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A big part of scaling robot learning to solve real-world problems is that we somehow need to get enough diverse, high-quality data to train our robots to perform useful things. GPT and its fellow large language models were bootstrapped and proved out on a massive dataset of real-world language... data. Unfortunately, despite our best efforts, similarly massive datasets don’t really exist for robotics — so, in our unending pursuit of high-quality, useful data, we turn to simulation. I compared a couple recent works on sim-to-real robot manipulation, which discuss how to train perception-driven manipulation policies in simulation, in such a way that they’re useful in the real world. - DextraH-RGB, from NVIDIA - Sim-and-Real Co-Training: A Simple Recipe for Vision-Based Robotic Manipulation, also from NVIDIA — specifically the GEAR lab - Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids, another GEAR lab paper - Local Policies Enable Zero-shot Long-Horizon Manipulation, from CMU (video from DextrAH-RGB)show more

Chris Paxton

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20,486 Aufrufe • vor 1 Jahr •via X (Twitter)

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11 Kommentare

Profilbild von Chris Paxton

Chris Paxtonvor 1 Jahr

Learning Robotic Manipulation from Simulations A comparison of a few recent works on sim-to-real robot manipulation that I liked

Profilbild von VistaShares

VistaSharesvor 1 Jahr

From semiconductors to data centers, AIS targets the critical components behind AI's exponential growth. Capture potential returns from this transformative technology sector.

Profilbild von Kyle🤖🚀🦭

Kyle🤖🚀🦭vor 1 Jahr

uncontested Isaac superiority 🫡 (very excited for warp too)

Profilbild von ahad

ahadvor 1 Jahr

what are you thoughts on a model that you just shovel in alot multimodal data and how much of a ratio you’ll need to get good performance? for example recent work from @physical_int they made an architecture where they can predict web data and predict actions.

Profilbild von Chris Paxton

Chris Paxtonvor 1 Jahr

Sim to real is definitely not the only way to do this I wrote a previous post that mentioned this; I wrote the two together so they reference the same paper on how much sim data you "need": I think it's worth a deeper look. but my guess is that sim and real data are fulfilling different needs: - sim data is generally very good at capturing robot planning and kinematics - video data is really good at capturing semantics and information about the world - robot data captures everything but isn't diverse enough and is too expensive (although people are trying to change that)

Profilbild von Godwyll Aikins

Godwyll Aikinsvor 1 Jahr

Excited for real2sim to get easier! Just using your phone + lidar to capture your workspace to port to simulation could be a game-changer for sim2real. Learned simulators could maybe bridge that gap as well

Profilbild von Bahram Banisadr

Bahram Banisadrvor 1 Jahr

What do you think about the sim2real transfer strategies & trying to scale up data that way, versus the strategies of training on real, non-robot data, which is readily available? I'm thinking about things like DexMachina learning from human ego-centric demonstration (could put smart glasses on people doing their everyday jobs) or Meta's V-JEPA 2 model that relies on the massive corpus of 'things happening in the world' to build a foundation model that has physical understanding.

Profilbild von Richa Sharma

Richa Sharmavor 1 Jahr

@chris_j_paxton I'm building browser-native generalized physics simulators and would love to chat about async agentic acceleration and scaling sim data. The current pipeline is incredibly fragmented.

Profilbild von Chris Paxton

Chris Paxtonvor 1 Jahr

Yeah interesting!

Profilbild von Martin Matak

Martin Matakvor 1 Jahr

yes, your "probably" is correct: we modeled the table geometry as a collision in dextrah (the original one, wasn't part of the rgb extension)

Profilbild von Rawlala

Rawlalavor 1 Jahr

the comparison doesn't seem to hold the amount of info u can infer from the universe is literally (uncountably) infinite, and the amount of knowledge we already discover is huge already, hence the necessity for gpt to have access to huge amount of data but for physical task ?..

