Lei Ye (Raymon Yip) @MasterYip3
PHD at Shanghai Institude of Technology (SII) & HIT Research Intern at Roboparty Lab AI & Robotics github.com/MasterYip China Joined February 2023-
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Different from motion planning and tracking, I-BFM uses FB representation to handle the box-carrying task. In the demonstration, the robot performs the pick-and-place task without palpable phase switching. FB makes the whole task into a "higher-level optimal control problem" that aims to move the box to the goal in any way with the lowest "energy".
Can humanoids recover in interaction tasks? 📦🤖 Excited to share I-BFM! Reward-conditioned policy: carry, push, kick. FB: robot–object–contact dynamics; LOGO: contact now → task goal later. G1 MuJoCo Carry: 94.3% → 89.3% after robot disturbance. 🌐 iamhardworking.github.io/I-BFM/
3DGS looks real—until you shift the camera or insert a car. 🚗 Excited to unveil SPVC, our joint work with @GWMGlobal! 4 upgrades to photorealistic driving video fixing: 🔹 Structured: Geometry-guided, not unconstrained. 🔹 Panoptic: Background + foreground, not just one. 🔹 Video: Temporally consistent, not frame-by-frame. 🔹 Cross-Dataset: One model across Waymo, nuScenes & PandaSet. One unified video diffusion fixer. See the difference! 👇 📄 Paper: arxiv.org/pdf/2608.17420 🌐 Project: li00147.github.io/SPVC-Project-P…
SGS shows unbelievable results in locomotion, it learns locomotion skills& behaviors without motion references
Our manipulation results were mind-blowing the first time I saw them, but earlier on, my favorite result was this locomotion run. Many great people have done parkour-type skills with locomotion already, but as far as I am aware, most, if not all, of these works trained separate
Our manipulation results were mind-blowing the first time I saw them, but earlier on, my favorite result was this locomotion run. Many great people have done parkour-type skills with locomotion already, but as far as I am aware, most, if not all, of these works trained separate policies for each terrain, and then distilled into a single policy. They also typically contained tons of hand-engineered rewards and curriculums. A very early motivation for SGS was to simply figure out if we could get similar results but get rid of as much of this hand-engineering as possible, and we managed to achieve that! This video shows a continuous 1-min run in much harder terrains than ever before, all solved by the same policy.
@ElijahGalahad the anymal example is Amazing too
imagine you can reject papers as a reviewer to win money?
A prediction market for peer review. Have we reached peak AI? 🤔
@newcomm Strongly recommand Unitree add Thor to their G1 configuration lol.
Can humanoid motion generation and control be unified in a single policy? PredActor uses a single end-to-end diffusion policy rather than separate motion generation and tracking. With proprioceptive feedback, it predicts internal future states and directly outputs actions at 50 Hz onboard a Unitree G1. Text-conditioned motion, joystick steering, disturbance response, and semantic interpolation are supported in the single policy. Project Page: masteryip.github.io/predactor.gith…
@mateoguaman truly amazing！Are there reference motions used for training? or it learns by it self about how to handle different type of terrains?
@ChenTessler many thanks for your feed back! I will refine the positioning of our data collection and training pipeline more precisely as soon as possible!
More Demonstration in MuJoCo Simulator, including Target tracking, Text Control, Text-Joy Combined Control, Disturbance. Mujoco evaluation codes can be found at the Repo: github.com/MasterYip/Pred…
PredActor supports Semantic Interpolation too:
@NliGjvJbycSeD6t Too Thrilled to Star!
More resources can be found: Project: masteryip.github.io/predactor.gith… GitHub: github.com/MasterYip/Pred… arXiv: arxiv.org/abs/2609.24840 HF: huggingface.co/MasterYip/Pred… YouTube: youtu.be/joIE_Iu7sPY?si… Discord Community: discord.gg/3ZsNAvzxZy
Unlike a motion-generator-plus-tracker pipeline, it uses proprioceptive feedback to jointly predict executable actions and an internal future-state trajectory. Only actions are executed; predicted states remain inside the policy as a guidance interface. Classifier-free guidance strengthens text-conditioned behaviors, while classifier guidance steers predicted states toward test-time objectives. Everything can be fully executed on Jetson Orin NX at 50 Hz.
@ChongZzZhang I've seen the recording the film at sii few weeks ago
@qiayuanliao This made me thought of something
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