We've hit 100 episodes! Here's a look back on our journey so far (website with some stats): robopapers100.com/ Some highlights in the thread 🧵:

Starting next week, the Earth Rover Grand Challenges will be at @ieeeiros IROS 2026 11 teams will test their navigation policies in 4 tracks ranging from city to indoor / off-terrain to marathon (50 miles of sidewalks, overpasses and bike paths) Let the competitions begin 🦾

Sharpa will be co-hosting two competitions at IROS 2026. Powered by Sharpa's robot platforms and software, the 2nd ROCO and Origami challenges (cohosted by @BitRobotNetwork) brought together 48 teams from leading institutions around the world—including Carnegie Mellon University, UC Berkeley, KAIST and NUS. Across both competitions, teams will put robotic manipulation and dexterity to the test on Sharpa hardware through challenging real-world tasks. Follow the challenges for more: ROCO: @NtuLab38456 Origami: robotic-origami-challenge.gi…

Next week, we’re heading to @ieee_ras_icra IROS 2026 in Pittsburgh with 50+ research teams pushing the frontier in 3 Grand Challenges: > Autonomous navigation w/ Earth Rover > Dexterity thru origami folding > Loco-manipulation thru Ikea assembly Excited to organize this w/ partners like @SharpaRobotics @LightwheelAI @UnitreeRobotics and more.

After 2+ years in the robotics data space, we are shutting @Eidon_AI down. The thesis was right. But the business is brutally hard. We close this chapter by open-sourcing everything we built and sharing lessons for anyone venturing into the space.

Name another project doing this. $AGPU is now one of the fastest-growing neoclouds on the market. Shares up ~590% in six months. $3B+ in signed real-world contracts this year, $6B more in the pipeline, and prepayments already flowing in the hundreds of millions. Dedicated NVIDIA Blackwell clusters. Enterprise SLAs. Actual cash from actual customers. Literally nobody else in crypto is doing billions in real-world GPU contracts with a token sitting at the center of the stack. This is uncharted territory. It’s still early. The big Build deployments haven’t even fully ramped yet. Next is Aethir Access - Aethir moving down the stack into physical AI data centers. Access already secured to 10 sites totaling up to 20 MW across the US and Europe, purpose-built for NVIDIA B300 and GB300 clusters. Projected up to $700M in contracts by end of 2026 and over $2B at full buildout. Months to live capacity, not years. A lot of people keep asking “what about ATH?” Don’t worry. ATH is core to the thesis. Aethir Foundation is $AGPU’s largest shareholder. $AGPU holds a Strategic Compute Reserve of ATH. I don’t know how to tell you any more clearly. This is your accumulation time. Don’t bet against us. You’ll wish you listened when the opportunity still looked like this. @AxeCompute @AethirCloud $AGPU ethereum:0xbe0ed4138121ecfc5c0e56b40517da27e6c5226b

Full ABC release 🔤 ! Train, test, and evaluate your models. The full suite of simulation tasks and data has also been released! Excited to see what people build 🔨🤖

Inspired by recent thoughts from senior researchers whom I deeply respect (e.g. @JitendraMalikCV @Michael_J_Black @Ken_Goldberg @phillip_isola), I wrote down some thoughts as a junior researcher too. The recent Astra demos in dexterous manipulation are truly impressive. But they also raise two questions that I don’t think academia has good answers to yet: - How should credit be assigned when an agent synthesizes many prior works into a new research result? - How can academia attract and retain talents when so much of its incentive structure relies on credit? I reflect on these questions in more detail in a blog post. Curious to hear what others think, and debates are very welcome! toruowo.github.io/blog/posts…

Imitation learning, especially with interventions, has driven so much recent robotics progress. However, improving a policy via targeted interventions until it reaches a useful and deployable success rate is a time and labor intensive process. Instead, wouldn’t it be great if policies could improve on their own? That’s what @VarunGiridhar3 and @animesh_garg join us to talk about. In Q-Planning, they start with a large policy like pi-0.5, and add a Q-function estimator to predict value instead of just actions, then use both successful and failed rollouts to update this Q-function online, then use it to guide sampling and trajectory selection. With just a few rollouts they can dramatically improve policy performance online. This provides a way to do really difficult tasks like inserting a credit card into a wallet, increasing success rate from 25% to 80% in just a few iterations. Learn more in Episode 105 of RoboPapers, hosted by @micoolcho, @chris_j_paxton, and @ruijie_sg.

muse is out there controlling this robot!! we’re rooting for you buddy!!

Grounded API is live. - SOTA on hand-tracking benchmarks (< 1 cm) - SOTA on SLAM benchmarks - In-the-wild ego data -> enriched data in minutes - Integration with @huggingface @LeRobotHF & @rerundotio - Built for @BitRobotNetwork RoboCap suite Technical report & more↓