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So we did a bunch of projects with real world reinforcement learning - but it was often too inefficient to be practical to train tabula rasa. This suggests we need better priors, but acquiring these from on-robot data can often be expensive as well. In our recent work, we show that despite being fundamentally inaccurate, simulation can guide provide a cheap way to guide real-world RL finetuning to be super efficient! We propose Simulation-Guided Fine-Tuning (SGFT) - a simple paradigm for sim2real finetuning that uses simulation to provide reward shaping that accelerates real world RL finetuning beyond just providing an initialization. TLDR: Use value functions from sim to shape rewards for real-world RL, see large sample efficiency improvements 🧵(1/6)

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39,960 Aufrufe • vor 9 Monaten

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Trained a humanoid entirely in a 3D scan of the office. Zero real-world fine-tuning. It just walked in and worked. RL needs hundreds of thousands of attempts, and real robots can't afford to crash. A misjudged gap or a glass door collision breaks hardware and costs hours resetting. So you train in a sim. But sim policies usually train on randomized, untextured geometry; depth is easy to fake. The robot learns structure, not the real world: no materials, no lighting, no idea what anything actually is. RGB cameras carry all of that but training RGB policies in generic fake worlds won’t generalize to the real world. Niantic Spatial 🌎 Scaniverse reconstructs your scan of the real deployment site. One 360° camera walkthrough → photorealistic 3D Gaussian splat at metric scale → collision mesh pulled from the same reconstruction, so vision and physics match exactly. Drops straight into NVIDIA Isaac Sim/Lab, no manual conversion. Flexion simulation-first approach then seamlessly enables the training of RGB-only nav policies inside that reconstruction. With added domain randomization + large image encoders for robustness, this deploys straight to hardware. No real-world fine-tuning. Deployment: months of on-site adaptation → days. Tune into the NVIDIA livestream on 12 August to hear how these companies are closing the sim2real gap: NVIDIA Robotics ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

117,692 Aufrufe • vor 5 Tagen

Testing robot policies on hardware is slow, expensive and hard to scale. World models offer a promising path to accelerating robot policy development. We're sharing new research from the Runway Robotics team, in which we simulated 8 robot policies inside our General World Model and found 0.95 correlation with real-world results. Those early results point to world model simulation as a practical substitute for hardware evaluation, comparing favorably to existing real-to-sim approaches. Learn more at the link below.

Testing robot policies on hardware is slow, expensive and hard to scale. World models offer a promising path to accelerating robot policy development. We're sharing new research from the Runway Robotics team, in which we simulated 8 robot policies inside our General World Model and found 0.95 correlation with real-world results. Those early results point to world model simulation as a practical substitute for hardware evaluation, comparing favorably to existing real-to-sim approaches. Learn more at the link below.

Runway

13,464 Aufrufe • vor 5 Monaten

Historically, RL policies for robots have been trained in synthetic, untextured environments, limiting perceptive policies to depth images where the sim-to-real gap is manageable. RGB has always had more potential, but leveraging it to train policies in simulation remained an open problem. Partnering with Niantic Spatial 🌎 and NVIDIA Robotics, we built a pipeline that addresses exactly that. We can now scan a real deployment site with off-the-shelf hardware, reconstruct it into a photorealistic Gaussian splat, and run massively parallel RL training. The policies trained in our Gym environment then transfer zero-shot to the real robot and environments they were trained for. This enables faster deployment of more capable and robust policies for the end user. The new resulting capabilities are a big step towards solving sim-to-real and also apply well beyond navigation. Read the full technical breakdown on our blog; link in the comments. #HumanoidRobots #Flexion #NianticSpatial #NVIDIA

