OpenDriveLab
232 posts
@OpenDriveLab
Official account for OpenDriveLab @hkuniversity and Beyond. We do cutting-edge research in Robotics, Autonomous Driving. Email: [email protected]
RISE (3/N) To address this bottleneck, we introduce RISE: Reinforcement learning via Imagination for SElf-improving robots. RISE shifts the learning environment from physical world to a Compositional World Model, which first emulates future observations for proposed actions, then evaluates imagined states to derive advantage for policy improvement.
🧐Applying world models to improve real-world policy on challenging manipulation tasks used to be considered out of reach. 😌After sustained effort, we’re now seeing encouraging progress. 🚀Thrilled to introduce RISE: Self-Improving Robot Policy with Compositional World Model opendrivelab.com/kai0-rl/ arxiv.org/abs/2602.11075 RISE is, to our knowledge, the first work to use a world model as an effective learning environment for challenging real-world manipulation, enabling policy improvement on tasks that demand high dynamics, dexterity, and precision. Incredible teamwork with @lin_kunyang111 @francislee2020 @YueXiangyu @HaoZhao_AIRSUN @smch_1127
Introducing #WorldEngine, github.com/OpenDriveLab/W…, a two-year long project. The missing infrastructure for Physical AI post-training in Autonomous driving. Open-source. Production-validated.
Introducing #WorldEngine, github.com/OpenDriveLab/W…, a two-year long project. The missing infrastructure for Physical AI post-training in Autonomous driving. Open-source. Production-validated.
Introducing #WorldEngine, github.com/OpenDriveLab/W…, a two-year long project. The missing infrastructure for Physical AI post-training in Autonomous driving. Open-source. Production-validated.
1/n 🎉 SimScale: Learning to Drive via Real-World Simulation at Scale 🤖 An innovative real-world simulation pipeline and a real-sim co-training strategy that significantly boost the robustness and generalization of any end-to-end planner. 📈 For the first time, we reveal the scaling effect of simulation data in autonomous driving: with zero extra real-world data, simply scaling up simulation alone can keep improving model performance!
Humanoid robots have been prisoners of the lab. We set them free — with human data. We present EgoHumanoid: The first endorsement of human-to-humanoid transfer for whole-body loco-manipulation. 🔗 Home: opendrivelab.com/EgoHumanoid 📑 Arxiv: arxiv.org/abs/2602.10106 🧵👇
