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HuB is a unified framework that enables humanoids to execute complex balancing tasks. It integrates reference motion refinement, balance-aware policy learning, and sim-to-real robustness training. Project page:

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The Humanoid Hub

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53,251 görüntüleme • 1 yıl önce •via X (Twitter)

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Moytoy profil fotoğrafı
Moytoy1 yıl önce

Don't hurt it...teach it kindness and everything will be just fine. Right? @sierracatalina1

RTTS profil fotoğrafı
RTTS1 yıl önce

Testing Salesforce presents unique challenges due to its complexity, scalability and customizability. ​ RTTS can plan, design & automate a successful testing process for you.

Miguel Guau! profil fotoğrafı
Miguel Guau!1 yıl önce

¡Ese control es puro nivel pro! Luchar contra el gap sim-to-real es como pasar el jefe final en robótica. 🤖 Ojalá suelten algo open-source pronto.

Christopher Cook profil fotoğrafı
Christopher Cook1 yıl önce

Oof.

Paul profil fotoğrafı
Paul1 yıl önce

Insane progress.

Ellis Leonidas profil fotoğrafı
Ellis Leonidas1 yıl önce

This framework potentially enhances robot stability significantly.

Crypto Pioneer profil fotoğrafı
Crypto Pioneer1 yıl önce

This is a fascinating development in robotic motion control and balance enhancement.

df profil fotoğrafı
df1 yıl önce

What is happening here??? This video and the dancing one from Tesla which is currently trending are showing an incredible progress in motorics. The high torque and precise, low-latency control of these motors is absolutely insane.

Adam Dorfx profil fotoğrafı
Adam Dorfx1 yıl önce

Wow

Benzer Videolar

Proof that human videos can be converted into context-aware humanoid locomotion. VideoMimic is a real-to-sim-to-real pipeline that reconstructs humans and scenes from video, retargets motion to a humanoid, and trains a single policy in sim immediately deployable on real robots.
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Proof that human videos can be converted into context-aware humanoid locomotion. VideoMimic is a real-to-sim-to-real pipeline that reconstructs humans and scenes from video, retargets motion to a humanoid, and trains a single policy in sim immediately deployable on real robots.

The Humanoid Hub

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Amazon is training humanoids to move boxes. Makes sense! OmniRetarget is a data generation engine that enables complex loco-manipulation for humanoids. It uses offline retargeting from human MoCap datasets and augments data from single demos to produce 8 hours of trajectories that train RL policies in simulation, transferred zero-shot to real robots. Amazon FAR is the primary affiliation for the paper:
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Amazon is training humanoids to move boxes. Makes sense! OmniRetarget is a data generation engine that enables complex loco-manipulation for humanoids. It uses offline retargeting from human MoCap datasets and augments data from single demos to produce 8 hours of trajectories that train RL policies in simulation, transferred zero-shot to real robots. Amazon FAR is the primary affiliation for the paper:

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World Model meets robot policy! Robbyant's LingBot-VA: unifies video world modeling and robotic policy learning. - A single model generates both future video and the actions to make it real. - Long-term memory enables long-horizon tasks. - Claims significant outperformance over π₀.₅ in real-world tasks. - It's open-source
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World Model meets robot policy! Robbyant's LingBot-VA: unifies video world modeling and robotic policy learning. - A single model generates both future video and the actions to make it real. - Long-term memory enables long-horizon tasks. - Claims significant outperformance over π₀.₅ in real-world tasks. - It's open-source

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Chinese humanoid robotics company LimX Dynamics has unveiled COSA (Cognitive Operating System of Agents). COSA is described as a unified "brain-body" architecture that allows the robot to think and act simultaneously in the real world. It integrates: - high-level cognition (reasoning, planning, adaptation) - and whole-body motion control (low-latency dynamic locomotion, manipulation) It enables the Oli robot to act on natural language instructions and perform tasks that involve both walking and manipulation while being adaptive to interruptions.

