push toward general control instead of one model per task. If you want the full paper: Project page: —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

70,090 просмотров • 7 месяцев назад

How can non-experts quickly teach robots a variety of tasks? Introducing HAND ✋, a simple, time-efficient method of training robots! Using just a **single hand demo**, HAND learns manipulation tasks in under **4 minutes**! 🧵
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How can non-experts quickly teach robots a variety of tasks? Introducing HAND ✋, a simple, time-efficient method of training robots! Using just a **single hand demo**, HAND learns manipulation tasks in under **4 minutes**! 🧵

Jesse Zhang

31,995 просмотров • 1 год назад

How can robots reliably place objects in diverse real-world tasks? 🤖🔍 Placement is tough—objects vary in shape and placement modes (such as stacking, hanging, and insertion), making it a challenging problem. We introduce AnyPlace, a two-stage method trained purely on synthetic data to predict diverse placement poses of unseen objects for real-world tasks. Read on for more👇
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How can robots reliably place objects in diverse real-world tasks? 🤖🔍 Placement is tough—objects vary in shape and placement modes (such as stacking, hanging, and insertion), making it a challenging problem. We introduce AnyPlace, a two-stage method trained purely on synthetic data to predict diverse placement poses of unseen objects for real-world tasks. Read on for more👇

Animesh Garg

24,662 просмотров • 1 год назад

To automate time-consuming tasks like household chores, robots must be precise & robust for very specific environments. With MIT’s “RialTo” method, users can scan their surroundings w/their phone so a robot can practice in a digital twin environment. This novel real-to-sim-to-real approach allows the machines to train much faster & safer than they would in the real world:
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To automate time-consuming tasks like household chores, robots must be precise & robust for very specific environments. With MIT’s “RialTo” method, users can scan their surroundings w/their phone so a robot can practice in a digital twin environment. This novel real-to-sim-to-real approach allows the machines to train much faster & safer than they would in the real world:

MIT CSAIL

16,626 просмотров • 1 год назад

Imagine robots finally handling cables and wires! 🔌 A new simulation now allows robots to practice handling flexible objects like cables, wires, and hoses in a virtual environment. Handling wires is especially difficult for robots because they are not rigid and behave unpredictably. This simulation captures realistic physics such as bending, tension, and complex movements, helping robots learn more effectively. 📚 Training with these simulated assets helps robots perform better when moving from virtual training to real-world tasks. Applications include electronic device assembly, automotive wire harness installation, smart home setup, and industrial wiring. This approach makes robot training more complete and prepares them for the challenges they will face in real environments.
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Imagine robots finally handling cables and wires! 🔌 A new simulation now allows robots to practice handling flexible objects like cables, wires, and hoses in a virtual environment. Handling wires is especially difficult for robots because they are not rigid and behave unpredictably. This simulation captures realistic physics such as bending, tension, and complex movements, helping robots learn more effectively. 📚 Training with these simulated assets helps robots perform better when moving from virtual training to real-world tasks. Applications include electronic device assembly, automotive wire harness installation, smart home setup, and industrial wiring. This approach makes robot training more complete and prepares them for the challenges they will face in real environments.

Lukas Ziegler

29,916 просмотров • 1 год назад

Introducing CQN: Coarse-to-fine Q-Network, a value-based RL algorithm for continuous control🦾Initialized with 20~50 demonstrations, it learns to solve real-world robotic tasks within 10 mins of training, without any pre-training and shaped rewards! (1/4)
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Introducing CQN: Coarse-to-fine Q-Network, a value-based RL algorithm for continuous control🦾Initialized with 20~50 demonstrations, it learns to solve real-world robotic tasks within 10 mins of training, without any pre-training and shaped rewards! (1/4)

Younggyo Seo

16,413 просмотров • 2 лет назад

RL is back! But is it always the best choice? In a new paper, we investigate under what circumstances neuroevolution outperforms RL in transfer learning tasks. See more details in the thread below 🧵 While NE performs best in simpler domains, it will be interesting to see if the lessons learned here can also be applied to more complex systems/tasks (LLMs?).
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RL is back! But is it always the best choice? In a new paper, we investigate under what circumstances neuroevolution outperforms RL in transfer learning tasks. See more details in the thread below 🧵 While NE performs best in simpler domains, it will be interesting to see if the lessons learned here can also be applied to more complex systems/tasks (LLMs?).

Sebastian Risi

11,130 просмотров • 11 месяцев назад

Spatial AI ( is building large real-world datasets to teach robots how to navigate the world and complete tasks. Their first open source dataset, SEA (Spatial Everyday Activities), is the largest curated egocentric dataset of people carrying out real tasks, with 10,000 hours of data.
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Spatial AI ( is building large real-world datasets to teach robots how to navigate the world and complete tasks. Their first open source dataset, SEA (Spatial Everyday Activities), is the largest curated egocentric dataset of people carrying out real tasks, with 10,000 hours of data.

