300 hours. 28,993 demonstrations. 59 tasks. Touch on all 10 fingertips, in every frame. We're thrilled to share SharpaDex v1.0 with the robotics community! To our knowledge, SharpaDex is the largest publicly available real-robot dexterous-hand manipulation dataset with fingertip tactile sensing, measured by publicly released demonstration hours containing tactile data as of October 2026. This release brings together: • Diverse manipulation: tool use, assembly, deformable-object handling, and long-horizon tasks. • Synchronized sensing: multi-view RGB, fingertip tactile images, fingertip force/torque, and 65-dimensional joint actions. • Contact-rich data: approximately 195 hours with at least one fingertip in contact, including 55 hours of simultaneous contact across both hands. • Hierarchical language annotations: task descriptions, 217,642 temporally grounded subtask segments covering 89.3% of frames, and 45 distinct skill labels. What excites us most is what comes next: researchers using these data to explore how robots can learn from touch, coordinate both hands, and perform increasingly complex tasks. We hope SharpaDex becomes a useful resource for visual-tactile VLA models, tactile representation learning, and general-purpose dexterous manipulation. We can't wait to see what you build—and learn alongside you. Available in LeRobot v3.0 and v2.1 formats under CC BY-NC 4.0. Explore the data, tasks, and annotations through our report and interactive visualizations, or download the dataset to get started: Dataset on Hugging Face：huggingface.co/datasets/Shar…

Can an LLM agent play Angry Birds? Introducing Embodied Playground 🤖 410 simulated task environments that test physical, strategic, and spatial reasoning. 🌏 Website: winniechen2002.github.io/Age… 📄 Paper: arxiv.org/abs/2610.27039 💻 Code: github.com/Winniechen2002/Em…

Reinforcement Learning (RL) in simulation is a scalable strategy to learn dexterous skills, but naive exploration from scratch often fails to discover manipulation strategies for diverse objects. At @AppliedInt, we propose X-Reset, a straightforward method to resolve the exploration problem for generalist RL policies by randomly sampling resets from human hand-object states. - We train a single dexterous policy to manipulate a variety of objects to any goal pose, ranging from small markers to heavy powerdrills with zero teleoperation data - The learned policy zero-shot generalizes well beyond the training set (DexYCB), and transfers from sim-to-real using object pose-tracking models We are continuously expanding robotics research at @AppliedInt beyond X-Reset, with the bounds of simulation and synthetic data for dexterous manipulation pushed. Stay tuned and join us! 🌐 Project Page: xreset-applied.github.io 📄 Paper: arxiv.org/abs/2609.35715

It's interesting that I have spent quite some time working on sim-to-real for screwdriving, often using it as an example of why finger coordination is too hard to teleoperate and why we need simulation. But is that really the case? We explore whether carefully designed low-level control, especially with compliance, can actually make these tasks teleoperable. Checkout the original thread ⬇️ And the @SharpaRobotics glove has been amazing to work with! It played a key role in making this project possible!

The first ever Origami Challenge with @SharpaRobotics at @ieeeiros IROS 2026 concluded. We asked teams to fold a paper airplane to test policies in dexterous manipulation. 14 global teams made it to the final round. Here are the results ↓

🚩Human touch can help scale robot dexterity, not just human motion. 🤖Introduce UVTA: a universal system leverage human tactile data for dexterous manipulation. ✉️ Project available at: uni-vta.github.io/ Arxiv: arxiv.org/abs/2609.34182

One human demonstration. Any multi-fingered hand. Zero-shot sim-to-real visuomotor policy. morphometricimitation.github… Collaborators: @he_siming @ckwolfeofficial @HaozhiQ @LeaMue27 Shankar Sastry, Claire Tomlin, @JitendraMalikCV

Learning bimanual dexterous manipulation from a single human video? Agents can be a solution! Introducing DexAgent, an agentic Human2Sim2Robot framework for dexterous manipulation with a self-evolving tool library. We evaluated DexAgent on 11 real-world, long-horizon dexterous manipulation tasks spanning rigid, articulated, and deformable objects. 🤖 63.6% policy rollout success rate, compared with 18.2% for the strongest baseline ⚡ 2.1 hours average inference time with the tool library, compared with 3.3 hours for the baseline 🧰 A self-evolving library with 103 skills + 188 verifiers, designed to keep growing as DexAgent encounters new tasks and objects Learn more and contribute to the growing tool library: Project Website: dexagent1.github.io/ Arxiv: arxiv.org/abs/2609.35318

This is soooo coooool! Really grateful to partner @SharpaRobotics on this one!