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 👇

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

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.

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: rlinf.github.io/steam/

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.

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. 💫

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.

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%

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

👀 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

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: striding.ai