Runhan Huang

Yilun Du Verified account @du_yilun · Oct 16

Robotics has spent years scaling demonstration data and model size. What happens when we scale EXPLORATION for robot agents?

We're incredibly excited to share RAX, an agentic robotics system that learns by scaling online exploration.

A coding agent connects directly to the robot’s perception and control APIs, learns from feedback, and builds programs that perceive, plan, and act. The model stays frozen. As exploration scales, the agent improves its programs and builds up reusable skills.

The results:
• ZERO teleoperation or action demonstrations on real robots—from insertion with 0.1 mm clearance and air-fryer manipulation to deformable objects and long-horizon mobile whole-body control.
• +37 percentage points on average over leading learned policies across five simulation benchmarks: RoboDojo, BEHAVIOR-1K, HumanoidArena, DexJoCo’s bimanual tasks, and LIBERO-Pro.
• One framework spanning single-arm, bimanual, dexterous, humanoid, and mobile robots, with new embodiments connected through APIs.

The craziest part: on each real-world embodiment, going from scratch to mastering the tasks took roughly two hours. No teleop. No model finetuning.

We’re open-sourcing the full framework AND every experiment session. We can’t wait to see what people build with it!

Project + sessions: robot-agent-exploration.com
Paper: assets.robot-agent-exploration.com/paper/rax.pdf
GitHub: N

Project fully led by @RunhanH and @xczhangxx who are absolutely awesome.

How it works, experiments, and robot videos below 🧵

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Jerry Huang @jerryhuang01 · 2d

0% success on L3 tasks in a recent Astra benchmark, including bottle-cap unscrewing.

Now RAX tackles jar opening with real dexterous hands.
6 chained skills. 4 twists. 140° rotation.

The jar opens.

@RunhanH @xczhangxx @du_yilun
#RAX #Robotics

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arxiv.org Scaling Online Exploration for Robot Agents Robot learning has advanced by scaling demonstration datasets and model capacity; however, deploying a new robot embodiment or task typically necessitates a dedicated teleoperation corpus. The...

Robotics Center of Silicon Valley @RoboticsCtr · 2d

0% success on L3 tasks in a recent Astra benchmark, including bottle-cap unscrewing.

Now RAX tackles jar opening with real dexterous hands.
6 chained skills. 4 twists. 140° rotation.

The jar opens.

@RunhanH @xczhangxx @du_yilun
#RAX #Robotics

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Yuxin Zuo Verified account @zuo_yuxin · 5d

‼️ Can Qwen directly control the robot as well as GPT-6 Astra?

🚀 Introducing uni-robot-agent: a unified harness for Robot-Use Agents (RUAs), a first step toward RUA training and local deployment.

We tested uni-robot-agent with Qwen3.8-Flash-Next running locally:
1️⃣ On RoboDojo, its score rose from 1.90 to 31.39, surpassing the RoboDojo Team’s score with GPT-6 Astra.
2️⃣ On real-world tasks, it outscored fine-tuned π0.5 by 5.75 points and zero-shot Dream-1 by 10 points.
🧵 [1/7]

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arxiv.org Scaling Online Exploration for Robot Agents Robot learning has advanced by scaling demonstration datasets and model capacity; however, deploying a new robot embodiment or task typically necessitates a dedicated teleoperation corpus. The...

Robotics Center of Silicon Valley @RoboticsCtr · Oct 27

An agent explores on a real robot for 2 hours, then runs the skills it learned as code.

That’s RAX.

No demonstrations. No teleoperation. Just exploration turned into reusable robot skills.

@RunhanH @xczhangxx @du_yilun @pabbeel @wangyenjen

#Robotics #EmbodiedAI

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