In my latest PhD paper, we declare WAR on sensor-maxxing. For the first time, push-resilient humanoid walking, just with joint encoders! No IMU, no F/T sensors. 🥁 Introducing Blind Dexterity 🧵 👇 w/ @OKaidanov @liu_puze @Jan_R_Peters at @DFKI @ias_tudarmstadt

To address long-horizon robotic manipulation tasks, we propose X-Planner, an embodied long-horizon task planner: It decomposes high-level natural language instructions into event-level subtasks, and can also output continuous implicit Chain-of-Thought (CoT). It directly interfaces with downstream VLA or WAM models to operate robots. GitHub: https://github.com/X-Square-Robot/Xplanner Checkpoint: https://huggingface.co/x-square-robot/X-Planner-9B-0916 Benchmark: https://huggingface.co/datasets/x-square-robot/xplanner-benchmark

🍾🍲 Saturday Robotics x IROS 2026 — Robotics Research Night 👉🏻 https://luma.com/tzbw7n61 We’re bringing a high-signal evening of robotics research to Pittsburgh on September 28. After a full day at IROS, we’ll bring together researchers, engineers, founders, students, and investors for technical discussions, networking, and a series of ~10-minute lightning talks. Tentative preview of the current lineup: 🤖 1. PAC-MAN: Perception-Aware CBF-RL for Whole-Body Safety in Humanoid Dodgeball Gary Yang @lzyang2000 (@Caltech) Perception-aware reinforcement learning + Control Barrier Functions for whole-body humanoid safety. Demonstrated on a Unitree G1, with 19/20 successful dodges and zero falls in real-world experiments. 🧠 2. How In-Context Learning Is Reshaping Robot Learning Data at Scale AaronLi (@RhodaAI) Exploring how in-context learning can change the way we think about robot learning data, scaling, and generalization. 🧪 3. X2Real: An eXtensive Simulation Benchmark for Real-World Generalist Policies Liangwang Ruan (@XSquareRobot) A new simulation benchmark built around faithfulness, diversity, and fairness, with 44 hierarchical long-horizon tasks across 10 capability dimensions and a reported 0.84 simulation-to-real correlation. 🦾 4. Rethinking Generalist Robotic Manipulation: Architecture, Data and Inference for Real-World Deployment Peiyan Li (Chinese Academy of Sciences, @CAS__Science) 3D VLA architectures, memory augmentation, ego/UMI human priors, large-scale robot pretraining, and inference-time contextual learning for deployable generalist manipulation. 🎯 5. HiRE: Hindsight Reward Editing for Policy Finetuning Haoyi Niu @t641769919 (@UCBerkeley) Accepted at CoRL 2026. A training-free approach to reward editing that uses successful and failed trajectories to identify “trap states” and provide denser, control-aware feedback for RL. 🔥 6. Lightning Talk — Open Slot We’re opening one additional slot for a technically deep research talk, new project, frontier paper, demo, open problem, or startup technical insight. 10 minutes. A few slides. One sharp technical idea. No fluff. Topics include World Models, Physical AI, Humanoids, VLAs, Robot Foundation Models, Manipulation, RL, Simulation & Sim-to-Real, Spatial Intelligence, Computer Vision, and Embodied AI. 📍 Pittsburgh 📅 September 28, 2026 🕠 5:30–9:30 PM 🍾 Networking + Technical Talks + Research Discussion 📩 junfanzhu98@gmail.com See you in Pittsburgh. 🤖 #IROS2026 #Robotics #PhysicalAI #RobotLearning #WorldModels #HumanoidRobotics #VLA #EmbodiedAI #RobotFoundationModels

Today, we give robots a /skills library that self-evolves and compounds indefinitely! Introducing ASPIRE: a robot solving its 100th task is no longer as clueless as solving its first. Coding agents observe multimodal sensory traces from simulation and real robots, launch an evolutionary search over control programs, and distill the best know-how into an ever-expanding library. ASPIRE is a new type of continual learning: "training" is skill refinement instead of gradient descent. "Trained model" is a repo of sensorimotor skills instead of floating weights. “Distributed training” is a panel of agents each practicing a different skill instead of sharded minibatches. Here's the beauty: ASPIRE gives the tired terms "sim2real transfer" and "cross-embodiment transfer" a whole new meaning. Bridging the sim-to-real gap is notoriously brutal. An end-to-end policy has to swallow both the visual shift (sim looks toyish next to a real camera) and the subtle contact physics it never quite gets right. ASPIRE sidesteps the mess, because it doesn't ship pixels or weights across the gap, but ships the know-how. The robot still has to practice in the real world, not zero-shot, but it gets there way faster because it isn't rediscovering the strategy from scratch. Same for going single-arm to bimanual hardware, which usually requires new data and retraining from zero. ASPIRE achieves up to ~10x cut in "transfer learning” tokens (yes, tokens are the new unit of *training* compute ;) Check out our gallery of 150+ tasks and 90+ skills the robots taught themselves, all on the website! Kind of wild that we can ship the "learned weights" as an HTML page rather than a GGUF. We'll open-source the full stack so your own robot library starts compounding from ours! Deep dive in thread: