Striding AI

Bring Ease to the World with Physical AI.

[Striding AI @StridingAI]

Striding AI launches Physical AI solution for 24/7 retail.

H1 Humanoid · C1 Carrying Robot · M1 Management Platform

🧠 World models + RL + embodied agents — enabling robots to perceive, plan, & execute on the floor, supporting 24/7 operations. 🏪 📹

https://t.co/jzr33G0qRn

[Striding AI @StridingAI]

Introducing C1, Striding AI's physical AI solution designed for real-world retail environments.

Built for narrow aisles and dynamic store environments, C1 features:
• Omnidirectional movement with a compact footprint
• No modification required to existing shelves or store layouts
• 100mm–1750mm+ operating height
• Transport, restocking and handling capabilities
• Depth vision and 360° visual perception

From navigating narrow spaces to handling everyday retail tasks, C1 is designed to bring Physical AI into real commercial environments.

#C1 #RetailRobot #PhysicalAI #Robotics #EmbodiedAI #StridingAI

[Striding AI @StridingAI]

Meet H1, Striding AI’s first fully self-developed humanoid robot.

Built for real-world environments, H1 combines physical intelligence with natural human-robot interaction:

• 20+ languages
• 45 facial expressions
• 36 gestures
• Open API for further development

Designed to work alongside people across retail, service, and other real-world settings.

A step toward bringing Physical AI into the real world.

#H1 #HumanoidRobot #PhysicalAI #Robotics

[Striding AI @StridingAI]

#AI has learned to generate answers in the digital world.
The next step is to take action in the physical world.

At #APRCE2026, Song Yao, Founder and CEO of Striding AI, shared how AI innovations can reshape 24/7 #retail & bring ease to the world with #physicalAI.

[Striding AI @StridingAI]

What will 24/7 retail look like in the age of #PhysicalAI?

Our Founder & CEO Song Yao will take the stage at #APRCE2026 with a talk on Physical AI and the Future of 24/7 Retail.
📅 October 8 | 11:00
📍 Tokyo International Forum

Stay tuned.
#StridingAI #Robotics #Retail

[Striding AI @StridingAI]

Congrats to Prof. @ChaoYuTHU on being named a 2025 TR 35 Asia Pacific.🏆🎉
Her MAPPO work and large-scale RL infrastructure for embodied intelligence are already shaping how we train robots that act in the real world.🤖🧠

#MIT #TR35 #InnovatorsUnder35 #Rlinf

[Chao Yu @ChaoYuTHU]

Honored to be named a 2025 TR35 Asia Pacific 🏆
Thank you to everyone who has supported this journey.
🚀Onward with better AI models & RL systems for embodied intelligence.

#MIT #TR35

[Striding AI @StridingAI]

Clearing a cluttered table is more than a pick-and-place task.
The task demonstrates three core capabilities:
1️⃣Semantic understanding of everyday objects
2️⃣Long-horizon planning and execution
3️⃣Coordinated dual-arm manipulation and handovers

[Striding AI @StridingAI]

How can robots clear a cluttered tabletop? 🤖
Our Physical AI system enables the robot to recognize each object, decides where it belongs, and completes a long-horizon sequence of actions—including coordinated dual-arm handovers.
Thread 👇

[Striding AI @StridingAI]

Clearing a cluttered table is more than a pick-and-place task.
The task demonstrates three core capabilities:
1️⃣Semantic understanding of everyday objects
2️⃣Long-horizon planning and execution
3️⃣Coordinated dual-arm manipulation and handovers

[Striding AI @StridingAI]

Higher task success and faster execution. 🚀
Our Physical AI system completes a multi-stage unboxing task with tool use & high-precision bimanual manipulation.
Powered by #STEAM , it learns frame-level task progress from mixed-quality real-world experience.

[Striding AI @StridingAI]

Real-world gains 🚀

On four real-robot tasks, paired with CFGRL, STEAM lifts success rate by +59%, +54.3%, +23%, and +16.2% — towel folding, chip checkout, cola restocking, and pick-and-place.
🌐 Project: https://t.co/s3AbFi8KOE

[Striding AI @StridingAI]

From learning trajectories → learning progress.

@ChaoYuTHU proposed STEAM, a label-free advantage model that reads progress, stalls, failures, and recoveries frame‑by‑frame — learned from nothing but the temporal order of expert demos.

[Striding AI @StridingAI]

Results at a glance👇
Major gains over frozen-policy baselines across perturbed manipulation benchmarks.

Beyond VLA, the Harness Layer provides a general framework for other Embodied AI models. 💫

[Striding AI @StridingAI]

We see a possible paradigm shift:
End-to-End VLA → Harness VLA
@ChaoYuTHU open-sourced Harness VLA — adding a Harness Layer to organize VLA models without changing their weights.

LIBERO-Pro:
Harness VLA 82.4% 🚀
vs.
Pi_RLinf 50%
NVIDIA Cap-X 18.2%
Berkeley RATS 43.8%

[Striding AI @StridingAI]

Harness VLA improves reliability because exploration teaches the planner how to use a fixed primitive set: where to ground entities, when to invoke VLA_ACT, how to re-stage failed contacts, and which failures should not be repeated.

[Striding AI @StridingAI]

How can robots help people at the checkout? 🛒
Here's another Striding AI's demo as we work toward a future where humans and robots collaborate seamlessly.

#PhysicalAI #Robotics #AI #EmbodiedAI

[Striding AI @StridingAI]

👀 A sneak peek at Striding AI’s Physical AI system performing autonomous shelf restocking in a simulated retail environment.

#PhysicalAI #Robotics #AI #StridingAI #EmbodiedAI #MachineLearning

[Striding AI @StridingAI]

Introducing Striding AI. 💫

Our mission is simple:
Bring Ease to the World with #PhysicalAI.

What will the future look like?

We envision robots becoming trusted teammates—helping people work safer, smarter, and more efficiently. 💙

Explore more: https://t.co/WFW1HtQp0w