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Introduce HumanPlus - Shadowing part Humanoids are born for using human data. We build a real-time shadowing system using a single RGB camera and a whole-body policy for cloning human motion. Examples: - boxing🥊 - playing the piano🎹/ping pong - tossing - typing Open-sourced!

Zipeng Fu

13,227 subscribers

230,204 次观看 • 2 年前 •via X (Twitter)

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Zipeng Fu 的头像

Zipeng Fu2 年前

Which hardware platform should HumanPlus be embodied on? We build our own 33-DoF humanoid with two dexterous hands using components: - Inspire-Robots RH56DFX hands - @UnitreeRobotics H1 robot - @ROBOTIS Dynamixel motors - @Razer webcams We open-source our hardware design.

Zipeng Fu 的头像

Zipeng Fu2 年前

Naively copying joints from humans to humanoids does not work due to gravity and different actuations. We train a transformer-based whole-body RL policy in IsaacGym simulation with realistic physics using AMASS dataset containing 40 hours of human motion:

Zipeng Fu 的头像

Zipeng Fu2 年前

To retarget from humans to humanoids, we copy the corresponding Euler angles from SMPL-X to our humanoid model. We use open-sourced SOTA human pose and hand estimation methods (thanks!) - WHAM for body: - HaMeR for hands:

Zipeng Fu 的头像

Zipeng Fu2 年前

Compared with other teleoperation methods, shadowing - is affordable - requires only 1 human operator - avoids singularities - natively supports whole-body control

Zipeng Fu 的头像

Zipeng Fu2 年前

Shadowing is an efficient data collection pipeline. We then perform supervised behavior cloning to train skill policies using egocentric vision, allowing humanoids to complete different tasks autonomously by imitating human skills.

Zipeng Fu 的头像

Zipeng Fu2 年前

This project is not possible without our team of experts, covering from computer graphics to robot learning to robot hardware: - co-leads: @qingqing_zhao_ @Qi_Wu577 - advisors: @chelseabfinn @GordonWetzstein project website: hardware: code:

Yuchen Jin 的头像

Yuchen Jin2 年前

how much can I buy it? I need ten of these buddies shadowing me

Michael Black 的头像

Michael Black2 年前

Nice!

Boris Belousov 的头像

Boris Belousov2 年前

Very impressive work!

Yi Li 的头像

Yi Li2 年前

very impressive!

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Introduce HumanPlus - Autonomous Skills part Humanoids are born for using human data. Imitating humans, our humanoid learns: - fold sweatshirts - unload objects from warehouse racks - diverse locomotion skills (squatting, jumping, standing) - greet another robot Open-sourced!

Introduce HumanPlus - Autonomous Skills part Humanoids are born for using human data. Imitating humans, our humanoid learns: - fold sweatshirts - unload objects from warehouse racks - diverse locomotion skills (squatting, jumping, standing) - greet another robot Open-sourced!

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TWIST: a real-time teleoperation system for humanoid robots to mimic whole-body motions. Reference motion data is generated by retargeting human motion-capture data to the robot. Then, the controller is trained in simulation using reinforcement learning and behavior cloning.

TWIST: a real-time teleoperation system for humanoid robots to mimic whole-body motions. Reference motion data is generated by retargeting human motion-capture data to the robot. Then, the controller is trained in simulation using reinforcement learning and behavior cloning.

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46,715 次观看 • 1 年前

🤖 Introducing H2O (Human2HumanOid): - 🧠 An RL-based human-to-humanoid real-time whole-body teleoperation framework - 💃 Scalable retargeting and training using large human motion dataset - 🎥 With just an RGB camera, everyone can teleoperate a full-sized humanoid to perform actions like pick and place, walking, kicking, boxing, etc - 💡Unleash the potential of humanoids with human cognitive skills and adaptability 🔗:

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21,932 次观看 • 9 个月前

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The Humanoid Hub

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213,954 次观看 • 1 个月前

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.

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.

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125,319 次观看 • 3 年前

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106,527 次观看 • 3 年前