arXiv Machine Learning
Jun 18
Hierarchical Planning with Latent World Models
arXiv:2604. 03208v2 Announce Type: replace Abstract: World models are a promising path to zero-shot embodied control through planning.
FlowMPC: Improving Flow Matching policies with World Models
arXiv:2606. 16286v1 Announce Type: cross Abstract: Flow Matching (FM) is a powerful approach for behavior cloning in multimodal action spaces [Jiang et al.
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arXiv AI
Jul 3
WorldSample: Closed-loop Real-robot RL with World Modelling
arXiv:2607. 02431v1 Announce Type: cross Abstract: Reinforcement learning (RL) can overcome the demonstration-coverage limitation of imitation learning (IL) by allowing robots to improve through trial-and-error interaction beyond the states observed in demonstrations.
arXiv AI
23h ago
Q-Learning With World Models
arXiv:2608. 17163v1 Announce Type: cross Abstract: Off-policy reinforcement learning (RL) has become increasingly sample-efficient, enabling applications such as RL fine-tuning of Vision-Language-Action models into reliable, high-performing policies.
arXiv Machine Learning
Jul 13
FlowDAgger: Human-in-the-Loop Adaptation of Generative Robot Policies in Latent Space
arXiv:2607. 08877v1 Announce Type: cross Abstract: Pretrained generative robot policies based on flow matching and diffusion have achieved impressive results across a wide range of manipulation tasks.
arXiv Machine Learning
Jul 10
SeFA-Policy: Fast and Accurate Visuomotor Policy Learning with Selective Flow Alignment
arXiv:2511. 08583v2 Announce Type: replace-cross Abstract: Developing efficient and accurate visuomotor policies poses a central challenge in robotic imitation learning.
arXiv Machine Learning
Jun 8
SERNF: Sample-Efficient Real-World Dexterous Policy Fine-Tuning via Action-Chunked Critics and Normalizing Flows
arXiv:2602. 09580v4 Announce Type: replace-cross Abstract: Real-world fine-tuning of dexterous manipulation policies remains challenging due to limited real-world interaction budgets and highly multimodal action distributions.
arXiv AI
1d ago
Max-Q Selective Imitation for Human-in-the-Loop Online Robot Learning
arXiv:2608. 15088v1 Announce Type: cross Abstract: Human-in-the-loop (HIL) online reinforcement learning for real robots must absorb human interventions quickly while continuing to improve beyond the human prior.
arXiv Machine Learning
Jul 17
DriftWorld: Fast World Modeling through Drifting
arXiv:2607. 15065v1 Announce Type: cross Abstract: Predictive world models enable robots to plan by imagining the outcomes of their actions, but their value for control hinges on generating many rollouts quickly.
arXiv Machine Learning
Aug 11
LUCID: Latent-Skill Unified Control via Imagined Dynamics for Long-Horizon Humanoid Loco-Manipulation
arXiv:2608. 07746v1 Announce Type: new Abstract: Long-horizon humanoid loco-manipulation requires composing versatile whole-body skills and reliable high-level decision making.
Hugging Face Trending Papers
Jul 6
Qantara: Bridge-Flow Training for Multi-Paradigm JEPA Control
Joint-Embedding Predictive Architectures (JEPAs) underpin a growing family of latent world models for control from raw pixels, but every existing JEPA world model commits at training time to a single inference paradigm: either trajectory optimisation in a learned dynamics model, or direct behaviour cloning. A single checkpoint that serves both would defer this choice to inference, when deployment constraints (rollout cost, observation accessibility) determine which path wins.
arXiv Machine Learning
Jun 29
Support-Constrained RL Enables Real-World Policy Improvement without Real-World Experience
arXiv:2606. 27475v1 Announce Type: cross Abstract: Robots trained on real world data tend to be imprecise, slow, and brittle to perturbations.
arXiv AI
Jun 30
WoVR: World Models as Reliable Simulators for Post-Training VLA Policies with RL
arXiv:2602. 13977v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) promises to unlock capabilities beyond imitation learning for Vision--Language--Action (VLA) models, but its requirement for massive real-world interaction prevents direct deployment on physical robots.