BrainMem, a hierarchical, evolving memory system, improves long-horizon embodied task success by over 20% in benchmarks.

Xiaoyu Ma, Lianyu Hu, Wenbing Tang et al.

ComFree-Sim is a GPU-parallelized contact physics engine achieving near-linear scaling in contact-rich scenarios, with 2-3x throughput improvement.

Chetan Borse, Zhixian Xie, Wei-Cheng Huang et al.

Proposed dynamic modeling and gravity compensation for dVRK-Si PSM, reducing joint errors by 68-84%.

Haoying Zhou, Hao Yang, Brendan Burkhart et al.

Proposed a decentralized cooperative localization framework with asynchronous sensor fusion, achieving 34% RMSE reduction.

Nivand Khosravi, Niusha Khosravi, Mohammad Bozorg et al.

CRC-CBF provides risk-adaptive probabilistic safety for human-robot navigation, but the supplied paper text reports no numerical outcome metrics.

Jake Gonzales, Kazuki Mizuta, Karen Leung et al.

ReST-RL combines pre-trained locomotion policies with residual modules, achieving 96.9% success in stable humanoid tray transport under disturbances.

Anlun Huang, Zhenyu Wu, Soofiyan Atar et al.

TATIC uses torque estimation and TCN to achieve a 0.904 Macro-F1 score in intent recognition.

Jiurun Song, Xiao Liang, Minghui Zheng

Proposes Step-Aware Contrastive Alignment (SACA), leveraging step-by-step evaluation to improve vision-language navigation in continuous environments.

Haoyuan Li, Rui Liu, Hehe Fan et al.

ZeroWBC leverages egocentric videos and vision-language models to generate and execute whole-body humanoid behaviors without teleoperation, achieving diverse scene-aware actions.

Haoran Yang, Jiacheng Bao, Yucheng Xin et al.

UniUncer enhances driving accuracy by integrating dynamic-static uncertainty, reducing trajectory error by 7%.

Yu Gao, Jijun Wang, Zongzheng Zhang et al.

InterReal combines physics simulation and automatic reward learning to achieve high-precision human-object interaction control in humanoid robots, outperforming baselines.

Dayang Liang, Yuhang Lin, Xinzhe Liu et al.

Contact-Grounded Policy achieves superior dexterous manipulation via generative contact prediction, outperforming existing baselines.

Zhengtong Xu, Yeping Wang, Ben Abbatematteo et al.

SCOUT method uses 3D scene graphs for open-world interactive object search, enhancing efficiency.

Imen Mahdi, Matteo Cassinelli, Fabien Despinoy et al.

Proposed perception-aware time-optimal trajectory planning integrating nonlinear dynamics and visual constraints, enabling high-speed quadrotor racing with improved robustness.

Chao Qin, Jiaxu Xing, Rudolf Reiter et al.

Proposes ADR-VINS, a tightly-coupled monocular visual-inertial filter using pixel reprojection errors, supporting only two corners, achieving 0.143m translation error.

Maulana Bisyir Azhari, Donghun Han, Sung Jun Park et al.

LAD-Drive bridges language and trajectory with action-aware diffusion transformers, improving Driving Score by 59%.

Fabian Schmidt, Karol Fedurko, Markus Enzweiler et al.

Introduces RMBench benchmark and Mem-0 policy, systematically evaluating memory capabilities in robotic manipulation with success rates up to 42%.

Tianxing Chen, Yuran Wang, Mingleyang Li et al.

Minimalist Compliance Control uses motor current signals for sensorless compliance control, applicable to various robots.

Haochen Shi, Songbo Hu, Yifan Hou et al.

OmniXtreme uses flow matching and physics-aware residual fine-tuning to overcome the scalability and fidelity limits in high-dynamic humanoid control.

Yunshen Wang, Shaohang Zhu, Peiyuan Zhi et al.

SPARR combines simulation-trained and real-world residual policies, improving assembly success by 38.4% and reducing cycle time by 29.7%.

Yijie Guo, Iretiayo Akinola, Lars Johannsmeier et al.