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X Square Robot· @XSquareRobot · X·· 17 天前AI 评分23
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期待参加在匹兹堡举办的 Saturday Robotics × IROS 2026!🤖 我们将展示 X2Real,这是我们用于评估真实世界通用机器人策略的大规模仿真基准——涵盖 10 个能力维度的 44 个分层长时程任务。 期待分享我们的最新工作,并与推进机器人学习、仿真到真实迁移和具身 AI 的研究者和开发者交流。 📍 匹兹堡 📅 2026 年 9 月 28 日 👉🏻 https://luma.com/tzbw7n61 到时见!

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Excited to join Saturday Robotics × IROS 2026 in Pittsburgh! 🤖

We will present X2Real, our extensive simulation benchmark for evaluating real-world generalist robot policies—featuring 44 hierarchical, long-horizon tasks across 10 capability dimensions.

We look forward to sharing our latest work and connecting with researchers and builders advancing robot learning, simulation-to-real transfer, and embodied AI.

📍 Pittsburgh
📅 September 28, 2026
👉🏻 https://luma.com/tzbw7n61

See you there!

引用Junfan Zhu 朱俊帆 ✈️ IROS@junfanzhu98
🍾🍲 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
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来源:X Square Robot · x.com