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#Robot models and learning
Oct 18Sun
  1. Guyuehome58

    τ₀-VLA breakdown: Turning subtask decisions into a scalable reasoning problem

    The article breaks down τ₀-VLA, a hierarchical robot foundation model open-sourced on July 27, 2026 by Luo Jianlan's team at the Shanghai Innovation Institute (SII). The model targets long-horizon mobile manipulation on the order of ten minutes in home scenarios, such as meal prep, folding and storing clothes, and tidying across rooms. Its core idea is to turn "what should the next subtask be" into a reasoning problem that can be scaled with more compute.

  2. HuggingFace Daily Papers66

    VLA-Rolling: Reusing Action Tails to Improve VLA Control with Fewer Denoising Steps

    VLA-Rolling is a lightweight inference framework that reuses the action tails discarded during rolling planning by flow-matching VLA models. Through three stages—action-tail inheritance, per-action confidence estimation, and renoising with denoising step allocation—it reuses denoising progress while keeping the underlying VLA frozen.

  3. HuggingFace Daily Papers42

    World Model Interpretability: From Decoding Representations to Understanding Latent Worlds

    A Perspective article argues that Joint-Embedding Predictive Architectures (JEPA) are moving from representation learning toward latent world models that support prediction, planning, and control, and that interpretability research must shift from "what is encoded" to how latent variables interact, evolve, and causally drive prediction.

  6. HuggingFace Daily Papers62

    HumanoidPose: Estimating Full-Body Joint Poses of Humanoid Robots from Video

    The paper proposes the HumanoidPose framework, which treats vision as proprioception and recovers a humanoid robot's full-body joint configuration in world coordinates from video alone. The method combines pretrained human pose features with robot kinematics in a unified model and retargets human motions to sixteen different humanoid embodiments, enabling scalable learning entirely from synthetic data; it also uses unlabeled online videos through self-training to develop a 2D robot pose detector to improve real-world generalization.

  8. Guyuehome46

    asRoBallet: Dynamic Balance Control of Spherical Underactuated Robots via Friction-Aware Reinforcement Learning

    asRoBallet achieves dynamic balance control of spherical underactuated robots through friction-aware reinforcement learning. Starting from underactuated dynamics and the two-layer contact friction among wheel, ball, and ground, it derives the kinematic mapping and dynamic balance mechanism of the spherical chassis. The method uses proximal policy optimization (PPO) to train position-holding and velocity-tracking policies, and applies friction domain randomization to improve policy adaptability under different friction conditions.

  9. Guyuehome51

    TD-MPC2: Model-Predictive Reinforcement Learning Control Practice with Latent World Models

    The author uses official pretrained weights to drive a quadruped canine model through real running inference on a local CPU, reconstructing item by item the mathematical construction of the latent world model, the consistency learning objective, symlog two-hot encoding, and MPPI-style sampling-based planning, and records observed phenomena such as planning distribution contraction and the gap between estimates and realized outcomes.

  10. Guyuehome46

    Humanoid Robot Shooting and Goalkeeping Control Based on Motion-Prior Reinforcement Learning

    A G1 shooter based on motion-prior reinforcement learning scored on all 60 penalty kicks, and a goalkeeper using historical encoding saved 85% of shots. However, when the two models were placed in the official adversarial scenario, they scored zero goals across 20 matches; the article uses frame-by-frame data to identify a two-layer distribution-boundary mechanism.

Oct 17Sat
  2. 机器之心 · 微信公众号70

    HKU's EgoWild2Dex trains dual-arm robot on 538.9 hours of "extremely dirty" human data, with under 1 hour of real-robot data

    The University of Hong Kong's MMLab team proposes EgoWild2Dex, which directly trains a dual-arm dexterous robot on 538.9 hours of unscripted human first-person manipulation data, using under 1 hour of real-robot data per downstream task; across three long-horizon tasks—unpacking a parcel, fixing ornaments with a hot glue gun, and making a cold drink—the average success rate is 96.7%.