Y Combinator

20,944 просмотров • 7 месяцев назад

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 Huang
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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 Huang

Bain Capital Ventures

21,327 просмотров • 3 месяцев назад

Zep AI (YC W24) is the new state of the art in agent memory. It's a memory layer for AI agents that continuously learns from user interactions and changing business data. By providing agents with a complete, holistic view of each user, Zep enables developers to build applications that tackle complex, personalized tasks. In research published today, Zep demonstrated that it delivers up to 18.5% higher accuracy with 90% lower latency when compared to tools like MemGPT, excelling in both the Deep Memory Retrieval (DMR) and LongMemEval benchmarks.
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Zep AI (YC W24) is the new state of the art in agent memory. It's a memory layer for AI agents that continuously learns from user interactions and changing business data. By providing agents with a complete, holistic view of each user, Zep enables developers to build applications that tackle complex, personalized tasks. In research published today, Zep demonstrated that it delivers up to 18.5% higher accuracy with 90% lower latency when compared to tools like MemGPT, excelling in both the Deep Memory Retrieval (DMR) and LongMemEval benchmarks.

Y Combinator

51,142 просмотров • 1 год назад

Congratulations also to Patrick 👍 for his #ICLR paper on Temporal Difference (TD) learning , in it We solve the decades-old puzzle of why TD can solve complex RL tasks that Gradient Descent cannot.
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Congratulations also to Patrick 👍 for his #ICLR paper on Temporal Difference (TD) learning , in it We solve the decades-old puzzle of why TD can solve complex RL tasks that Gradient Descent cannot.

Thuerey Group at TUM

22,074 просмотров • 1 год назад

Humanoid Robots finally stopped “turning around and forgetting everything” 🧠🤖 Alibaba DAMO Academy today open-sourced RynnBrain:the world's first truly embodied brain model with spatiotemporal memory. Interrupted tasks? Even after being interrupted, it can accurately recall where things were moved, predict trajectories… /🧵
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Humanoid Robots finally stopped “turning around and forgetting everything” 🧠🤖 Alibaba DAMO Academy today open-sourced RynnBrain:the world's first truly embodied brain model with spatiotemporal memory. Interrupted tasks? Even after being interrupted, it can accurately recall where things were moved, predict trajectories… /🧵

CyberRobo

11,638 просмотров • 4 месяцев назад

Stop endless app switching. You can now ask Gemini to complete complex tasks for you across two apps - with one prompt. Look up a location on Maps AND text it to a friend. Ask a question about a video AND save it to notes. What two-in-one tasks will you be asking Gemini?
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Stop endless app switching. You can now ask Gemini to complete complex tasks for you across two apps - with one prompt. Look up a location on Maps AND text it to a friend. Ask a question about a video AND save it to notes. What two-in-one tasks will you be asking Gemini?

Android

183,847 просмотров • 1 год назад

Turns out the "IDE but for notes and tasks" is also becoming my... IDE! More and more I just make notes and boards with specs of what needs to be built, connect a bunch of code agents to thymer all working in parallel, have them pick up open tasks, build it and update progress back in the notes.
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Turns out the "IDE but for notes and tasks" is also becoming my... IDE! More and more I just make notes and boards with specs of what needs to be built, connect a bunch of code agents to thymer all working in parallel, have them pick up open tasks, build it and update progress back in the notes.

Wim Cools

28,299 просмотров • 4 месяцев назад

System ID for legged robots is hard: (1) Discontinuous dynamics and (2) many parameters to identify and hard to "excite" them. SPI-Active is a general tool for legged robot system ID. Key ideas: (1) massively parallel sampling-based optimization, (2) structured parameter space, and (3) active exploration based on Fisher Information to collect the most informative data in real. SPI-Active provides an accurate robot model and effectively reduces the sim2real gap. In sim2real policy learning setting, it outperforms baselines by 42-63% in various quadruped & humanoid tasks. Led by Nikhil Sobanbabu Guanqi He
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System ID for legged robots is hard: (1) Discontinuous dynamics and (2) many parameters to identify and hard to "excite" them. SPI-Active is a general tool for legged robot system ID. Key ideas: (1) massively parallel sampling-based optimization, (2) structured parameter space, and (3) active exploration based on Fisher Information to collect the most informative data in real. SPI-Active provides an accurate robot model and effectively reduces the sim2real gap. In sim2real policy learning setting, it outperforms baselines by 42-63% in various quadruped & humanoid tasks. Led by Nikhil Sobanbabu Guanqi He

Guanya Shi

21,442 просмотров • 1 год назад

RoboBallet: End of Manual Robot Programming Google DeepMind/Intrinsic/UCL just cracked multi-robot coordination. Published in Science Robotics. 🤖What it does: – 8 robots, 40 tasks, zero collisions - all planned in seconds – 25% better motion plans than expert-designed solutions – 60% faster task completion when scaling from 4 to 8 robots – Works on factory layouts, it's never seen 👩‍💻 The tech: – Graph Neural Networks map robots/obstacles as connected nodes – Reinforcement learning trains on millions of scenarios – No manual programming required - just give it CAD files and tasks ❓Question: The bottleneck to robot collaboration has always been manual prototyping. Now that we can automate the automation what will this unlock?
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RoboBallet: End of Manual Robot Programming Google DeepMind/Intrinsic/UCL just cracked multi-robot coordination. Published in Science Robotics. 🤖What it does: – 8 robots, 40 tasks, zero collisions - all planned in seconds – 25% better motion plans than expert-designed solutions – 60% faster task completion when scaling from 4 to 8 robots – Works on factory layouts, it's never seen 👩‍💻 The tech: – Graph Neural Networks map robots/obstacles as connected nodes – Reinforcement learning trains on millions of scenarios – No manual programming required - just give it CAD files and tasks ❓Question: The bottleneck to robot collaboration has always been manual prototyping. Now that we can automate the automation what will this unlock?

Jack 🤖

41,633 просмотров • 9 месяцев назад