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Testing the new grasping motion with Microduck: trained in simulation using reinforcement learning, one-shot sim-to-real transfer.

13,064 görüntüleme • 4 gün önce •via X (Twitter)

28 Yorum

Nick K profil fotoğrafı
Nick K4 gün önce

Cool, is this open-loop? (e.g. no vision input)?

Remi Fabre profil fotoğrafı
Remi Fabre4 gün önce

Remoted controlled here, but I'm working on an autonomous version for simple cases

Nick K profil fotoğrafı
Nick K4 gün önce

How are you doing the remote control though? Does this work on other surfaces (e.g. walls)?

Raphael Gangl profil fotoğrafı
Raphael Gangl4 gün önce

how much compute do i need to train it? is my macbook air enoug?

Remi Fabre profil fotoğrafı
Remi Fabre4 gün önce

Might be slow but haven't tested as I use HF jobs all the time

Samantha profil fotoğrafı
Samantha4 gün önce

You do such an awesome job !!!

Phillip Chananda profil fotoğrafı
Phillip Chananda4 gün önce

This is great. 👏👏🔥🔥

M1LL5 profil fotoğrafı
M1LL54 gün önce

Incredible

Helene A profil fotoğrafı
Helene A4 gün önce

Soon he will be playing chess against Reachy mini telling him where to be out his pieces🤣

Remi Fabre profil fotoğrafı
Remi Fabre4 gün önce

Oô

Nikhil Kumar profil fotoğrafı
Nikhil Kumar4 gün önce

impressive!!

JeCo profil fotoğrafı
JeCo4 gün önce

That's some fine grasping with that large beak! Do your little beta-testers really believe you when you tell them you're actually "working" with microduck? 😄

Remi Fabre profil fotoğrafı
Remi Fabre4 gün önce

They do :)

martin profil fotoğrafı
martin4 gün önce

@witcheer

Adam profil fotoğrafı
Adam2 gün önce

so cool to grow up as a kid with microducks 🥺🔥

dani.3dprint profil fotoğrafı
dani.3dprint4 gün önce

😁

Richard W. profil fotoğrafı
Richard W.4 gün önce

The robotics will replace the human.

Lore profil fotoğrafı
Lore4 gün önce

What did you randomize in simulation for the grasp to transfer, especially contact friction and actuator delay?

Future Visuals profil fotoğrafı
Future Visuals4 gün önce

Precise motion trained in simulation

Tomorrowist profil fotoğrafı
Tomorrowist4 gün önce

It‘s as interesting as going to church for a one-year-old child to eat.

Lyle Heiss profil fotoğrafı
Lyle Heiss4 gün önce

Is it possible to train them without having a great GPU? Would an average laptop suffice?

Remi Fabre profil fotoğrafı
Remi Fabre4 gün önce

As far as I know, you need an NVIDIA GPU to train for real. without one it only runs on CPU, way too slow for a full run. I use HF jobs personnaly

Lyle Heiss profil fotoğrafı
Lyle Heiss4 gün önce

Thank you for the reply. Hugging face it is👍

Raphael Gangl profil fotoğrafı
Raphael Gangl4 gün önce

does it have similiar sound and speaking capability as reachy?

Remi Fabre profil fotoğrafı
Remi Fabre4 gün önce

it has a small speaker and internet access, so you could use the same software to make Microduck talk. I’m sure a lot of people will do this, but probably won’t because we don’t like the duck talking like a human

Negar Nasiri profil fotoğrafı
Negar Nasiri4 gün önce

poor Microduck he is being bullied 😂

someone profil fotoğrafı
someone4 gün önce

ofc the man never disappoints wym chess pieces 🥺

Iceland attom profil fotoğrafı
Iceland attom3 gün önce

NO DUBI; DO NOT PUT THAT ON YOUR PALLAT.

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Today, we're joined by Nikita Rudin, co-founder and CEO of Flexion to discuss the gap between current robotic capabilities and what’s required to deploy fully autonomous robots in the real world. Nikita explains how reinforcement learning and simulation have driven rapid progress in robot locomotion—and why locomotion is still far from “solved.” We dig into the sim2real gap, and how adding visual inputs introduces noise and significantly complicates sim-to-real transfer. We also explore the debate between end-to-end models and modular approaches, and why separating locomotion, planning, and semantics remains a pragmatic approach today. Nikita also introduces the concept of "real-to-sim", which uses real-world data to refine simulation parameters for higher fidelity training, discusses how reinforcement learning, imitation learning, and teleoperation data are combined to train robust policies for both quadruped and humanoid robots, and introduces Flexion's hierarchical approach that utilizes pre-trained Vision-Language Models (VLMs) for high-level task orchestration with Vision-Language-Action (VLA) models and low-level whole-body trackers. Finally, Nikita shares the behind-the-scenes in humanoid robot demos, his take on reinforcement learning in simulation versus the real world, the nuances of reward tuning, and offers practical advice for researchers and practitioners looking to get started in robotics today. 🗒️ For the full list of resources for this episode, visit the show notes page: 📖 CHAPTERS =============================== 00:00 - Introduction 04:07 - Is robot locomotion solved? 06:04 - Sim-to-real gap 08:58 - Adding semantics to policies 09:42 - Modular vs end-to-end architectures 10:29 - Planner model 12:21 - Adapting RL techniques from quadrupeds to humanoids 15:39 - Behind robot demos 18:09 - Humanoid robots in home environments 22:03 - Training approach 23:56 - VLA models 27:59 - Closing the sim-to-real gap 32:55 - Task orchestration using VLMs 36:38 - Tool use 38:10 - Model hierarchy 43:37 - Simulator versus simulation environment 44:57 - Combining imitation learning and reinforcement learning 46:42 - RL in real world versus RL in simulation 52:58 - Reward tuning and value functions in robotics 56:38 - Predictions 1:00:10 - Humanoids, quadropeds, and wheeled platforms 1:02:45 - Advice, recommended robot kits, and community pla

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