  3. 钛媒体72

    General-purpose large models are redoing robot R&D and delivery

    Chen Youwen, an analyst at Zhixun Think Tank, compared two paths by which general-purpose large models are entering robotics: MicroFactory, in a single customer deployment, uses agents based on Claude Opus and GPT-6 Astra to generate control plans and dedicated programs for execution; according to the founder's own account, the robot autonomously assembled electronic products for 1.5 consecutive hours with zero operational errors throughout; Poke & Wiggle uses Claude Opus 5.5 to take over the entire process of reading papers, writing algorithms, training models, and testing on real robots, reproducing 10 recent papers.

  4. 深蓝AI · 微信公众号50

    Zhejiang University's GuardPIBT enhances PIBT with residual reranking, completes conflict-free 3D planning for 1 million agents in simulation

    A Zhejiang University team proposed GuardPIBT, which, while retaining the PIBT execution kernel, uses a neural network to make residual reranking proposals over PIBT's native candidate actions; conflict checking, priority inheritance, and backtracking are still handled by the PIBT kernel, and counterfactual grouped gating is introduced to filter harmful interventions.

  5. 深蓝AI · 微信公众号29

    Principles and Application Differences Between CNN and Transformer in Machine Vision

    In machine vision, CNNs and Transformers divide work according to real-time performance and complexity: CNNs handle lane recognition, close-range obstacle detection, traffic sign classification, and workpiece positioning and defect detection for industrial robots, while Transformers handle bird's-eye-view scene reconstruction, multi-sensor fusion, trajectory prediction, and drivable-area segmentation.

  6. 高工人形机器人 · 微信公众号60

    CASBOT PA perception-to-action model deployed at shoe factory in Putian

    CASBOT PA (Perception-to-Action), a perception-to-action model developed in-house by CASBOT, has been deployed at real workstations in the Putian shoe factory of Fujian Xinbai Sports. At this stage, a robotic arm serves as the execution platform, with the pipeline running from visual perception to motion generation, kinematics processing, and real-machine execution.

  7. Reddit r/robotics38

    2-hour reinforcement learning simulation of a quadruped robot dog in MuJoCo

    In a training platform built on a self-made MuJoCo physics simulator, the developer trained a quadruped robot dog simulation using reinforcement learning with motion imitation (style reference motions). Trained for only 2 hours on a MacBook Air CPU (no GPU), it learned a keyframe trot gait and balance. Joints are limited by a realistic hobby servo model in torque, speed curve, backlash, and latency, making the gait smoother than pure reinforcement learning. The simulation supports WASD-driven multidirectional control.

  8. 机器人产业应用 · 微信公众号74

    How far has Generalist AI GEN-1.5's physical prompting paradigm progressed?

    Generalist AI released GEN-1.5 on August 19, 2026; it can attempt new tasks from a single 3-12 second task demonstration without gradient updates, achieving a one-shot average success rate of 59%±10% across 10 short manipulation tasks, and this can be raised to 83%±9% by adding about 5 minutes of data and performing 10 gradient updates.

  9. Reddit r/robotics49

    Team fully switches to frontier LLMs for robot control in customer deployments

    A team fully switched to using frontier LLMs to control robots in customer deployments over the past month. The architecture resembles a System 1/System 2 division of labor: high-level reasoning is handled by agents based on Opus and Astra, while the fast execution layer uses computer vision code written by the models themselves, which they describe as fast, editable, and reliable. LLMs also bring in-context learning and continual learning; when new situations arise, the LLM steps in and adjusts the process.

Oct 16Fri
  1. 高飞的电子替身 · 微信公众号63

    AlphaGo lead programmer David Silver: Abandon human data, achieve superintelligence within three years

    On the Sequoia Capital podcast Training Data, David Silver, lead programmer of AlphaGo and co-founder of Ineffable Intelligence, spoke with Lasse Espeholt. Silver gave two new predictions: strong evidence within one year that the superlearner approach is viable, and superintelligence within three years, more aggressive than his original estimate of three to five years.

