🍾🍲 Saturday Robotics x IROS 2026 — Robotics Research Night 👉🏻 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#

🤖 @saturdayrobotic Robotics & World Models Reading Club #28#: Dyna-2, @JasonMa2020 (@DynaRobotics). 👥 ~300 registrations Hosts: @junfanzhu98, @aurorafeng_01, @jerryhuang01 (@RoboticsCtr), @zhen_do_ob 🔥 human video as next scaling axis for robotics?

🎐 @saturdayrobotic Robotics & World Models Reading Club #13#, @huang_biwei (Assistant Professor at @UCSanDiego & Founder of @AetherLab_AI) delivered a deep technical vision for what a true Causal World Model (CWM) should be—and why today's frontier AI systems still fall short. Current paradigms: 🔹 LLMs (GPT, Claude, Gemini, π): language correlation models with weak grounding in physical interventions. 🔹 Video generators (Sora, Wan, Veo 3): high-fidelity pixel prediction but limited mechanism modeling, counterfactual reasoning, or causal guarantees. 🔹 JEPA-style models (V-JEPA, AMI): promising representation learning, but not yet fully causal world models. Key diagnosis: ❌ Modeling correlations ✅ Modeling how latent causes generate observations and how the world evolves under actions Causal World Model learns: 📥 Inputs: • Observations • Interventions / Actions • Temporal Information • Context / Environment • Other Agents / Entities ⚙️ Core Components: 1️⃣ Causal Feature Representation Learning Encoder → latent causal variables Z, recovering disentangled causal factors rather than merely compressing observations. 2️⃣ Causal Structure Learning Learn causal graph G: • Directed arrows → causal effects • Dashed links → latent confounders • Explicit latent causal variables and hidden drivers 3️⃣ Causal Dynamic Learning Temporal causal model over t → t+1 → t+2 with intra-time and inter-time causal dependencies. Benefits: ✅ Generalization under distribution shift ✅ Precise forecasting & action control ✅ Data-efficient and model-size-efficient learning ✅ Intervention & counterfactual reasoning ✅ Discovery of hidden mechanisms and new knowledge A recurring principle across tabular data, questionnaires, images, videos, time series, and genomics: 👉 Representation learning should recover latent factors and causal relations among them—not just compress observations. Examples: 📊 Tabular/questionnaire data → latent DAGs with causal variables and latent factors U1–U5 (NeurIPS 2022/2023, ICLR 2024, ICML 2024). 🎥 Images/videos → compositional concept graphs ("chicken eating rice" + "peacock" → "peacock eating rice"), hierarchical concepts, temporal pose graphs z₁,₁…z₃,₆ (CVPR 2026, ICLR 2025/2022, NeurIPS 2024/2022). 📈 Time series → trend, seasonal, cyclical, noise decomposition. 🧬 Genomics → motifs, binding sites, regulatory features. Strong critique of current video world models: ❌ Precise Action Control (WorldGym) ❌ Object Consistency (UniSim) ❌ Physical Consistency (CogVideoX) Beyond scaling, world models need explicit causal structure. Mathematically, the framework jointly models: • Observation function oₜ • Reward function rₜ • Hierarchical latent states sᵢ,ₜ • State dynamics • Action variables aₜ • Structural relation masks Dₛ(·,i) Empirical results: 🏃 Walker2d-v2 & Humanoid-v2: Causal Dynamic Learning (CDL) consistently outperforms Curiosity, CID, and ASR in: • Lower next-state prediction error • Higher policy returns For distribution shifts, the framework introduces: 🔹 Domain factors c 🔹 Structural adaptation parameters θₖ Key insights: • Graphs compactly encode what changed and where • Not all state dimensions matter for action prediction • Not all θ need adaptation • Factors can be adapted independently 🎮 CoinRun experiments further show that new latent variables and structural relations (adding new state dimensions such as s₄) can be introduced while preserving causal factorization, enabling efficient adaptation to new environments, mechanics, and visual styles. World Models = Causal Feature Learning + Causal Graph Discovery + Causal Dynamics + Factorized Distribution Shift Modeling Only then can AI achieve: 🔮 Counterfactual simulation 🎯 Intervention reasoning ⚡ Data-efficient adaptation 🔬 Discovery of previously unknown mechanisms This is not simply scaling larger models—it is a proposal to move from predicting what happens next toward understanding why the world changes.