Chen Wang

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@chenwang_j

Co-founder and CTO @RewardAI_ | Prev PhD @Stanford

Stanford, CA chenwangjeremy.net Joined January 2021

871 Following 4.4K Followers 428 Posts

Latest posts 5

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Chen Wang @chenwang_j Sep 14

The speed is what you notice first. What excites me most is how we were able to transfer skills learned from human motion across different robots, without robot data or robot-specific fine-tuning. Really proud of what our team has built!

Reward AI @RewardAI_ Sep 14

Introducing OM-1, our first robot foundation model, zero-shot generalizing to any robot: table-top arms, industrial arms and humanoids.
- learned directly from human manipulation data
- no teleop/robot data
- close to human-level dexterity and efficiency
- multi-robot collab

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Chen Wang @chenwang_j Oct 27

Excited to show OM-1 taking on last-mile delivery! From picking up packages to carrying them down, it coordinates the whole body and adapts on the fly. We’re pushing into more tasks and environments behind the scenes. More to come!

Reward AI @RewardAI_ Oct 27

We deploy OM-1 on more humanoid tasks in both indoor and outdoor environments. OM-1 can power humanoids to do useful things robustly that other models or form factors cannot.
- last-mile delivery from a Cybertruck
- manipulation on stairs

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Chen Wang @chenwang_j Sep 11

Let the robot grab you a drink and stay tuned. We can’t wait to share what we’ve been building!

Reward AI @RewardAI_ Sep 11

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Chen Wang @chenwang_j Oct 14, 2024

Excited to introduce ARCap! We found that visual feedback is crucial for high-quality data collection, and AR can greatly help! We invited 20 novice users to each gather a small amount of data using only AR—no robot hardware required. The combined data can successfully train a robot policy!

Sirui Chen ✈️ CoRL2026 Verified account @eric_srchen Oct 14, 2024

How can we collect high-quality robot data without teleoperation? AR can help!

Introducing ARCap, a fully open-sourced AR solution for collecting cross-embodiment robot data (gripper and dex hand) directly using human hands.

🌐:stanford-tml.github.io/ARCap/
📜:arxiv.org/abs/2410.08464

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Chen Wang @chenwang_j Aug 29, 2024

We found that the relations between keypoints are a powerful way to represent tasks. What’s more exciting is that these keypoint relations can be formulated as constraint satisfaction problems, allowing us to use off-the-shelf optimization solvers to generate complex robot actions. Check out @wenlong_huang's thread for more on how we automate the keypoint constraints generation process and how this enables robots to perform reactive, bimanual, and long-horizon tasks!

Wenlong Huang @ CoRL 2026 Verified account @wenlong_huang Aug 29, 2024

What structural task representation enables multi-stage, in-the-wild, bimanual, reactive manipulation?

Introducing ReKep: LVM to label keypoints & VLM to write keypoint-based constraints, solve w/ optimization for diverse tasks, w/o task-specific training or env models.

🧵👇

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