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Here’s a pretty weird and surprising result - retrieval-augmented generation works unreasonably well for robot learning – but only when parameterized using difference vectors! We introduce Difference-Aware Retrieval Policies for Imitation Learning (DARP), a simple, semi-parametric RAG architecture for imitation learning that achieves gains of up to 200% over... show more

Abhishek Gupta

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21,039 Aufrufe • vor 1 Monat •via X (Twitter)

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What's different between these two BC policies? It's the same architecture, training budget, and data collection setup — the only difference is the controller gains! Controller gains are an understudied design parameter in robot learning. In our new work (w/ antonia bronars*, Pulkit Agrawal), we show how they act as an inductive bias across BC, RL, and Sim2Real transfer, with real consequences on performance. Here's what we found 🧵 * Equal Contribution 📄arxiv: 🔗website:

What's different between these two BC policies? It's the same architecture, training budget, and data collection setup — the only difference is the controller gains! Controller gains are an understudied design parameter in robot learning. In our new work (w/ antonia bronars, Pulkit Agrawal), we show how they act as an inductive bias across BC, RL, and Sim2Real transfer, with real consequences on performance. Here's what we found 🧵 Equal Contribution 📄arxiv: 🔗website:

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In Knowledge Graphs for RAG, our latest short course, you’ll explore how knowledge graphs work, how to build with them, and create better retrieval augmented generation applications. Start today for free:

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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.

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So I hear that behavior cloning is all the rage now. What if we could do better, but with the same data? :) In CCIL, we show that imitation via BC is improved by synthesizing corrective labels to account for compounding error, without interactive oracles. Lets you do 👇! 🧵(1/9)

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🤔 How to fine-tune an Imitation Learning policy (e.g., Diffusion Policy, ACT) with RL? As an RL practitioner, I’ve been struggling with this problem for a while. Here’s why it’s tough: 1️⃣ Special designs (usually for multimodal action distributions) in modern IL models make them non-trivial to fine-tune by RL. 2️⃣ Large policy models + RL's poor sample efficiency = a nightmare But finally, we figured out a simple solution that works for any model architecture! 🌟 Check out our #ICLR2025 paper: “Policy Decorator: Model-Agnostic Online Refinement for Large Policy Models”, led by my amazing mentee Xiu Yuan. 🔗 🧵 Read more below!

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Tongzhou Mu 🤖🦾🦿

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This figure from HIL-SERL is one of the clearest visualisations of how RL learns differently from imitation learning. The difference comes down to this: imitation learning treats each (state, action) pair as independent. A correction at timestep 20 teaches nothing about timestep 19 or 21. RL propagates reward backward through time. One successful insertion updates the value estimate of every state along the trajectory. So RL builds a full map of "which states lead to success"; imitation learning just memorizes individual snapshots. Setup: a robot inserting a RAM stick into a motherboard slot. Each dot is an end-effector position (Y = lateral, Z = height). Starting position is randomized. Left to right = training progressing. Top row (RL): the policy builds a funnel. Broad at the top, narrowing into the target. It systematically fills in the state space, learning which paths lead to success from many different starting positions. Bottom row (imitation learning / HG-DAgger, same human data): sparse, diffuse, no funnel. The policy only learns near states the human demonstrated. Both have access to the same data, including human corrections, but a completely different structure emerges.

This figure from HIL-SERL is one of the clearest visualisations of how RL learns differently from imitation learning. The difference comes down to this: imitation learning treats each (state, action) pair as independent. A correction at timestep 20 teaches nothing about timestep 19 or 21. RL propagates reward backward through time. One successful insertion updates the value estimate of every state along the trajectory. So RL builds a full map of "which states lead to success"; imitation learning just memorizes individual snapshots. Setup: a robot inserting a RAM stick into a motherboard slot. Each dot is an end-effector position (Y = lateral, Z = height). Starting position is randomized. Left to right = training progressing. Top row (RL): the policy builds a funnel. Broad at the top, narrowing into the target. It systematically fills in the state space, learning which paths lead to success from many different starting positions. Bottom row (imitation learning / HG-DAgger, same human data): sparse, diffuse, no funnel. The policy only learns near states the human demonstrated. Both have access to the same data, including human corrections, but a completely different structure emerges.

