Jesse Zhang @ ICRA 2026 ✈️'s banner

Jesse Zhang @ ICRA 2026 ✈️

@Jesse_Y_Zhang • 1,649 subscribers

Robotics Postdoc @uwcse w/ A. Gupta, D. Fox. Collab @allen_ai. Prev: PhD from @csatusc. Intern @ NVIDIA, AWS.

Shorts

How can non-experts quickly teach robots a variety of tasks? Introducing HAND ✋, a simple, time-efficient method of training robots! Using just a **single hand demo**, HAND learns manipulation tasks in under **4 minutes**! 🧵

How can non-experts quickly teach robots a variety of tasks? Introducing HAND ✋, a simple, time-efficient method of training robots! Using just a single hand demo, HAND learns manipulation tasks in under 4 minutes! 🧵

30,294 просмотров

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

A reward model that works, zero-shot, across robots, tasks, and scenes? Introducing Robometer: Scaling general-purpose robotic reward models with 1M+ trajectories. Enables zero-shot: online/offline/model-based RL, data retrieval + IL, automatic failure detection, and more! 🧵 (1/12)

A reward model that works, zero-shot, across robots, tasks, and scenes? Introducing Robometer: Scaling general-purpose robotic reward models with 1M+ trajectories. Enables zero-shot: online/offline/model-based RL, data retrieval + IL, automatic failure detection, and more! 🧵 (1/12)

Jesse Zhang @ ICRA 2026 ✈️

99,385 просмотров • 3 месяцев назад

How can we help *any* image-input policy generalize better? 👉 Meet PEEK 🤖 — a framework that uses VLMs to decide *where* to look and *what* to do, so downstream policies — from ACT, 3D-DA, or even π₀ — generalize more effectively! 🧵

How can we help any image-input policy generalize better? 👉 Meet PEEK 🤖 — a framework that uses VLMs to decide where to look and what to do, so downstream policies — from ACT, 3D-DA, or even π₀ — generalize more effectively! 🧵

13,295 просмотров • 8 месяцев назад

Having humans annotate data to pre-train robots is expensive and time-consuming! Introducing SPRINT: A pre-training approach using LLMs and offline RL to equip robots w/ many language-annotated skills while minimizing human annotation effort! URL: 🧵👇

Having humans annotate data to pre-train robots is expensive and time-consuming! Introducing SPRINT: A pre-training approach using LLMs and offline RL to equip robots w/ many language-annotated skills while minimizing human annotation effort! URL: 🧵👇

24,662 просмотров • 3 лет назад

Больше нет контента для загрузки