RouterVLA leverages smoke tests as supervision via outcome-disjoint cross-fitting, boosting heterogeneous VLA policy selection success from 46.86% to 61.49%.

Xingyu Ren, Chugang Yi, Ge Ma et al.

ReTVL leverages sparse retry event supervision to capture local mistake-recovery dynamics, outperforming progress-based models in fine-grained value estimation.

Xinyao Qin, Junjie Lu, Kaixin Wang et al.

See2Act employs diffusion models for active perception, coupling viewpoint inference with action prediction, achieving 95% success in occluded manipulation tasks.

Kuancheng Wang, Vaibhav Saxena, Shuo Cheng et al.

Integrating Genetic Algorithm and A* with AHP for multi-criteria path planning enhances continuum robot resilience by increasing path diversity and robustness.

Oxana Shamilyan, Ievgen Kabin, Zoya Dyka et al.

Proposes ARC: a unified block-coordinate descent framework combining adaptive robust loss, IRLS, and MWCD for joint state and covariance estimation, robust against outliers and non-Gaussian noise, with automatic parameter tuning.

Alexandre Hadji-Thomas, Andrew Stirling, James R. Forbes

Proposes DO AS I DO framework that reconstructs human hand-object interactions from monocular RGB videos and retargets them to dexterous robots, outperforming state-of-the-art methods.

Bhawna Paliwal, Haritheja Etukuru, William Liang et al.

Analyzes observability and consistency of VINS with anchored feature parameterization, demonstrating improved estimator reliability through theoretical and experimental validation.

Mitchell Cohen, Vassili Korotkine, James Richard Forbes

VERITAS framework uses inference-time verification with visual models to improve robot policies by 10% success rate without additional training.

Mingtong Zhang, Dhruv Shah

R2RDreamer enhances 2D manipulation policies' spatial generalization via 3D-aware augmentation and occlusion-aware video completion, achieving significant performance gains with limited demonstrations.

Xiuwei Xu, Haowen Sun, Angyuan Ma et al.

Proposes MoE-RM-SRL, integrating reward machines, safe distance, and sparse gating experts, achieving safe and efficient highway autonomous driving.

Chufei Yan, Zhihao Cui, Yiyan Lv et al.

Flow Reversal Steering (FRS) leverages reverse flow models to map coarse actions into high-quality behaviors, boosting zero-shot control and rapid learning in robotic policies.

Andy Tang, William Chen, Andrew Wagenmaker et al.

Proposed MCR-Bionic hand integrates anatomical structural priors with hydraulic artificial muscles, achieving dexterous manipulation via wrist-finger coupling and intrinsic muscle pathways.

Haosen Yang, Guowu Wei

This paper introduces DIRECT, a multimodal scene-aware routing framework that dynamically allocates test-time compute among embodied planners, reducing latency by up to 65% while maintaining or surpassing top performance.

Jadelynn Dao, Milan Ganai, Yasmina Abukhadra et al.

Proposes DAR-Net, a transformer-based framework with pixel-level scene supervision, achieving 73.33% accuracy in underwater diver activity recognition on the first UDA dataset.

Sadman Sakib Enan, Junaed Sattar

A Unity-based virtual sea trial framework with traceable command-actuation logging supports IMO/ITTC-standard maneuvering data collection for USV system identification.

Paria Rezayan

JOIN employs opposition-score and task-conditioned manipulability for autonomous heterogeneous bimanual collaboration, achieving 95% success in real-world tests.

Drake Moore, Matt Cheng, Xiang Zhi Tan et al.

This paper introduces a fully distributed multi-UGV exploration framework combining descriptor-based loop closure detection and loop-aware hierarchical planning, achieving AR@1 of 89.9% and reducing exploration time by 15%.

Zhiwei Li, Haiou Liu, Xijun Zhao et al.

This paper introduces a language-driven adaptive cost optimization framework for autonomous driving, leveraging GPT-4 to interpret natural language queries and adjust MPPI control parameters in real-time.

Diego Martinez-Baselga, Khaled Mustafa, Javier Alonso-Mora

MemoryVLA++ integrates memory and imagination for full temporal modeling, significantly improving robotic task success rates.

Hao Shi, Weiye Li, Bin Xie et al.

iMaC translates future robot actions into image controls, significantly improving spatial accuracy in video prediction and policy evaluation.

Zhenyu Wu, Xiuwei Xu, Yukun Zhou et al.