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1/ 🤲 LeRobot has made low-cost robot learning widely accessible — but most policies are still blind to contact. Today we release LeFlexiTac: a tactile extension for the LeRobot platform using FlexiTac sensors. Make tactile robot learning as easy as possible. Project page: Code/docs:

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𝗥𝗼𝗯𝗼𝘁𝘀 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 𝗺𝗼𝗿𝗲 𝗱𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻𝘀. 𝗧𝗵𝗲𝘆 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝗳𝗮𝗶𝗹𝘂𝗿𝗲 — 𝗮𝗳𝘁𝗲𝗿 𝘄𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗵𝘂𝗺𝗮𝗻𝘀. Most robot learning systems assume failure is the end of learning. In our new work, we study whether robots can improve after deployment by learning from their own failures, without any human intervention, teleoperation, or corrective labels. The key idea is simple: human videos contain structure about how the world works. We use them to learn cross-embodiment representations of action, dynamics, and value, enabling a shared predictive space between human behavior and robot experience. This allows a new learning loop: 👉 pretrain on human videos 👉 deploy robot policy 👉 observe failures 👉 reinterpret failures using human priors 👉 improve autonomously We evaluate this across 7 real-world manipulation tasks, showing: 📈 40% → 81% success rate 🏆 Strong improvements over π0.6 RECAP and RISE ✔️ Zero human intervention during post-deployment improvement 🧬 Generalizes across robot embodiments and policy backbones A key finding is that explicit failure repair significantly outperforms failure reweighting, yielding substantially larger gains under identical data conditions (+25 pts vs +5 pts on the same π0.5 base policy). Overall, the results suggest a shift in how we think about robot learning: Human videos are not only for pretraining policies. They can provide the structure needed for continual self-improvement after deployment. 📄 Paper: 🌐 Project: I am grateful for working with the fantastic leads Hanzhi Chen and Anran Zhang, and our collaborators Simon Schaefer, Kejia Chen, Shi Chen, Daniel Cremers. Special thanks to Stefan Leutenegger for co-advising this project with me. ETH Zürich TU München Microsoft Check out Hanzhi's 🧵 for more details

𝗥𝗼𝗯𝗼𝘁𝘀 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 𝗺𝗼𝗿𝗲 𝗱𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻𝘀. 𝗧𝗵𝗲𝘆 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝗳𝗮𝗶𝗹𝘂𝗿𝗲 — 𝗮𝗳𝘁𝗲𝗿 𝘄𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗵𝘂𝗺𝗮𝗻𝘀. Most robot learning systems assume failure is the end of learning. In our new work, we study whether robots can improve after deployment by learning from their own failures, without any human intervention, teleoperation, or corrective labels. The key idea is simple: human videos contain structure about how the world works. We use them to learn cross-embodiment representations of action, dynamics, and value, enabling a shared predictive space between human behavior and robot experience. This allows a new learning loop: 👉 pretrain on human videos 👉 deploy robot policy 👉 observe failures 👉 reinterpret failures using human priors 👉 improve autonomously We evaluate this across 7 real-world manipulation tasks, showing: 📈 40% → 81% success rate 🏆 Strong improvements over π0.6 RECAP and RISE ✔️ Zero human intervention during post-deployment improvement 🧬 Generalizes across robot embodiments and policy backbones A key finding is that explicit failure repair significantly outperforms failure reweighting, yielding substantially larger gains under identical data conditions (+25 pts vs +5 pts on the same π0.5 base policy). Overall, the results suggest a shift in how we think about robot learning: Human videos are not only for pretraining policies. They can provide the structure needed for continual self-improvement after deployment. 📄 Paper: 🌐 Project: I am grateful for working with the fantastic leads Hanzhi Chen and Anran Zhang, and our collaborators Simon Schaefer, Kejia Chen, Shi Chen, Daniel Cremers. Special thanks to Stefan Leutenegger for co-advising this project with me. ETH Zürich TU München Microsoft Check out Hanzhi's 🧵 for more details

Oier Mees

11,985 Aufrufe • vor 15 Tagen

For Ghost Of Yōtei we rebuilt our frame to use a more general dynamic resolution algorithm, with upsampling, partly so we could take advantage of PSSR. PSSR works very well for us with only a few tweaks, including running conservative rasterization for small particles. By comparison, our standard upsampling algorithm requires significantly more hinting and authoring help to achieve good results. Here’s a side-by-side look. If you zoom in very close (16x before gif compression), you can see that PSSR does a better job reconstructing fine details on architecture and foliage. It is also more stable under motion.

