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#Research
Oct 20Tue
  1. HuggingFace Daily Papers69

    Learning from Fleet Experience: Improving Robot Manipulation Policies with Shared Fleet Experience

    The paper proposes the collective experience learning (CEL) paradigm, in which deployed robots share execution experience to improve a common policy without collecting additional expert demonstrations or corrective actions. It uses failure-guided experience retrieval fine-tuning (FERT) to retrieve relevant trajectory segments from the fleet's shared successful experience as supervision, and a fault-tolerant asynchronous training system (FATS) to coordinate experience collection, policy optimization, and distribution.

  2. HuggingFace Daily Papers55

    Holi-World: Building Holistic World Intelligence from Human First-Person Experience

    The study proposes the HOLI-WORLD integrated ecosystem, which connects experience collection, world representation, automated data construction, model learning, and independent evaluation. HOLIWORLD-Foundry combines static and dynamic perception to automatically construct world records, and HOLIWORLD-Data organizes approximately 100 self-collected scenes into approximately 80K QA pairs used to train HOLIWORLD-VL-4B; independently human-annotated held-out recordings form HOLIWORLD-Bench.

  3. HuggingFace Daily Papers50

    AdaExploreBench: Benchmarking LLM Agents for Proactive Exploration and Test-Time Adaptation in Evolving Environments

    AdaExploreBench introduces a benchmark that instantiates environmental dynamics as hidden rules, requiring LLM agents to actively acquire evidence and infer the rules, and, under implicit rule drift, to detect that prior knowledge has become stale and re-explore. Experiments on eight leading LLMs show that GPT-6 Sol achieves only 61.9% calibrated accuracy; providing sufficient evidence or removing rule drift improves average performance by close to 10%. The results indicate that agents can reason from valid evidence but struggle to actively acquire evidence and to recognize when evidence becomes invalid.

  4. HuggingFace Daily Papers50

    Vision-Language-Delta-Action: Using Compact Deltas as an Executable State-Transition Interface for Robotic Manipulation

    VLDA proposes modeling task-relevant state transitions with compact Delta variables, replacing VLA's direct observation-to-action mapping and the full future-state prediction of world-action models. It learns Delta under a unified objective via LT-Filter and GT-Anchor, and DDAR injects Delta into the action denoising process as a structured seed. Experiments implemented in the open-source training framework LoongForge show that VLDA improves long-horizon consistency, robustness, and generalization.

  5. CVer · 微信公众号20

    World Models: From Beginner to Publishing at CCF-A

    A world-model resource repository lays out an advancement path from conceptual introduction and theoretical foundations to hands-on coding, must-read papers, and open-source projects. It covers introductory tutorials, 14 must-read beginner papers with source code, 101 papers from top conferences in 2026, and 20 papers with source code for each of 5 trending directions: world models + VLA, reinforcement learning, agents, world action models, and video world models.

  6. HuggingFace Daily Papers62

    AnyGoal: Evaluating World Models' Compositional Generalization Beyond Expert Trajectories

    The study introduces the AnyGoal benchmark and open-source library to evaluate world models' compositional generalization on goal-shifted task variants. The task design ensures that individual state factors and actions remain within the training support, but their combinations are underrepresented in expert trajectories. Experiments across multiple environments and world model families show that current models consistently fail under these recombination scenarios, and the authors further analyze where failures occur at the dynamics and representation levels.

  7. HuggingFace Daily Papers53

    TokenWorth: Learning which memories are worth keeping under resource constraints

    The paper proposes TokenWorth, a lightweight online memory framework that learns query-relevant memory value from sparse execution feedback and applies it consistently to memory retrieval and bounded memory management. Across three LifelongAgentBench environments and in both local and API-based deployments, TokenWorth achieves the highest task success rate among the evaluated methods while using the fewest output tokens. The code is publicly available at https://github.com/AIGNLAI/TokenWorth.

