Super psyched for this competition! While folding origami is not "economically valuable task" which many commercial labs care about outright, I love this challenge b/c: 1. Origami, at the highest level, is IMO one of the most complicated dexterous task human hands are capable of. 2. I reckon even for fairly simple origami designs, you'd need more than 2 fingers. So this is a task that really showcase the use of 5-fingered hands. 3. Folding origami involves lots of occlusion. In fact, I've seen an origami master fold a crane more or less completely based on touch. This means tactile and even force sensing would be required for complicated origami designs. 4. Origami is an art form with long cultural history in Japan. It's not just the physical manipulation techniques involved, there's also a creative aspect that I think will be a frontier goal for Embodied AI to shoot for. 5. We're co-organizing this challenge with the Nippon Origami Association, who are basically the human experts. This means we'll have human expert benchmark against what embodied AI can do. I reckon this is such a difficult task that this competition will last for years before embodied AI (both model and hardware improvements) can catch up to human performance. I'm also very confident that on the path of solving robotic origami we'd have also unlocked some fundamental manipulation capabilities that'd be useful in lots of other real-world dexterous tasks. Grateful to be able to do this with my buddies @DJiafei @chris_j_paxton as well as strong partners from @SharpaRobotics @LightwheelAI @BitRobotNetwork as well as friends from academia.

Very cool work from the DexSkin team! I saw the live demo myself at Stanford; really neat, affordable and open source!!

Full episode dropping soon! Geeking out with @s_wistreich @BaiyuShi147 on DexSkin: High-Coverage Conformable Robotic Skin for Learning Contact-Rich Manipulation dex-skin.github.io/ Co-hosted by @chris_j_paxton @DJiafei

We added new skills to the Rerun repo to make it easier to investigate existing robotics data with Rerun. Very excited to see the new Human in the Wild dataset by @bitrobotnetwork, @huggingface, and @unitreerobotics this week as a great opportunity to try them out, We'll be playing more with this dataset in the coming weeks, but check out the repo below if you want to start looking at and querying this new dataset in the interim.

This is incredible! This isn't a 24-hr stream doing essentially the same tasks over and over. This is a 30-min continuous sequence of various subtasks; any of them failing along the way we won't be getting no Mapo Tofu! Great stuff from @fancy_yzc & the PokeBot team.

Phenomenal work from the AI2 folks. Great service to the community! Tks for all the sharing @DJiafei @hq_fang

Full episode dropping soon! Geeking out with @hq_fang @DJiafei on MolmoAct 2: An open foundation for robots that work in the real world allenai.org/blog/molmoact2 Co-hosted by @micoolcho @chris_j_paxton

Full episode dropping soon! Geeking out with @hq_fang @DJiafei on MolmoAct 2: An open foundation for robots that work in the real world allenai.org/blog/molmoact2 Co-hosted by @micoolcho @chris_j_paxton

Manipulation is cool, but Navigation is still my first love. We need more researchers pushing urban autonomous navigation. Happy to help co-organize our 3rd Earth Rover Challenge at IROS!

Robot policies must be both reliable and highly capable to be useful; the best way to achieve this level of performance is with reinforcement learning. However, for reinforcement learning you are usually stuck between two difficult options: reinforcement in the real world is often risky and expensive, while reinforcement learning in a traditional simulator takes a lot of engineering work and has a persistent sim-to-real gap. What if instead you could train your robot purely in a world model? RISE by @jiazhi_yang2024 et al. uses a compositional world model to predict the future and evaluate progress. This allows for a self-improving pipeline, which learns a world model from real data and then learns how the robot should perform different tasks. This pipeline results in a data-driven way to improve policy performance from real data but without real-world reinforcement learning. Watch Episode #86 of RoboPapers, with @chris_j_paxton and @DJiafei, to learn more!

Full episode dropping soon! Geeking out with @jiazhi_yang2024 on RISE: Self-Improving Robot Policy with Compositional World Model opendrivelab.com/rise/ Co-hosted by @chris_j_paxton @DJiafei

Can humanoids assemble IKEA furniture? Calling for competing teams at IROS 2026! Co-organized by @UnitreeRobotics @BitRobotNetwork @LightwheelAI etc

Can humanoids assemble IKEA furniture? Can't wait to work with @UnitreeRobotics @LightwheelAI, etc on this Humanoid IKEA Assembly Challenge at IROS! More to come from @BitRobotNetwork

Full episode dropping soon! Geeking out with @jiazhi_yang2024 on RISE: Self-Improving Robot Policy with Compositional World Model opendrivelab.com/rise/ Co-hosted by @chris_j_paxton @DJiafei

Some thoughts on @helium and solana:hntyVP6YFm1Hg25TN9WGLqM12b8TQmcknKrdu1oxWux 1) Network wide revenue is up roughly 4x y/y from $13k / day to $50k / day. The asset is currently trading at an 8x PE 2) Based on the current buy and burn, more than 10% of the float is being systematically purchased from the open market per year. About 90% of the FDV is already floating, and inflation is low single digits per year. 3) The network offload business - selling capacity to Tmobile, Verizon, Att, and international carriers such as Telefonica. Obviously, this business will continue to grow nicely both with general internet growth, and by signing more carriers 4) Selling the consumer facing business - Helium Mobile - to Andrew Yang's new mobile business, Noble Moble, that will further accelerate growth of Helium/Noble. The biggest challenge that Helium Mobile faced was CAC. Andrew Yang is a repeat successful entreprenuer, has raised a ton of money for Noble, and has a fresh mandate to invest in growth with a balance sheet. Also, he's super famous and loved and has a big platform I bought some more HNT yesterday.

This whole week I was at @ieee_ras_icra I was mostly focused on the LeHome Challenge, but I also really enjoyed walking around the exhibition. Here are a few cool examples from the conference. Here I am teleoperating a tendon-driven semi-humanoid from @Astribot_Inc . 1/5

More folks sld follow the works from @Ed__Johns lab at Imperial. The data efficiency they r going for is incredible. Their Instant Policy and Learning 1000 Tasks in a Day papers are definitely worth reading. Photo: taken at Beyond Teleop workshop #icra2026