Historically, RL policies for robots have been trained in synthetic, untextured environments, limiting perceptive policies to depth images where the sim-to-real gap is manageable. RGB has always had more potential, but leveraging it to train policies in simulation remained an open problem. Partnering with Niantic Spatial 🌎 and NVIDIA Robotics, we built a pipeline that addresses exactly that. We can now scan a real deployment site with off-the-shelf hardware, reconstruct it into a photorealistic Gaussian splat, and run massively parallel RL training. The policies trained in our Gym environment then transfer zero-shot to the real robot and environments they were trained for. This enables faster deployment of more capable and robust policies for the end user. The new resulting capabilities are a big step towards solving sim-to-real and also apply well beyond navigation. Read the full technical breakdown on our blog; link in the comments. #HumanoidRobots #Flexion #NianticSpatial #NVIDIA

Flexion

71,065 Aufrufe • vor 25 Tagen

📢📢📢 Excited to release ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning (CVPR25). 🤏🤙✌️With ManipTrans, we can transfer dexterous manipulation skills into robotic hands in simulation and deploy them on a real robot, using a residual policy learned for dex manipulation. 🤖🤖🤖The video below illustrates how the MoCap data can be transferred to Inspire, Shadow, Xhand, Allegro, and Mano. With ManipTrans, we can scale up dex manip data greatly with minimal effort. For more details, please check our -webpage: -code: -huggingface:

📢📢📢 Excited to release ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning (CVPR25). 🤏🤙✌️With ManipTrans, we can transfer dexterous manipulation skills into robotic hands in simulation and deploy them on a real robot, using a residual policy learned for dex manipulation. 🤖🤖🤖The video below illustrates how the MoCap data can be transferred to Inspire, Shadow, Xhand, Allegro, and Mano. With ManipTrans, we can scale up dex manip data greatly with minimal effort. For more details, please check our -webpage: -code: -huggingface:

Siyuan Huang

20,975 Aufrufe • vor 1 Jahr

The Real-to-Sim part of R2S2R transforms physical robots, sensors, environments, objects, and interactions into simulations that preserve task-relevant observations and dynamics - not only how the world looks, but how it acts when the robot interacts with it. These results set a new bar for sim-real alignment in robot manipulation.

The Real-to-Sim part of R2S2R transforms physical robots, sensors, environments, objects, and interactions into simulations that preserve task-relevant observations and dynamics - not only how the world looks, but how it acts when the robot interacts with it. These results set a new bar for sim-real alignment in robot manipulation.

Fei-Fei Li

24,736 Aufrufe • vor 17 Tagen

🔥 #ICRA2026 Best Paper Finalist The era of "robot VLA = single-arm gripper" is ending. Introducing Dexora — the first open-source Vision-Language-Action system for dual-arm, dual-hand, 36-DoF dexterous manipulation. 🦾 Dual Arms 🖐️ Dual Hands 🎯 36 DoF Control 🌍 Open Source Trained on: • 100K simulated trajectories • 10K real-world demonstrations Dexora achieves: ✓ 90%+ success on basic manipulation ✓ Strong dexterous manipulation performance ✓ Cross-embodiment generalization Our key hypothesis: Train on the hardest embodiment. Transfer to simpler robots later. Instead of scaling up gripper policies, we train directly in the most expressive action space and project downward to simpler embodiments. This may be a practical path toward universal robot controllers. 🎥 Demos: 📄 Paper:

🔥 #ICRA2026 Best Paper Finalist The era of "robot VLA = single-arm gripper" is ending. Introducing Dexora — the first open-source Vision-Language-Action system for dual-arm, dual-hand, 36-DoF dexterous manipulation. 🦾 Dual Arms 🖐️ Dual Hands 🎯 36 DoF Control 🌍 Open Source Trained on: • 100K simulated trajectories • 10K real-world demonstrations Dexora achieves: ✓ 90%+ success on basic manipulation ✓ Strong dexterous manipulation performance ✓ Cross-embodiment generalization Our key hypothesis: Train on the hardest embodiment. Transfer to simpler robots later. Instead of scaling up gripper policies, we train directly in the most expressive action space and project downward to simpler embodiments. This may be a practical path toward universal robot controllers. 🎥 Demos: 📄 Paper:

Hao Zhao

17,048 Aufrufe • vor 2 Monaten