Papers to watch: UniWAM The idea: stop separating "understand the world", "predict what happens next" and "act" Instead their UniWAM combines a VLM-based physical reasoner + video world model + action policy in one architecture. The model was trained on: - robot demos - human egocentric video - VQA data Results - achieved SOTA across several benchmarks: 99.2% LIBERO 92.6% LIBERO-Plus 68.3% RoboTwin Clean2Rand UniWAM The part I find most interesting: human + robot co-training appears to follow a log-linear scaling trend. Another datapoint that human video may matter a lot for scaling robot foundation models. project page: uniwam.github.io

🏆 IROS 2026 LRC championship highlights! Nanyang Technological University took 1st place in the fully autonomous category with GENISOM M1. Watch the highlights—and special thanks to @AgilexRobotics for its robotic arm support! 👏 #GENISOMAI #IROS2026

@typesafeai Jev vs. GPT-6 Astra 🤖 on a real robotic task @AgilexRobotics : Task: “Put the red cube in the box.” 🔴 → 📦 ⚡ Jev 27s vs. Astra 1m 11s 💰 Jev much cheaper The arm was limited to just 10% speed for safety — at full speed, Jev’s advantage would be even more significant.