Built on our Data Foundation Model (DFM), delivering higher-quality, more scalable, and more efficient Ego+Fingers data production.Industry-first DFM enables end-to-end data processing#EmbodiedAI# #HumanData# #6DPose# #PhysicalAI# #RobotLearning# #EgoData#

Can an AI agent learn a pool table’s physics by playing on it? 🎱 Closing the visual gap between reality and simulation is one thing. Closing the dynamics gap is much harder. In this fun project, we let an agent take real shots, observe the outcomes, and iteratively improve its simulation of the physical world. The idea: rather than just observing the world, let agents actively experiment with it and learn its physics through interaction. A fun demonstration of a key capability for Physical AI: learning the physics of the real world through autonomous experimentation. A @corl_conf late breaking contribution. If you plan to attend #CoRL#, see you there! In collaboration with @XilunZhangXZ @MatthewFoutter @Luis_A_Pabon @Liam862373 Check it out: #PhysicalAI# #Robotics# #AgenticAI# #RobotLearning# @StanfordAILab @StanfordASL

How do we make RL-powered humanoid robots safe without sacrificing performance? In our latest #CoRL2026# paper, Constrained Whole-Body Tracking for Humanoid Robots, we introduce safety filters that enforce kinematic and dynamic constraints—even those never encountered during training. The approach minimally interferes with the underlying RL policy and requires *no retraining*. At CoRL in Austin? Come meet @danielpmorton at Poster Session 2, Tuesday evening, #204#! Project: Paper: Work led by @danielpmorton, in collaboration with Pranit Mohnot #HumanoidRobotics# #ReinforcementLearning# #RobotLearning# #PhysicalAISafety# #Robotics# @StanfordAILab @StanfordASL

Human motion. Human touch. A new chapter begins. Two next-generation products from debuting at CoRL 2026: 🔹 DAS Ego 2.0 — A first-person multimodal data collection system with hand-tracking precision within 4 mm. 🔹 DAS Dex 2.0 — A high-fidelity human dexterity data glove with e-skin tactile sensing. From whole-body motion to tactile sensing, perfectly in sync. Come experience it live. See you at Booth #200#! 📅 November 9–11, 2026 📍 110 E 2nd St, Austin, TX 78701, USA #CoRL2026# #GenRobotAI# #Robotics# #EgoData# #WholeBodyManipulation# #DexterousManipulation# #EmbodiedAI# #RobotLearning#

🤖 Great to meet robotics builders at our Embodied Intelligence Workshop at RoseLab, Toulouse! With SenseCraft Robotics, you can bring the robot arm workflow into one platform:🔗 Connect → 📊 Collect Data → 🧠 Train → 🚀 Deploy 🎁 New users get 1,000 free credits on first login. 👉 #SenseCraftRobotics# #SeeedStudio# #Robotics# #RobotLearning# #EmbodiedAI#

Can we steer a robot foundation model without retraining it? Our new paper explores this question through control-inspired notions of feature observability and controllability in Vision-Language-Action (VLA) models. We find that state- and action-relevant information can often be linearly predicted from VLA internal representations—and that these representations can be steered at inference time to change robot behavior, without fine-tuning. As an example, on a real robot, our approach increased the preferred handle-grasp rate from 14% to 74%, while adding only ~1% inference-time overhead. The broader idea is that representation-level control could provide a lightweight interface for adapting robot foundation models to new preferences and constraints—without retraining. Joint work with Hugo Buurmeijer Carmen Amo Alonso @SwannAiden Paper: #PhysicalAI# #Robotics# #VLA# #RobotLearning# @StanfordEng @StanfordAILab