For Ghost Of Yōtei we rebuilt our frame to use a more general dynamic resolution algorithm, with upsampling, partly so we could take advantage of PSSR. PSSR works very well for us with only a few tweaks, including running conservative rasterization for small particles. By comparison, our standard upsampling algorithm requires significantly more hinting and authoring help to achieve good results. Here’s a side-by-side look. If you zoom in very close (16x before gif compression), you can see that PSSR does a better job reconstructing fine details on architecture and foliage. It is also more stable under motion.

@Zuby_Tech

10,357 Aufrufe • vor 7 Monaten

Little feet, big imitation. When a young one perfectly mimics grandpa’s walk, you realize respect, love, and learning start at home .

Little feet, big imitation. When a young one perfectly mimics grandpa’s walk, you realize respect, love, and learning start at home .

AgriHopper

49,717 Aufrufe • vor 5 Monaten

Today we’re announcing our new Mentava Basics curriculum, aimed at kids who are still a bit too young for the standard Mentava curriculum. Here’s what people don’t understand about teaching a 2 or 3 year old to read: The necessary skills for reading don’t all develop at the same time. The ability to associate letters with sounds happens first, and at a pretty early age. If you think about it, learning “this funny shaped animal says moo” is pretty similar to learning “this funny shaped line says aaa.” However, the second necessary skill for reading is blending those letter sounds together, and kids often aren’t developmentally capable of doing that until at least 6-12 months later. Until today, our recommendation has been to wait until the child is developmentally ready to blend sounds. and then we just go full speed and teach them everything all at once, as fast as possible. But sometimes we have students who start a little younger. And then their parents are confused, because they see that their kids are having a ton of fun learning letter sounds super fast, but then are being gatekept from additional learning because they aren't yet developmentally ready to blend those sounds. That’s why we created the new Mentava Basics curriculum. Mentava Basics lets our youngest students focus on letter/sound pairings until they're developmentally ready to begin blending them. Mentava Basics takes the fun and delight of the core Mentava experience, but applies it to a curriculum that’s developmentally appropriate for even younger children. Mentava’s standard curriculum is still the fastest way to go from zero to reading, but with Mentava Basics now we can give kids a head start by helping them learn their letter sounds in advance. If your child is struggling with blending and you think it may just be a developmental readiness issue, you can use the grownup menu to switch into the Mentava Basics curriculum. We save your progress on both pathways, so you can switch back to our standard curriculum whenever you want.

Today we’re announcing our new Mentava Basics curriculum, aimed at kids who are still a bit too young for the standard Mentava curriculum. Here’s what people don’t understand about teaching a 2 or 3 year old to read: The necessary skills for reading don’t all develop at the same time. The ability to associate letters with sounds happens first, and at a pretty early age. If you think about it, learning “this funny shaped animal says moo” is pretty similar to learning “this funny shaped line says aaa.” However, the second necessary skill for reading is blending those letter sounds together, and kids often aren’t developmentally capable of doing that until at least 6-12 months later. Until today, our recommendation has been to wait until the child is developmentally ready to blend sounds. and then we just go full speed and teach them everything all at once, as fast as possible. But sometimes we have students who start a little younger. And then their parents are confused, because they see that their kids are having a ton of fun learning letter sounds super fast, but then are being gatekept from additional learning because they aren't yet developmentally ready to blend those sounds. That’s why we created the new Mentava Basics curriculum. Mentava Basics lets our youngest students focus on letter/sound pairings until they're developmentally ready to begin blending them. Mentava Basics takes the fun and delight of the core Mentava experience, but applies it to a curriculum that’s developmentally appropriate for even younger children. Mentava’s standard curriculum is still the fastest way to go from zero to reading, but with Mentava Basics now we can give kids a head start by helping them learn their letter sounds in advance. If your child is struggling with blending and you think it may just be a developmental readiness issue, you can use the grownup menu to switch into the Mentava Basics curriculum. We save your progress on both pathways, so you can switch back to our standard curriculum whenever you want.

Niels Hoven 🐮

10,497 Aufrufe • vor 1 Jahr

Yes, such a great BioRob meeting. Enjoyed catching up with everyone. Great to see all the RL+biomechanics here. Also honored that our work on imitation learning for gait analysis was another best paper finalist Nice updates in MuJoCo coming soon!

Yes, such a great BioRob meeting. Enjoyed catching up with everyone. Great to see all the RL+biomechanics here. Also honored that our work on imitation learning for gait analysis was another best paper finalist Nice updates in MuJoCo coming soon!

R. James Cotton

12,006 Aufrufe • vor 1 Jahr