Shaoting Zhu

Humanoid parkour on unseen, extremely challenging terrains... even replicating Boston Dynamics-style moves? 🤖 Introducing TTT-Parkour: A Real-to-Sim-to-Real framework enabling robots to master stakes, beams, and wedges in under 10 minutes! ⏱️ 🌐 Project: ttt-parkour.github.io

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

See our full-stack, instinct-level whole-body control toolkit for legged & humanoid robots at: x.com/ziwenzhuang_le…

• Edge-Aware Safety: Novel volumetric edge penalization prevents slipping on terrain edges. • Extremely dynamic: Robust traversal at up to 2.5 m/s! • Open-sourced: All codes for training, deployment are open-sourced! 👇 Check out the project page! project-instinct.github.io/hiking-in-the-…

🚀 Hiking in the Wild: A Scalable Perceptive Parkour Framework for Humanoids! Excited to release our latest work! We push the boundaries of humanoid agility in unstructured environments. Highlights: • Zero-Shot Sim-to-Real: No mapping, no external state estimation.

@michaelyuancb Congratulation!

After a long road, we finally achieve zero-shot human-to-robot motion transfer for DP&VLA🥳. Introduce MotionTrans, our first framework directly transfers motions of 13 human tasks to end-to-end policies (RGB-to-Action). motiontrans.github.io Scalable & Motion-Level Transfer!

🚀 VR-Robo: A Real-to-Sim-to-Real pipeline for RGB vision-based navigation & control in legged robots. 💡 Reconstruct realistic indoor scenes using RGB 🧠 Train RL policies with photorealistic simulation 🤖 Deploy directly on real visual robots! 🔗 vr-robo.github.io

🤖 Task decomposition via VLM 🔁 Closed-loop subtask execution 🦿 PAS: robust locomotion control 🎥 Real-world deployment & success across stairs, ramps, gaps, doors

🧠+🐶 = Terrain Navigation Introducing SARO: a Space-Aware Robot System that combines Vision-Language Models and RL-based control for robust quadruped navigation in 3D terrains 🌄 Catch us at Thursday 15:15pm at Room 309 👉 saro-vlm.github.io

④/④ We design a new Tiny Trap Benchmark in simulation, which consists of a 5m×60m runway and different tiny traps on it. Robots begin on the left side and must pass through tiny traps to reach the goal on the right side.

🚨 New work at #ICRA2025! Robust Robot Walker 🐾 We enable quadruped robots to pass tiny traps (bars, pits, poles) using only proprioception – no cameras, no depth! Catch us at Thursday 16:55pm in Room 305! 🔗 robust-robot-walker.github.io

③/④ During deployment, our policy achieves approximate omnidirectional movement by joystick commands without motion capture or other auxiliary localization techniques. A joystick is used to generate fake goal commands, consisting of constant values for Δ𝐺 and Δ𝑡.

②/④ We use explicit-implicit dual-state learning. The contact force is first encoded by a contact encoder to an implicit latent, and concatenated with explicit privileged state to the dual-state. In addition, we introduce a classification head to guide the policy in learning.

Enjoy generalizable robot segmentation and plug-and-play visual augmentation🥳! RoboEngine enables visual generalization and robustness in almost all scenes with data collected from just one scene! The simplest idea, but useful and effective😂. roboengine.github.io

@michaelyuancb Cool!