Runhan Huang

High-quality motion reference data is key for humanoid skill learning 🤖🕺💃 A natural idea is to leverage human motions and “translate” them to humanoid motions, a process known as retargeting. For interaction-rich tasks such as scene interaction and loco-manipulation, retargeting is challenging: it must ensure motion consistency, smoothness, kinematic feasibility (no artifacts like penetration or foot skating), and scalability (one framework can handle thousands of motions). Excited to release OmniRetarget — a scalable retargeting method with a 4-hour high-quality humanoid motion dataset for interaction-rich tasks. OmniRetarget takes an interaction-preserving perspective: we optimize Laplacian deformation between source and target interaction meshes while enforcing kinematic constraints, producing consistent, smooth, and feasible trajectories at scale. Even better, OmniRetarget can efficiently augment motions by varying terrains, objects, and initial poses. This high-quality interaction-preserving retargeting enables a minimal RL setup to execute long-horizon (up to 30s) agile, interaction-rich skills. All tasks in the video share just 5 rewards, 4 domain randomization terms, and rely only on proprioception. More details: omniretarget.github.io

very excited for the 4D Digital Twins workshop happening tomorrow #CVPR2026 ! we have an amazing set of speakers talking about 4D real-to-sim-to-real challenges🦾 🗓️ Thurs June 4 · 1:00 – 6:00 PM 📍 Mile High 2C 🔗 research.nvidia.com/labs/amri/proj…

We're thrilled to organize the 2nd Workshop on Agents in Interactions: From Humans to Robots! Submit your best work by May 8 and join us at CVPR in Denver to discuss research in this exciting space w/ @yufei_ye @DandanShan_ @jiaman01 @xiaolonw Alan Yuille

We introduce PDI-Bench🤩, a benchmark for quantitatively evaluating geometric consistency in video world model by uplifting 2D video pixel dynamics into 3D space.😀😉🥰 Paper:arxiv.org/pdf/2605.15185 Project Page:pdi-bench.github.io @xyz2maureen & @Yuheng120766

Are you still running your robot policies on vision encoders trained purely on static images? Nowadays, the standard practice in robot learning is to plug in powerful vision models like CLIP, SigLIP, or DINOv2. This inherits a quiet, convenient assumption: “Let mainstream computer vision handle perception, and the downstream policy will figure out the dynamics.” But let’s be real for a moment. Is this truly the best we can do? We introduce DynaFLIP: Rethinking Robotics Perception via Tri-Modal-Dynamics Guided Representation.⬇️ 🔷 Dynamics upstream: we push motion understanding into perception. 🔷 Tri-modal-dynamics supervision: image transitions × language × 3D flow, fused via simplex-volume alignment (260K trajectories from robot & human video) 🔷 Transfers everywhere: a visual backbone for diverse policies (MLP, Diffusion Policy, VLA) 🔷 +22.5% over the strongest baseline (DINOv2, SigLIP) under real-world OOD 🔷 Open-Source & easy to use 🌐 Website: dynaflip-robotics.github.io 📄 Paper: arxiv.org/abs/2605.30350 💻 Code: github.com/JU-SUK/DynaFLIP 🤗 Hugging Face: huggingface.co/jlee-larr/dyna…

Flexible Locomotion Learning with Diffusion Model Predictive Control Excited to share that our paper has been accepted to #ICRA2026 @ieee_ras_icra! A diffusion-planning framework for flexible real-world quadruped locomotion. Instead of learning a fixed RL policy or relying on hand-crafted dynamics for MPC, we train a diffusion trajectory prior that jointly predicts future states and actions. Key Ideas: Diffusion-MPC: A diffusion planner unlocks flexible locomotion through test-time reward and constraint adaptation Interactive reward-weighted finetuning enables continual behavior refinement from online environment feedback Real-world deployment on Unitree Go2 with efficient and adaptive planning The same planner can adapt at test time to height changes, posture/joint constraints, balancing under external disturbances, energy-aware locomotion, and zero-shot outdoor walking on grass and slopes. 🌐Homepage: flexible-diffusion-mpc.github.io 📖Paper: arxiv.org/abs/2510.04234 🔗Code: github.com/hrh6666/Flexib… This work is by @RunhanH, Haldun Balim, @hankyang94 , and @du_yilun. #ICRA2026 #Robotics #LeggedRobots #RobotLearning #DiffusionModels #MPC #MachineLearning

Flexible Locomotion Learning with Diffusion Model Predictive Control Excited to share that our paper has been accepted to #ICRA2026 @ieee_ras_icra! A diffusion-planning framework for flexible real-world quadruped locomotion. Instead of learning a fixed RL policy or relying on hand-crafted dynamics for MPC, we train a diffusion trajectory prior that jointly predicts future states and actions. Key Ideas: Diffusion-MPC: A diffusion planner unlocks flexible locomotion through test-time reward and constraint adaptation Interactive reward-weighted finetuning enables continual behavior refinement from online environment feedback Real-world deployment on Unitree Go2 with efficient and adaptive planning The same planner can adapt at test time to height changes, posture/joint constraints, balancing under external disturbances, energy-aware locomotion, and zero-shot outdoor walking on grass and slopes. 🌐Homepage: flexible-diffusion-mpc.github.io 📖Paper: arxiv.org/abs/2510.04234 🔗Code: github.com/hrh6666/Flexib… This work is by @RunhanH, Haldun Balim, @hankyang94 , and @du_yilun. #ICRA2026 #Robotics #LeggedRobots #RobotLearning #DiffusionModels #MPC #MachineLearning

Flexible Locomotion Learning with Diffusion Model Predictive Control Excited to share that our paper has been accepted to #ICRA2026 @ieee_ras_icra! A diffusion-planning framework for flexible real-world quadruped locomotion. Instead of learning a fixed RL policy or relying on hand-crafted dynamics for MPC, we train a diffusion trajectory prior that jointly predicts future states and actions. Key Ideas: Diffusion-MPC: A diffusion planner unlocks flexible locomotion through test-time reward and constraint adaptation Interactive reward-weighted finetuning enables continual behavior refinement from online environment feedback Real-world deployment on Unitree Go2 with efficient and adaptive planning The same planner can adapt at test time to height changes, posture/joint constraints, balancing under external disturbances, energy-aware locomotion, and zero-shot outdoor walking on grass and slopes. 🌐Homepage: flexible-diffusion-mpc.github.io 📖Paper: arxiv.org/abs/2510.04234 🔗Code: github.com/hrh6666/Flexib… This work is by @RunhanH, Haldun Balim, @hankyang94 , and @du_yilun. #ICRA2026 #Robotics #LeggedRobots #RobotLearning #DiffusionModels #MPC #MachineLearning