  3. PaperWeekly · 微信公众号54

    Beihang KDD 2026 paper proposes MoE-Pointer, using Seq2Seq reinforcement learning to jointly optimize rider-drone relay delivery

    Professor Wang Jingyuan's team at Beihang University, together with the School of Management at Shanghai University, published the paper MoE-Pointer at KDD 2026, defining the Dynamic Multi-Stage Pickup and Delivery Problem (DM-PDP) and using a sequence-to-sequence generation framework to achieve end-to-end reinforcement learning joint optimization of a three-stage rider-drone delivery chain.

  4. Xbotics具身智能实验室 · 微信公众号19

    Xbotics Teams Up with Hangzhou Robotics School for 'Embodied Frontier Half-Day Offline Course': Getting the MicroDuck Robot Duck Moving

    Xbotics and Hangzhou Robotics School jointly present the 'Embodied Frontier Half-Day Offline Course,' held on October 25, 2026, 13:00–17:00 on the 3rd floor of Hangzhou Robotics School, at 400 yuan per person, limited to a small class of 15.

  6. 机器之心 · 微信公众号69

    From Hardware and Data to Models: How Sharpa Systematically Advances Dexterous Manipulation

    In a conversation with Synced, Sharpa's academic lead Zhang Kaifeng outlined three main threads for advancing dexterous manipulation: human-centered data collection, high-frequency tactile low-level control that decouples motion from interaction, and tactile-anchored cross-embodiment representation learning.

  7. Reddit r/humanoidrobotics22

    Opinion: VLA models alone cannot achieve safe humanoid robot deployment

    A Reddit user argues that vision-language-action (VLA) models alone are not enough to safely integrate humanoid robots into daily life. Humanoid robots far exceed humans in strength and speed; without complex multi-sensory input, learning from video by vision alone cannot master the human instinct to adjust force at the right moment in tasks such as holding a child's arm, carrying a cake, or closing a door, which can easily cause accidents and hinder the industry's adoption.

  8. AI前线 · 微信公众号78

    Google releases Gemini 4 Argon physical reasoning model, connecting the full robotics R&D chain

    Google has released Gemini 4 (Argon), positioned as a general physical reasoning model focused on interaction with the physical world, covering the full robotics lifecycle from control execution and simulation reconstruction to data annotation and self-improvement. On the ERQA embodied reasoning benchmark, Argon scored 87%, surpassing Claude Opus 5.5 (82.3%) and GPT-6 Astra (81%); in the AA-Omniscience test, its hallucination rate was 15%, lower than the 51% and 59% of the latter two.

    Why it matters: The article breaks down the model's specific uses and measured figures across the robotics R&D chain in four layers: control, simulation, data quality inspection, and self-improvement.

  10. Robotics & Automation News64

    Skild AI trains S1 model to play soccer via over 140 years of simulated self-play, then transfers it to humanoid robots

    Skild AI demonstrated a self-play training method in which its S1 robot foundation model learned through more than 140 years of simulated soccer matches in Nvidia Isaac Sim, without relying on human demonstrations. In simulation, the policy successively learned to recover from falls, dribble past opponents, shield the ball, and tackle, and was then transferred to physical humanoid robots for soccer-kicking demonstrations. The company says the method has been extended to everyday robotic tasks, and early experiments with four-agent passing plays have been conducted.

  11. 亿欧网 · 微信公众号60

    Google, OpenAI, and DeepSeek Enter Together; Embodied AI CEO Says He Can't Sleep

    The article analyzes how, with the releases of GPT-6 Astra, Gemini 4 Argon, and the Pi API stacking up, the semantic, simulation, and fine-tuning layers of embodied intelligence are being taken over by general foundation model companies, and the world model is being demoted from overall commander to physical verifier.