  8. HuggingFace Daily Papers55

    EgoXperience: Learning World-Aligned Egocentric Representations from Human Experience

    EgoXperience proposes a complete pipeline covering data collection, automatic annotation, unified modeling, and benchmark evaluation, aimed at jointly reconstructing observer motion, scene geometry, and hand-object interactions from egocentric video. At its core is the automatic annotation engine EgoXX-Engine, which combines geometrically verified object tracking with SLAM-based world alignment to produce the 50-hour dataset EgoX-Data-6M (with second-level task descriptions) and the 1-hour evaluation set EgoX-Bench.

  9. HuggingFace Daily Papers56

    Stateful Harness: Separating World State Evolution from Video Rendering to Support Rule-Governed Interaction

    The paper proposes the Stateful Harness framework, which separates world evolution from video rendering, using a frozen video model to render events and write back the world state, in order to support rule-governed interaction with hidden state. The authors build the WorldState-Bench benchmark, containing 50 counterfactual twin pairs that share an initial frame and action but differ in hidden state and required outcome.

  11. 雷科技73

    X Square Robot releases X2Real simulation benchmark, with 0.96 correlation between simulated and real-robot task success rates

    X Square Robot released the X2Real simulation benchmark for evaluating the performance of embodied models in the real physical world, proposing three design principles: credibility, validity, and objectivity. In testing, X2Real simulation evaluation achieved a 0.96 correlation with real-robot evaluation in task success rate and a 0.99 correlation in progress score; control parameter calibration achieved millimeter-level trajectory consistency, covering embodiments including Franka, ArtiXon Arm-6A, ARX R5, and Quanta X1.

  12. HuggingFace Daily Papers57

    Fréchet Forcing: Training Long-Horizon Streaming World Models with Short Video Clips

    Fréchet Forcing proposes a fine-tuning method that keeps streaming video world models stable during long-horizon generation beyond the training length and lets them continue to follow action commands. The method applies a flow-matching loss where self-generated history meets real data, constrains context fidelity with a Fréchet distance centered on the real clip itself, and enforces visual realism with a Fréchet distance on real frames in a frozen perceptual space. All three constraints come from short video clips, with no teacher model, online judge, or long videos needed.

  13. HuggingFace Daily Papers57

    ScaleNav: Internet Video Pretraining for Language-Conditioned Local Navigation

    ScaleNav proposes a unified world-action framework that achieves language-conditioned local navigation through three-stage training: first, instruction-conditioned video pretraining on internet first-person videos to learn how observations evolve as the camera approaches a target; then, aligning predicted executable action chunks using action-labeled simulation data; and finally, jointly fine-tuning all trainable components.

  14. Reddit r/robotics38

    Tabletop battle simulation of two open-source Jumper robot crabs in MuJoCo

    Zymbiotek created a tabletop battle simulation in MuJoCo of two open-source Jumper robot crabs for a KingKong Robotics competition. The motions are physics-simulated based on measured servo torque curves, and the dance is a reinforcement learning policy that can run on the real robot. The counter uppercut is a real simulated motion, with only the final flip being animation—testing showed one Jumper cannot flip another.

  18. HuggingFace Daily Papers72

    AgenticRoboRL: Improving Robot Planners with Coding-Agent Post-Training Methods

    AgenticRoboRL proposes a framework for post-training robot planners across different robot execution harnesses. The training recipe combines supervised fine-tuning with multi-turn reinforcement learning, and harness-specific adapters connect planner interaction records and task outcomes to a unified RL training pipeline while keeping execution components fixed.

  19. Beijing Humanoid Robot Innovation Center58

    Renmin University GeWu-Lab and Beijing Humanoid Innovation Center to livestream Robo-ValueRL millimeter-level VLA reinforcement learning solution

    Renmin University of China's GeWu-Lab and the Beijing Humanoid Innovation Center will livestream a presentation of the Robo-ValueRL millimeter-level VLA reinforcement learning solution on October 21, 2026 at 19:00 Beijing time, with talks by Hu Di and Xia Wenke.

