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Lerrel Pinto

@LerrelPinto • 9,550 subscribers

Making robots more dexterous, robust and general. Co-founder of Assured Robot Intelligence (ARI)

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Turns out you can train humanoid hands without any robot data. The idea in HUG is quite simple: (a) collect human data with smart glasses, (b) train a human manipulation model, (c) retarget to multi-fingered robot hands.

Turns out you can train humanoid hands without any robot data. The idea in HUG is quite simple: (a) collect human data with smart glasses, (b) train a human manipulation model, (c) retarget to multi-fingered robot hands.

32,446 просмотров

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Meet Dobb·E: a home robot system that needs just 5 minutes of human teaching to learn new tasks. Dobb·E has visited 10 homes, learned 100+ tasks, and we are just getting started! Dobb·E is fully open-sourced (including hardware, models, and software): 🧵

Meet Dobb·E: a home robot system that needs just 5 minutes of human teaching to learn new tasks. Dobb·E has visited 10 homes, learned 100+ tasks, and we are just getting started! Dobb·E is fully open-sourced (including hardware, models, and software): 🧵

211,736 просмотров • 2 лет назад

Excited to release OK-Robot, an open-vocabulary mobile-manipulator for homes. Simply tell the robot what to pick and where to drop it in natural language, and it will do it. Like: Me: "OK Robot, move the Takis from the desk to the nightstand" Robot: ⬇️

Excited to release OK-Robot, an open-vocabulary mobile-manipulator for homes. Simply tell the robot what to pick and where to drop it in natural language, and it will do it. Like: Me: "OK Robot, move the Takis from the desk to the nightstand" Robot: ⬇️

152,292 просмотров • 2 лет назад

Teaching robots to learn only from RGB human videos is hard! In Feel The Force (FTF), we teach robots to mimic the tactile feedback humans experience when handling objects. This allows for delicate, touch-sensitive tasks—like picking up a raw egg without breaking it. 🧵👇

Teaching robots to learn only from RGB human videos is hard! In Feel The Force (FTF), we teach robots to mimic the tactile feedback humans experience when handling objects. This allows for delicate, touch-sensitive tasks—like picking up a raw egg without breaking it. 🧵👇

70,328 просмотров • 1 год назад

We just released TAVI -- a robotics framework that combines touch and vision to solve challenging dexterous tasks in under 1 hour. The key? Use human demonstrations to initialize a policy, followed by tactile-based online learning with vision-based rewards. Details in🧵(1/7)

We just released TAVI -- a robotics framework that combines touch and vision to solve challenging dexterous tasks in under 1 hour. The key? Use human demonstrations to initialize a policy, followed by tactile-based online learning with vision-based rewards. Details in🧵(1/7)

138,931 просмотров • 2 лет назад

When life gives you lemons, you pick them up.

When life gives you lemons, you pick them up.

65,557 просмотров • 1 год назад

Imagine robots learning new skills—without any robot data. Today, we're excited to release EgoZero: our first steps in training robot policies that operate in unseen environments, solely from data collected through humans wearing Aria smart glasses. 🧵👇

Imagine robots learning new skills—without any robot data. Today, we're excited to release EgoZero: our first steps in training robot policies that operate in unseen environments, solely from data collected through humans wearing Aria smart glasses. 🧵👇

42,576 просмотров • 1 год назад

Super excited for the release of Robot Utility Models (RUMs)! RUMs is a simple method to build zero-shot robot policies that can solve useful tasks in completely new homes without any additional training often at 90%+ success rate. 🧵👇

Super excited for the release of Robot Utility Models (RUMs)! RUMs is a simple method to build zero-shot robot policies that can solve useful tasks in completely new homes without any additional training often at 90%+ success rate. 🧵👇

56,591 просмотров • 1 год назад

Tactile feedback is one of the most important modalities in manipulation, but has been underutilized in dexterous hands. T-Dex is a framework for learning dexterous policies from tactile play data, beating vision and torque-based methods by 1.7x. 🧵👇

Tactile feedback is one of the most important modalities in manipulation, but has been underutilized in dexterous hands. T-Dex is a framework for learning dexterous policies from tactile play data, beating vision and torque-based methods by 1.7x. 🧵👇

84,611 просмотров • 3 лет назад

Wouldn't it be great if we could train robots without any teleoperation! In our latest paper, we train robots to mimic a human video of the task by simply matching the object features using RL. We only need one video and under an hour of robot training.

Wouldn't it be great if we could train robots without any teleoperation! In our latest paper, we train robots to mimic a human video of the task by simply matching the object features using RL. We only need one video and under an hour of robot training.

46,221 просмотров • 1 год назад

Building robot intelligence requires high-quality robot data. But far too many tools to collect data are closed and custom-built. Open Teach is fully open-sourced and is: - Calibration-free - Supports multiple arms, hands, & mobile manipulators - Costs just $500

Building robot intelligence requires high-quality robot data. But far too many tools to collect data are closed and custom-built. Open Teach is fully open-sourced and is: - Calibration-free - Supports multiple arms, hands, & mobile manipulators - Costs just $500

53,286 просмотров • 2 лет назад

It is really hard to get robot policies that are both precise (small margins for error) and general (robust to env variations). We just released ViSk, where skin sensing is used to train fine-grained policies with ~1 hour of data. Below is a single-take video.

It is really hard to get robot policies that are both precise (small margins for error) and general (robust to env variations). We just released ViSk, where skin sensing is used to train fine-grained policies with ~1 hour of data. Below is a single-take video.

17,169 просмотров • 1 год назад

Deep SSMs are great in modeling audio / language, but they really excel in modeling continuous, raw sensor data. Introducing Hierarchical SSMs (HiSS), a new framework that improves upon Transformers, RNNs, S4 & Mamba for a range of sensors (touch, accelerometers & IMUs). 🧵👇

Deep SSMs are great in modeling audio / language, but they really excel in modeling continuous, raw sensor data. Introducing Hierarchical SSMs (HiSS), a new framework that improves upon Transformers, RNNs, S4 & Mamba for a range of sensors (touch, accelerometers & IMUs). 🧵👇

19,166 просмотров • 2 лет назад

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