Загрузка видео...

Не удалось загрузить видео

На главную

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!

230,388 просмотров • 2 лет назад •via X (Twitter)

Комментарии: 10

Фото профиля Zipeng Fu
Zipeng Fu2 лет назад

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.

Фото профиля Zipeng Fu
Zipeng Fu2 лет назад

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:

Фото профиля Zipeng Fu
Zipeng Fu2 лет назад

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:

Фото профиля Zipeng Fu
Zipeng Fu2 лет назад

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

Фото профиля Zipeng Fu
Zipeng Fu2 лет назад

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.

Фото профиля Zipeng Fu
Zipeng Fu2 лет назад

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:

Фото профиля Yuchen Jin
Yuchen Jin2 лет назад

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

Фото профиля Michael Black
Michael Black2 лет назад

Nice!

Фото профиля Boris Belousov
Boris Belousov2 лет назад

Very impressive work!

Фото профиля Yi Li
Yi Li2 лет назад

very impressive!

Похожие видео

Physics-based Motion Retargeting from Sparse Inputs paper page: Avatars are important to create interactive and immersive experiences in virtual worlds. One challenge in animating these characters to mimic a user's motion is that commercial AR/VR products consist only of a headset and controllers, providing very limited sensor data of the user's pose. Another challenge is that an avatar might have a different skeleton structure than a human and the mapping between them is unclear. In this work we address both of these challenges. We introduce a method to retarget motions in real-time from sparse human sensor data to characters of various morphologies. Our method uses reinforcement learning to train a policy to control characters in a physics simulator. We only require human motion capture data for training, without relying on artist-generated animations for each avatar. This allows us to use large motion capture datasets to train general policies that can track unseen users from real and sparse data in real-time. We demonstrate the feasibility of our approach on three characters with different skeleton structure: a dinosaur, a mouse-like creature and a human. We show that the avatar poses often match the user surprisingly well, despite having no sensor information of the lower body available. We discuss and ablate the important components in our framework, specifically the kinematic retargeting step, the imitation, contact and action reward as well as our asymmetric actor-critic observations. We further explore the robustness of our method in a variety of settings including unbalancing, dancing and sports motions.

AK

106,527 просмотров • 3 лет назад