Proud to co-organize the AlpaSim E2E Closed-Loop Challenge with @kesai_labs and NVIDIA’s ASPIRE Group. This post traces the journey from NAVSIM to photorealistic closed-loop evaluation—and our shared effort to make progress in autonomous driving measurable. Learn more ↓

Huge shout-out to our amazing organizing team @2318hhhz @wongfaikit @lucy_x_shi @smch_1127 Yasemin_Bekiroglu @francislee2020 — this wouldn't have happened without you! 🙌 📺 Our workshop recap is now live: RSS 2026 | Towards Robust Execution of Long-Horizon Whole-Body Control Tasks Catch the full playlist here 👇 youtube.com/playlist?list=PL… from @YouTube #rss226 #WholeBodyIntelligence #embodiedAI

Sydney-bound for #RSS2026, and not gonna lie, we’re buzzing! 🔥 On our slate: OpenDriveLab.com/rss2026/ 🎤 Dr. Hongyang Li — Early Career Spotlight 📄 Two papers we're proud of: RISE & EgoHumanoid 🤖 A humanoid doing real work on the floor - July 13th Morning Workshop Full rundown + where to find us 👇 medium.com/@opendrivelab/ope… Come have a yarn. #EmbodiedAI #WholebodyIntelligence #VLA #WorldModels #Sydney #ICC – at Sydney, New South Wales

Sydney, come sundown. 🌃 OpenDriveLab x @archon_robotics are throwing an afterparty! TopTalents for Embodied AI, a social mixer at W Sydney, harbour views, golden hour, and the people building the next gen of Embodied Intelligence. 📅 Jul 14 · 7–10PM 📍 W Sydney 🎟️ Apply: luma.com/0ml7kuwb #RSS2026 #EmbodiedAI #Sydney #WorldModel #FoundationModel – at Sydney, New South Wales

🚀 Join us at the #CVPR 2026 Workshop: From Labs to Life: Embodied Intelligence in the Wild (opendrivelab.com/cvpr2026/wo…) As embodied AI moves into the real world, we ask: how can agents perceive, reason, and act reliably in the wild? Featuring invited talks from: Hao Su, Zhiyu Huang, Jiahui Lei, Yilun Du, Rika Antonova, Jiatao Gu. 📅 9:00 AM - 5:30 PM, June 3, 2026 📍 Four Seasons 1, Colorado Convention Center. #CVPR #EmbodiedAI #PhysicalAI #Robotics

📢📢📢 Call for Contributions @ RSS 2026 Towards Robust Execution of Long-Horizon Whole-Body Control Tasks 🧑‍💻👩‍💻🧑‍💻 Speakers: - Javier Alonso-Mora (TU Delft) - Leslie Pack Kaelbling (MIT) - Shan Luo (King's College London) - Hamidreza Kasaei (University of Groningen) - Roberto Martín-Martín (UT Austin) - Fan Shi (NUS) 📝📝📝 Call for Contributions: We invite researchers to share their work with the community through submissions to the workshop in a variety of formats beyond traditional papers, including reports, demos, video, and etc. Submissions may include research papers or reports, but we equally welcome alternative formats such as videos demonstrating systems in action, demos, interactive artifacts, or other creative presentations of research ideas. We particularly welcome ongoing, preliminary, or exploratory work. 🌍🌍🌍 Website: opendrivelab.com/rss2026/wor… #RSS2026 #AI #Embodied

🚀 MM-Hand 1.0 Tech Report released: a 21-DoF multi-modal modular dexterous robotic hand with remote tendon-driven actuation. Motors are relocated outside the hand, freeing space for tactile sensors, joint encoders, in-palm stereo vision, and maintainable modular fingers. MM-Hand achieves 25N fingertip force through 1m tendon-sheath transmission and supports closed-loop joint control for dexterous manipulation research. Paper: arxiv.org/abs/2604.17245 Page: opendrivelab.com/MM-Hand #Robotics #DexterousHand #EmbodiedAI

Let's discuss in github.com/OpenDriveLab/Worl…

【5/5】That's all! Hope #WorldEngine brings you fresh insights into post-training infrastructure for Physical AI. If you find it useful, please give it a star 🌟！ Github: github.com/OpenDriveLab/Worl… Huggingface: huggingface.co/datasets/Open… Project Page: opendrivelab.com/WorldEngine… Teamwork from @OpenDriveLab Shoutout to @sephy_li @ilnehc @francislee2020 @CaojunWang @GeorgeL94133026 📢

【4/5】🚘Production-validation

【3/5】📺Visualization ① Our Behaviour World Model seeds from real-world cases to synthesize what your dataset is missing — cut-ins, sudden braking, edge-case interactions at scale. ② Post-training with World Engine: before vs. after. ③/④ The post-trained E2E model deployed in WorldEngine.

【2/5】🎉Highlights - Data-driven long-tail discovery: Failure-prone scenarios are automatically identified from real-world driving logs by the pre-trained agent itself — no manual design, no synthetic perturbations. Photorealistic interactive simulation via 3DGS: Each discovered scenario is reconstructed into a fully controllable, real-time-renderable simulation environment with independent dynamic agent manipulation. - Behavior-driven scenario generation: Leverages Behavior World Model (BWM) to generalize and synthesize diverse traffic variations from existing long-tail scenarios, expanding sparse safety-critical events into a dense, learnable distribution. - RL-based post-training on synthesized safety-critical rollouts substantially outperforms scaling pre-training data alone — competitive with a ~10× increase in pre-training data. - Production-scale validation: Deployed on a mass-produced ADAS platform trained on 80,000+ hours of real-world driving logs, reducing simulated collision rate by up to 45.5% and achieving zero disengagements in a 200 km on-road test.