github.com/OpenWAM-Official/… OpenWAM is the most fascinating open‑source project I have ever taken part in, and it has received the best feedback. It is not merely a single research project; rather, it represents collective creation by the community. We zeroed in on what people care about and built an open‑source platform for everyone to explore freely and get started. There used to be very few unified open‑source repositories related to WAM, so we believed it was necessary to take this step. This may not be all about intelligence, but it is about curiosity and passion. It is also about enabling sustainable growth for this community. When I see first‑ and second‑year undergraduates, as well as junior PhD students aiming to work on embodied intelligence, joining in to help build this project, I realize some endeavours do not need to be driven by utilitarian concerns. Thank you to the OpenWAM Team, and to my friends: @KnightNemo_ @YuranWang_rise. (btw, siqiao is looking for 27fall phd position, if you are interested with his great works, don't forget to discuss with this talent guy!) Congratulations on hitting our milestone of 1k stars! This project would never have succeeded without all of you!

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-explorati… GitHub: N Project fully led by @RunhanH and @xczhangxx who are absolutely awesome. How it works, experiments, and robot videos below 🧵 (1/N)

(1/5) Everyone says dexterous manipulation needs real-world data. These policies were trained with none. Our robots mesh gears, thread nuts, and insert pegs zero-shot from RGB, and the same recipe works for locomotion across terrains much harder than before. The missing piece wasn't a better RL algorithm, but a small tweak to what the robot practices. Today we're releasing Success-Guided Sampling (SGS), a simple change to the task sampler that unlocks new levels of dexterity and agility with large-scale, end-to-end sim2real RL. No demonstrations, no per-task reward tuning. Just PPO at scale. Check out the policies yourself at sgs-rl.github.io! All videos at 1x speed! 🧵 🔈

I generally find that nobody really cares about papers, so I’m trying to add something a bit more human to my personal website. If you think my research is cooked, at least enjoy some things I actually cooked :) joyemang33.github.io/cooking…