  21. Tech Xplore Robotics62

    Seoul National University's Dynin-Robotics robot foundation model guides actions by predicting future scenes, achieving 78.4% average success rate in real-world operation

    Professor He Jiong's team at Seoul National University's AIDAS Lab proposed Dynin-Robotics, a robot foundation model that unifies language, images, video, and robot actions in a single model, generating and selecting actions by predicting future scene images after an action; the paper has been accepted by NeurIPS 2026.

Oct 19Mon
  1. CVHub · 微信公众号74

    XPeng and multiple universities propose 4B perception foundation model GroundingPI, unifying point, box, and text localization and validating action transfer

    Researchers from XPeng and Peking University, the University of Hong Kong, UC Berkeley, Princeton University, National University of Singapore, Tsinghua University, and Hong Kong University of Science and Technology (Guangzhou) jointly proposed a 4B perception foundation model, GroundingPI, which unifies point selection, boxes, text, document regions, and interface controls into a single spatial token output protocol. The architecture uses a MoonViT-V2 vision encoder and a Qwen3-4B language backbone, and training includes supervised fine-tuning and GRPO reinforcement post-training.

  2. 机器人前瞻 · 微信公众号73

    Ropedia and multiple universities release cross-embodiment mobile manipulation world model Ropedia W0

    Singapore-based embodied intelligence startup Ropedia, together with Nanyang Technological University, Duke University, UC San Diego, and Tsinghua University, released the cross-embodiment mobile manipulation world model Ropedia W0, which uses a unified action representation to predict first-person future scenes after humans, robotic arms, and humanoid robots perform actions.

  3. Xbotics具身智能实验室 · 微信公众号76

    SenseTime launches SenseNova-RoboRSI and a 27B embodied model, achieving SOTA on 12 simulation benchmarks

    The SenseTime SenseNova team launched the SenseNova-RoboRSI unified execution framework and the 27B embodied model SenseNova-RoboRSI-27B-Preview.

    Why it matters: Through comparison data from the same model with different execution systems, readers can intuitively understand the magnitude of the execution framework's impact on embodied task performance and its cost.

  4. 智源研究院 · 微信公众号68

    BAAI's FlagOS 2.2 released: over 1,454 operators, 27% cross-chip performance gain

    BAAI presented FlagOS 2.2 progress at the 2026 AI Open Computing Conference and FlagOS Technical Conference; the version was released on September 30. Public operators in the nine operator libraries rose from 564 to 1,454 (890 added). In tests covering over a thousand operators and 10 heterogeneous AI chips, cross-chip operator performance improved by about 27% over the previous version. The FlagOS-Compressor weight conversion and quantization tool was released for the first time, enabling a new model to be adapted to as many as 12 chips within one day.

    Why it matters: The original text provides specific figures for operators, quantization, and serving throughput, which readers can use to assess the performance and cost potential of existing chips for running new models.

  5. PaperWeekly · 微信公众号56

    Central South University and other institutions release the first systematic review of system-level agent distillation

    A research team from Central South University, the Beijing Academy of Artificial Intelligence, Hong Kong Polytechnic University, Beijing University of Posts and Telecommunications, and other institutions jointly released a 57-page systematic review of system-level agent distillation. The paper is titled Distilling Agentic Systems: A Roadmap across Models, Artifacts, and Harnesses, and is available at https://arxiv.org/abs/2609.36630.

  6. 自变量机器人 · 微信公众号74

    X Square Robot releases X2Real simulation benchmark, with 0.96 correlation between simulated and real-robot task success rates

    X Square Robot has released X2Real, an evolvable simulation evaluation benchmark. Its simulated evaluation and real-robot evaluation achieve a task success rate correlation of 0.96 and a progress score correlation of 0.99.