  12. 机器人产业应用 · 微信公众号57

    CASBOT PA perception-to-action model deployed on real production line at Putian shoe factory

    CASBOT PA (Perception-to-Action), a perception-to-action model independently developed by CASBOT, has been deployed at real workstations in Fujian Xinbai Sports' Putian shoe factory. At the current stage, a robotic arm serves as the execution platform, with the pipeline running from visual perception to motion generation, kinematic processing, and real-machine execution.

  13. D-Robotics Developer Community72

    D-Robotics releases consolidated RoboGo × Model Zoo development images: 7 current versions covering vision, OCR, speech, and LeRobot

    The D-Robotics developer community has compiled its previously shared RoboGo × RDK Model Zoo development images into an index: 5 categories and 7 current-version images, plus 3 historical vision versions, covering X5/S series vision v1.1/v1.2, PaddleOCR v3/v6, Paraformer, Depth Anything V2 Small, and LeRobot ACT / π0.

  15. 未来机器人网 · 微信公众号55

    Wang Xingxing and Liang Wenfeng's moves point to an embodied intelligence inflection point next year

    The article analyzes three developments side by side and argues that the robotics industry may reach an inflection point next year: at a roundtable dialogue at the China International Industry Fair, Wang Xingxing said that, based on Unitree's deployment of GPT-6 to multiple robots for field testing, task completion exceeded expectations.

  16. 中科慧灵 · 微信公众号60

    CASBOT PA perception-to-action model deployed on production line at Putian shoe factory

    CASBOT PA (Perception-to-Action), a perception-to-action model developed in-house by CASBOT, has been deployed at real workstations in the Putian shoe factory of Fujian Xinbai Sports, currently using robotic arms as the execution platform, with a pipeline of visual perception, motion generation, kinematic processing, and real-machine execution.

  17. HuggingFace Daily Papers62

    OfficeTown: An Organizational Simulation Framework and Benchmark for Evolving Agent Capabilities

    Researchers propose OfficeTown, a professional organization simulation framework initialized from a natural-language description of an organization; members coordinate work, delegate tasks to assistant agents, and review deliverables, while artifacts, decisions, and unresolved matters continuously shape subsequent work.

  19. HuggingFace Daily Papers47

    What Kinds of Symbols Are Suitable for Hybrid Discrete-Continuous Planning?

    The paper proposes defining the usefulness of symbols with the solving algorithm at the center, introduces the concept of "solver-sufficient symbolization," and develops a counterexample-guided symbol invention algorithm for finite-horizon piecewise-affine systems. The method can be extended to optimization objectives that use neural networks as energy functions, inducing solver-sufficient symbols from continuous optimization landscapes. Simulations and real-robot task experiments show that the resulting symbols reduce solving cost while maintaining solution quality, and improve interpretability and manipulability.

  20. HuggingFace Daily Papers60

    RoboHarness: Simple robot framework outperforms VLA and world action models on BEHAVIOR tasks

    The research team proposes RoboHarness, which gives LLM agents a visual-geometric control panel so they can directly understand and invoke embodied tasks. With this framework, Qwen3.8-Flash-Next surpassed the current state-of-the-art VLA models on BEHAVIOR Challenge 2025 tasks, and the implementation is open source. The authors believe coding agents have the potential to achieve generalist embodied manipulation without relying on large-scale training of vision-language-action models.

  21. HuggingFace Daily Papers47

    TreeOPSD: A Tree-Aware Posterior Self-Distillation Framework for Long-Horizon Agentic Reinforcement Learning

    TreeOPSD proposes a critic-free, tree-aware posterior self-distillation framework for long-horizon agentic reinforcement learning. It treats each interaction turn as a tree node, branches alternative continuations from shared prefixes, and recursively backs up terminal rewards to obtain implicit turn-level credit.