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The AMO (Adaptive Motion Optimization) project, developed by researchers at UC San Diego, introduces a novel framework for enhancing the dexterity and adaptability of humanoid robots. This system integrates simulation-to-real reinforcement learning with trajectory optimization to achieve real-time, adaptive whole-body control Video Credit: USD #engineering #technology #robotics #robots
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The AMO (Adaptive Motion Optimization) project, developed by researchers at UC San Diego, introduces a novel framework for enhancing the dexterity and adaptability of humanoid robots. This system integrates simulation-to-real reinforcement learning with trajectory optimization to achieve real-time, adaptive whole-body control Video Credit: USD #engineering #technology #robotics #robots

Wevolver

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In order for robots to be deployed in the real world, performing tasks of real value, they must be reliable. Unfortunately, even more, most robotic demos work maybe 70-80% of the time at best. The way to get better reliability is to do real-world reinforcement learning: having the robot teach itself how to perform the task up to a high level of success. The key to doing this is to start with a core of expert human data, use that to train a policy then iteratively improve it, until finally finishing with on-policy reinforcement learning. Kun Lei talks through a unified framework for imitation and reinforcement learning based on PPO, which enables this improvement process. In this episode, Kun Lei explains the theory behind his reinforcement learning method and how it allowed his robot to run in a shopping mall juicing oranges for seven hours at a time, among experiments on a wide variety of tasks and embodiments. Watch episode 58 of RoboPapers now, hosted by Michael Cho - Rbt/Acc and Chris Paxton!

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Agility Robotics has developed a whole-body control foundation model for their humanoid robot, Digit, acting like a "motor cortex" to ensure safe, stable performance in diverse tasks. This small neural network (<1M parameters) is trained in NVIDIA's Isaac Sim for simulated decades over 3-4 days, transferring zero-shot to the real world. It handles balance, smooth motion, and precision manipulation while preventing falls. Prompted with free-space positions/orientations for arms and torso, it enables walking, picking/placing heavy objects, and robustness to disturbances. Downstream, it supports dexterous skills and LLM-coordinated behaviors.

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Introducing Holosoma (Greek for “whole body”): an easy-to-use and unified control stack that runs across humanoids (G1, T1), quadrupeds (Go2), multiple simulators (MuJoCo, MJLab, IsaacGym/Sim), any terrain, and tasks from velocity control to full-body tracking

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Today, we are introducing the Unified Balance Kit! It is a TypeScript SDK within App Kits that is built on top of Circle Gateway, a protocol that enables a unified USDC balance across supported blockchains and handles the underlying crosschain movement of funds when spending. Instead of stitching together different routing logics, developers can use this SDK to deposit, view, estimate, and spend from a unified USDC balance. Try it out yourself!
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Today, we are introducing the Unified Balance Kit! It is a TypeScript SDK within App Kits that is built on top of Circle Gateway, a protocol that enables a unified USDC balance across supported blockchains and handles the underlying crosschain movement of funds when spending. Instead of stitching together different routing logics, developers can use this SDK to deposit, view, estimate, and spend from a unified USDC balance. Try it out yourself!

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MotionGPT: Human Motion as a Foreign Language paper page: Though the advancement of pre-trained large language models unfolds, the exploration of building a unified model for language and other multi-modal data, such as motion, remains challenging and untouched so far. Fortunately, human motion displays a semantic coupling akin to human language, often perceived as a form of body language. By fusing language data with large-scale motion models, motion-language pre-training that can enhance the performance of motion-related tasks becomes feasible. Driven by this insight, we propose MotionGPT, a unified, versatile, and user-friendly motion-language model to handle multiple motion-relevant tasks. Specifically, we employ the discrete vector quantization for human motion and transfer 3D motion into motion tokens, similar to the generation process of word tokens. Building upon this "motion vocabulary", we perform language modeling on both motion and text in a unified manner, treating human motion as a specific language. Moreover, inspired by prompt learning, we pre-train MotionGPT with a mixture of motion-language data and fine-tune it on prompt-based question-and-answer tasks. Extensive experiments demonstrate that MotionGPT achieves state-of-the-art performances on multiple motion tasks including text-driven motion generation, motion captioning, motion prediction, and motion in-between.