How reliably can today’s robot policies complete tasks when the world changes mid-execution? In real-world robotics, things rarely go exactly as planned. Objects move, unexpected obstacles appear, and observations change as robots interact with their surroundings. Understanding how well robot policies handle these changes is essential for deploying Physical AI in the real world. In our new paper, LIBERO-MAX: Do Robot Policies Adapt When the World Changes?, we introduce a benchmark designed to study this question. We evaluate 14 state-of-the-art robot policies across 8,000 cases, introducing controlled changes after the robot has already begun acting. The results suggest that adapting to a changing world remains a key challenge for today’s robot policies. Task success drops by 11–26 percentage points, particularly when geometry or observations change, and simply updating policies more frequently does not close the gap. LIBERO-MAX provides a systematic way to measure these challenges, helping identify where current policies fall short and guiding the development of more adaptive and robust Physical AI systems. 📄 Paper: 🌐 Benchmark and project website: 💻 Code: Work led by @YunbeiZhang, in collaboration with Zijian Jin, Yuanzhe Liu, Janet Wang, Xilun Zhang, Yuyou Zhang, Zhenyu Zhang, Daoan Zhang, Shuaicheng Niu, Gen Li, Jianfei Yang, Jihun Hamm, Ismini Lourentzou, Weirui Ye, Bo Liu, and Peter Stone. #Robotics# #PhysicalAI# #RobotLearning# #RobotFoundationModels# #AutonomousSystems#

How do we make robot policies robust to rare but high-impact failures? Video #World# #Models# (WMs) are rapidly becoming a powerful tool for robotics, enabling policy evaluation and improvement by "imagining" future outcomes. But there's a catch: these imagined futures are typically nominal samples, making it easy to overlook the rare yet safety-critical events that matter most. In our new paper, StressDream: Steering Video World Models for Robust Policy Evaluation and Improvement, we explore a simple but powerful idea: 💡 Instead of passively sampling futures, actively steer world model imaginations toward high-impact yet still plausible scenarios. StressDream optimizes the initial diffusion noise at inference time, allowing us to generate targeted stress-test scenarios without retraining the world model. This enables: - More robust policy evaluation by exposing failure modes that random sampling often misses. - Improved policy optimization by training against challenging but realistic imagined futures. As generative world models become a foundation for #Physical# #AI#, the ability to systematically probe their "long tail" of plausible futures will be increasingly important for building reliable and trustworthy autonomous systems. 📌 𝖯𝗋𝗈𝗃𝖾𝖼𝗍 𝖯𝖺𝗀𝖾: 📄 𝖯𝖺𝗉𝖾𝗋: Work led by Junwon Seo, with a great set of collaborators: Sushant Veer, Thomas Ran Tian, Wenhao Ding, Apoorva Sharma, Karen Leung, Edward Schmerling, Andrea Bajcsy. @NVIDIADRIVE @NVIDIAAI #Robotics# #WorldModels# #PhysicalAISafety# #AISafety# #AutonomousSystems# #RobotLearnin#

🤖 Expensive robot data is scarce, so why not learn from our everyday first-person videos? This work turns human hand motion into robot actions to pretrain VLA models. 📰 Title: ACE-Ego-0: Unifying Egocentric Human and Robotic Data for VLA Pretraining 🔗 URL: 💡 Overview ACE-Ego-0 unifies robot demonstrations with egocentric human videos (Ego4D, EPIC-KITCHENS, and more) to pretrain Vision-Language-Action (VLA) models, trained on over 6,000 hours of combined data. 🔍 Challenges Solved Robot demonstrations are costly to collect, while human videos are cheap and abundant. But the two differ in action space, embodiment structure, temporal dynamics, and supervision quality, so naively mixing them breaks training. 🛠 Methodology & Proposed Approach ・Unifies actions in head-camera coordinates with 6D rotations, treating the human hand as an end-effector ・Encodes robot URDFs into morphology tokens via a GNN to absorb structural differences ・Chunks actions by consistent physical duration instead of fixed steps for temporal alignment ・Applies a reliability-weighted loss to noisy human videos, focusing on trustworthy position channels 📊 Use Cases / Results On RoboCasa it hits 72.8% average success (vs GR00T-N1.6 at 47.6%) and ~91% on RoboTwin 2.0. On a real bimanual robot it reaches 78.3% average (π0.5: 71.7%). Strikingly, on a task with only 34 robot demos, adding 419 human video episodes lifted success from 10% to 40%, a 4x gain. #RobotLearning# #VLA#