MORE enhances zero-shot mobile manipulation planning via scene graphs and task-relevant subgraph filtering, outperforming baselines on BEHAVIOR-1K.

Mohammad Mohammadi, Daniel Honerkamp, Martin Büchner et al.

Proposes RoVI for hand-drawn visual instructions, integrated with VIEW pipeline, achieving 87.5% success in complex robotic tasks.

Yanbang Li, Ziyang Gong, Haoyang Li et al.

Proposes COIL, modeling multi-task interactive learning as a UFL problem, reducing human effort by 23% in experiments.

Shivam Vats, Michelle Zhao, Patrick Callaghan et al.

This survey analyzes how physics simulators influence robotic navigation and manipulation, focusing on physical properties affecting sim-to-real transfer.

Lik Hang Kenny Wong, Xueyang Kang, Kaixin Bai et al.

DRO: Doppler-aware direct radar odometry using SE(2) optimization and Doppler velocity constraints, achieving 0.18% relative translation error.

Cedric Le Gentil, Leonardo Brizi, Daniil Lisus et al.

ARTEMIS couples autoregression and MoE to reach 87.0 PDMS and 83.1 EPDMS on NAVSIM.

Renju Feng, Ning Xi, Duanfeng Chu et al.

Sky-Drive combines CARLA, RPC, digital twins, and bidirectional human-AI mentoring; the paper reports no quantitative performance gains.

Zilin Huang, Zihao Sheng, Zhengyang Wan et al.

Introduced Bias-Eli-W PnP estimator, significantly reducing localization errors on KITTI dataset.

Guangyang Zeng, Yuan Shen, Ziyang Hong et al.

ManipDreamer enhances robotic manipulation models with action trees and visual guidance, improving PSNR to 21.05.

Ying Li, Xiaobao Wei, Xiaowei Chi et al.

SPECI introduces a hierarchical skill prompt framework with dynamic skill codebook and mode approximation, achieving state-of-the-art continual robot manipulation performance.

Jingkai Xu, Xiangli Nie

RoboOcc integrates Opacity-guided Self-Encoder and Geometry-aware Cross-Encoder to improve 3D scene understanding, outperforming SOTA with 8.47% IoU gain.

Zhang Zhang, Qiang Zhang, Wei Cui et al.

RayFronts fuses dense voxel mapping with multi-directional semantic ray frontiers, boosting real-time open-set scene understanding and exploration efficiency.

Omar Alama, Avigyan Bhattacharya, Haoyang He et al.

Introduces GraspClutter6D, a large-scale real-world dataset with 736K 6D object poses and 9.3 billion grasp annotations across 1000 cluttered scenes, significantly improving grasping in complex environments.

Seunghyeok Back, Joosoon Lee, Kangmin Kim et al.

Proposes domain-conditioned scene graphs for state grounding, achieving over 85% accuracy and 90% task success, outperforming LMM-based methods.

Jonas Herzog, Jiangpin Liu, Yue Wang

ASHiTA combines LLM and 3D scene graphs to automatically analyze hierarchical tasks grounded in environment.

Yun Chang, Leonor Fermoselle, Duy Ta et al.

Proposed turning circle-based control barrier function (TC-CBF) integrated with MPC significantly improves collision avoidance for nonholonomic vehicles, reducing unnecessary deceleration by 15%.

Changyu Lee, Kiyong Park, Jinwhan Kim

APEX-MR uses ILP and extended TPG for asynchronous multi-robot cooperative assembly, boosting efficiency by 48% over sequential planning.

Philip Huang, Ruixuan Liu, Shobhit Aggarwal et al.

INPROVF integrates LLMs and formal verification to efficiently repair high-level robot controllers during assumption violations, reducing repair time by up to 70%.

Qian Meng, Jin Peng Zhou, Kilian Q. Weinberger et al.

Active RL-based online demonstration transfer significantly improves success rate and sample efficiency in robotic policy transfer.

Muhan Hou, Koen Hindriks, A. E. Eiben et al.

Neural collision classifier with Eikonal regularization enables differentiable robot rendering avoiding static and self-collisions.

Quanyuan Ruan, Jiabao Lei, Wenhao Yuan et al.