AK

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0:42

Humanoids finally move like humans… and can do more than copy. [Details + demos in thread 👇] A new framework, BeyondMimic, shows how to learn naturalistic whole-body control from human motion. But then goes further by composing those skills into versatile, zero-shot policies. Two key pieces make it work: •Motion tracking pipeline: robustly turns human mocap into dynamic robot motions (jump spins, sprinting, cartwheels) on real hardware •Guided diffusion policy: distills motion primitives and enables zero-shot downstream tasks with simple cost functions The result: ✅ State-of-the-art motion quality from mocap tracking ✅ Versatile task execution; waypoint navigation, joystick teleop, obstacle avoidance ✅ Stable sim-to-real transfer for diverse humanoid skills Thank you for sharing, Qiayuan Liao! 📍 Paper: Video:

Ilir Aliu - eu/acc

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Remarkably lifelike motion and fluidity. BeyondMimic is a framework for training humanoid whole-body control from large mocap datasets. First, an open-source motion-tracking pipeline to reproduce diverse, highly dynamic human skills on real hardware, then distilling them into a guided state-action diffusion model for zero-shot, task-specific control. Project page:

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Scientists have developed a neural network-based planning framework that enables robots to navigate complex environments and mazes autonomously. Learn more in Science Robotics:
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Scientists have developed a neural network-based planning framework that enables robots to navigate complex environments and mazes autonomously. Learn more in Science Robotics:

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#AGIBOTAIWeek Day4: Introducing GE-Sim 2.0 (Genie Envisioner World Simulator 2.0) , a comprehensive embodied world simulator for robotic manipulation. By integrating future video generation, proprioceptive state estimation, and reward-based policy assessment, GE-Sim 2.0 moves beyond visual simulation toward an embodied world simulator with built-in evaluation capability, laying the foundation for a world-model-centric platform for closed-loop and interactive policy learning. It provides a practical framework for scalable evaluation and training of robotic manipulation policies. Project page: #AGIBOT #AGIBOTAIWeek #GenieEnvisioner #Robotics #visualsimulation

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🕸️ Introducing SPIDER — Scalable Physics-Informed Dexterous Retargeting! A dynamically feasible, cross-embodiment retargeting framework for BOTH humanoids 🤖 and dexterous hands ✋. From human motion → sim → real robots, at scale. 🔗 Website: 🧵 1/n

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Researchers have developed Humanoid-GPT, a new AI system that helps humanoid robots perform complex movements and tasks. Tested on the Unitree G1 robot, it enables real-time whole-body control and can perform new actions without special training. During demonstrations, the robot successfully carried out a variety of movements and tasks, showing strong potential for real-world applications.

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3:15

🤖 Ever dreamed of controlling a humanoid robot to perform complex, long-horizon tasks — using just a single Vision Pro? 🎉 Meet CLONE: a holistic, closed-loop, whole-body teleoperation system for long-horizon humanoid control! 🏃‍♂️🧍 CLONE enables rich and coordinated interactive tasks: 🥊 boxing 🏓 table tennis 🤲 object pickup 📦 room arrangement 🤝 handover … and more! 🌀 Our closed-loop error correction powered by LiDAR odometry ensures precision, while motion-captured demonstrations supercharge policy learning — unlocking the full potential of the G1 robot. 🎥 It’s hard to squeeze the magic into 1 minute — check out the full video demo and project page here: 🔗

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How to teach robots to perform long-horizon tasks efficiently and robustly🦾? Introducing MimicPlay - an imitation learning algorithm that uses "cheap human play data". Our approach unlocks both real-time planning through raw perception and strong robustness to disturbances!🧵👇
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How to teach robots to perform long-horizon tasks efficiently and robustly🦾? Introducing MimicPlay - an imitation learning algorithm that uses "cheap human play data". Our approach unlocks both real-time planning through raw perception and strong robustness to disturbances!🧵👇

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