    Why it matters: It provides correlation data between simulated and real-robot evaluation along with a unified integration method, allowing readers to judge whether simulated evaluation can replace part of real-robot testing.

  11. arXiv cs.RO64

    GroundingPI releases 4B grounding foundation model, averaging 73.68% across 34 benchmarks

    GroundingPI is a 4B-parameter grounding foundation model that outputs points and boxes as quantized coordinates in a shared vocabulary; its training combines multimodal and spatial pretraining, supervised fine-tuning, and GRPO reinforcement learning. It averages 73.68% across 34 grounding benchmarks and 44 baselines, above GPT-6 Astra's 71.54%.

  12. arXiv cs.RO57

    GroundAnything: Balancing Parallel Decoding and Precise Visual Grounding with Blockwise Denoising

    GroundAnything is a 4-billion-parameter visual grounding foundation model that combines parallel decoding with precise object localization through blockwise denoising. Its training integrates pretraining on public datasets, direct conversion from autoregressive to diffusion, supervised fine-tuning, and GRPO-based reinforcement post-training.

  13. arXiv cs.RO67

    Metro-WM: Long-horizon latent planning with realizable sub-goals

    Metro-WM proposes a hierarchical planning framework that, instead of generating unconstrained latent sub-goals, retrieves real states from observed frames in offline expert demonstrations or random-action trajectories as sub-goals, and connects frames from different episodes into paths toward the goal. Experiments show its long-horizon success rate is up to 37.33 percentage points higher than the next-best hierarchical method, up to 10.9 times faster, uses 13-56 times less offline computation, and requires less tuning. The authors also note that leading macro planners frequently output physically unrealizable sub-goals.

  14. arXiv cs.RO57

    EgoTac: Predicting In-the-Wild Touch from Egocentric Vision

    EgoTac is a generalizable model that predicts rich tactile information directly from egocentric human videos. It was trained on more than 5.7 million image-tactile pairs covering both continuous force measurements and binary contact. Experiments show an average force error below 0.06N for in-domain prediction, and on out-of-domain contact prediction benchmarks it consistently outperforms the current state-of-the-art contact estimator. It also enables zero-shot prediction on unconstrained real-world videos; scaling analysis indicates that both data diversity and data volume steadily improve performance. The work was accepted to NeurIPS 2026.

  15. arXiv cs.RO55

    CorrelationFlow: A Training-Free LiDAR Scene Flow Estimation Method Based on BEV Correlation

    The paper proposes CorrelationFlow, which reformulates LiDAR scene flow estimation as a correlation maximization problem between Bird's-Eye View (BEV) images, replacing learnable parameters with a chain of image processing operations. This training-free method won second place in the unsupervised track of the Argoverse 2 Scene Flow Challenge (CVPR 2026). The test set covers five datasets and a variety of vehicle platforms and environments, and results show its performance can match or even surpass learning-based methods. The source code will be open-sourced.

  16. arXiv cs.RO51

    NEXUS: An Embodied Planning Framework Jointly Learning Safety Constraints and Action Models

    NEXUS is a modular neuro-symbolic framework that jointly maintains a PDDL action domain model and an LTL safety specification: execution failures are used to repair action schemas, while detected hazards are converted into reusable LTL constraints through hierarchical semantic and translation sampling with equivalence consensus.

  19. arXiv cs.RO46

    Tidying Tasks for Robot Vacuums via Geometry-Induced Self-Locking Deployable Linkage

    The study proposes a two-stage deployable linkage that self-locks through its own geometry and is integrated into a commercial robot vacuum to support pushing-type tidying tasks. The mechanism uses a single actuator to deploy and lock, a passive trigger to release the lock for retraction, and a second actuator to adjust deployment height only; at full deployment, an internal three-link subchain approaches a collinear, near-singular configuration in which the transmission ratio tends toward zero, so loads produce only a very small residual back-driving torque.