2026-02-12
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YOR Your Own Mobile Manipulator for Generalizable Robotics (arXiv:2602.11150)
召回要点:低成本开源双臂移动操作平台,强调全身协同能力与可复现实验生态。 -
APEX Learning Adaptive High-Platform Traversal for Humanoid Robots (arXiv:2602.11143)
召回要点:以 ratchet progress reward + 多技能蒸馏实现超腿长高台安全跨越。 -
Data-Efficient Hierarchical Goal-Conditioned Reinforcement Learning via Normalizing Flows (arXiv:2602.11142)
召回要点:在层级 GCRL 中以 normalizing flow 替代高斯策略,强化多模态与低数据效率。 -
RISE Self-Improving Robot Policy with Compositional World Model (arXiv:2602.11075)
召回要点:组合世界模型做 imagination RL,显著减少真机交互依赖并提升动态操作鲁棒性。 -
ST4VLA Spatially Guided Training for Vision-Language-Action Models (arXiv:2602.10109)
召回要点:用空间监督双阶段训练避免 VLA 在动作学习中丢失几何 grounding。 -
EgoHumanoid Unlocking In-the-Wild Loco-Manipulation with Robot-Free Egocentric Demonstration (arXiv:2602.10106)
召回要点:引入机器人无关第一视角示教并对齐视角与动作,缓解人形数据瓶颈。 -
VLA-JEPA Enhancing Vision-Language-Action Model with Latent World Model (arXiv:2602.10098)
召回要点:以 JEPA 潜空间预测替代像素重建,提升 action-relevant 表征鲁棒性。 -
BagelVLA Enhancing Long-Horizon Manipulation via Interleaved Vision-Language-Action Generation (arXiv:2602.09849)
召回要点:交错式语言推理、视觉前瞻与动作生成,提高长时程任务恢复力。 -
Rethinking Visual-Language-Action Model Scaling Alignment, Mixture, and Regularization (arXiv:2602.09722)
召回要点:证明 VLA 扩展关键在对齐与混合策略,盲目加数据会触发负迁移。 -
Sample-Efficient Real-World Dexterous Policy Fine-Tuning via Action-Chunked Critics and Normalizing Flows (arXiv:2602.09580)
召回要点:flow policy + chunk critic 在真机低预算下实现更稳健灵巧微调。 -
Certified Gradient-Based Contact-Rich Manipulation via Smoothing-Error Reachable Tubes (arXiv:2602.09368)
召回要点:将平滑接触误差显式包进 reachable tube,给梯度接触控制提供可认证安全界。 -
Agile asymmetric multi-legged locomotion contact planning via geometric mechanics and spin model duality (arXiv:2602.09123)
召回要点:几何力学结合 spin 对偶搜索,挖掘非对称步态的速度与容错优势。
2026-02-11
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Mimic Intent, Not Just Trajectories (arXiv:2602.08602)
痛点与看点:模仿学习把意图和执行混在一起导致 OOD 易崩;这篇用多尺度动作表示显式解耦 intent/execution,迁移与鲁棒性更强。 -
Differentiate-and-Inject Enhancing VLAs via Functional Differentiation Induced by In-Parameter Structural Reasoning (arXiv:2602.07541)
痛点与看点:长任务中计划与控制纠缠;这篇把任务结构注入参数空间而非只靠提示词分解。 -
Self-Supervised Bootstrapping of Action-Predictive Embodied Reasoning (arXiv:2602.08167)
痛点与看点:推理很长但对动作未必有用;这篇自监督筛出“对动作预测有信息增益”的 reasoning。 -
Why Look at It at All Vision-Free Multifingered Blind Grasping Using Uniaxial Fingertip Force Sensing (arXiv:2602.07326)
痛点与看点:灵巧抓取常依赖昂贵视觉/触觉;这篇用极简传感(单轴力+本体感觉)实现高成功率盲抓。 -
TwinRL-VLA Digital Twin-Driven Reinforcement Learning for Real-World Robotic Manipulation (arXiv:2602.09023)
痛点与看点:真机在线 RL 在 OOD 区域学习效率低;这篇核心是用 twin 做状态分布重加权,显著提升样本效率。 -
DexFormer Cross-Embodied Dexterous Manipulation via History-Conditioned Transformer (arXiv:2602.08278)
痛点与看点:换手型几乎要重训;这篇用共享动作语义空间+时序建模,提升跨本体 zero-shot 泛化。 -
RLinf-USER A Unified and Extensible System for Real-World Online Policy Learning in Embodied AI (arXiv:2602.07837)
痛点与看点:真机在线学习常卡在系统瓶颈;这篇偏基础设施,强调调度、通信和异步训练闭环。 -
StreamVLA Breaking the Reason-Act Cycle via Completion-State Gating (arXiv:2602.01100)
痛点与看点:每步重推理导致高延迟和目标漂移;这篇通过 completion-state gating 实现“该想时想,该控时控”。 -
Learning Human-Like Badminton Skills for Humanoid Robots (arXiv:2602.08370)
痛点与看点:人形高动态全身协调难;这篇展示从模仿到交互打击的渐进式学习流程。 -
UniPlan Vision-Language Task Planning for Mobile Manipulation with Unified PDDL Formulation (arXiv:2602.08537)
痛点与看点:移动+操作任务规划碎片化;这篇统一到 PDDL 表达并连接视觉状态落地。 -
Leveraging Adaptive Group Negotiation for Heterogeneous Multi-Robot Collaboration with Large Language Models CLiMRS (arXiv:2602.06967)
痛点与看点:多机协作决策沟通成本高;这篇用多代理协商机制提升复杂协作效率。 -
SceneSmith Agentic Generation of Simulation-Ready Indoor Scenes (arXiv:2602.09153)
痛点与看点:机器人训练缺可仿真高密度场景数据;这篇以多 VLM agent 协作生成 simulation-ready 场景。 -
Agile asymmetric multi-legged locomotion contact planning via geometric mechanics and spin model duality (arXiv:2602.09123)
痛点与看点:多足接触规划组合爆炸;这篇用几何力学+统计物理对偶给出非对称步态新解。 -
Legs Over Arms On the Predictive Value of Lower-Body Pose for Human Trajectory Prediction from Egocentric Robot Perception (arXiv:2602.09076)
痛点与看点:社交导航里人体特征选取不清晰;这篇发现下肢 3D 姿态对轨迹预测贡献更大。