Lerrel Pinto's banner

Lerrel Pinto

@LerrelPinto • 9,232 subscribers

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

Videos

Anya Rossi

sweetdream.ai

SweetDream.ai•Sponsored•Livecam

Watch Anya Live

Anya is streaming live right now! Join her private show and enjoy exclusive content.

Exclusive private shows

1.2k viewers online

Private Show

Join now for exclusive access

Free preview available • Premium content

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): 🧵

211,697 次观看 • 2 年前

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: ⬇️

152,206 次观看 • 2 年前

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. 🧵👇

70,117 次观看 • 1 年前

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)

138,536 次观看 • 2 年前

When life gives you lemons, you pick them up.

When life gives you lemons, you pick them up.

65,557 次观看 • 1 年前

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. 🧵👇

42,538 次观看 • 1 年前

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 次观看 • 1 年前

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. 🧵👇

84,258 次观看 • 3 年前

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.

46,197 次观看 • 1 年前

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

53,268 次观看 • 2 年前

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.

17,040 次观看 • 1 年前

没有更多内容可加载