VOLT leverages vision-language models for trajectory segmentation, enabling robots to execute tasks up to 2.57× faster while maintaining success rates.

Robert Ramirez Sanchez, Daniel J. Evans, Dylan P. Losey et al.

Proposes iCEM+TL framework integrating transfer learning to boost low-level robotic motion planning success rate by 23%, enabling zero-shot transfer for complex tasks.

Yuanzhi He, Victor Romero-Cano, José J. Patiño et al.

Humanoid-GPT employs a 2B-frame large-scale motion dataset and GPT-style causal Transformer to achieve zero-shot high-dynamic motion tracking, surpassing shallow MLP trackers.

Zekun Qi, Xuchuan Chen, Dairu Liu et al.

Proposed PACT framework uses preference signals and task progress modeling to correct overestimated Q-values, boosting success rate by 24.5% and accelerating convergence 1.3× in real robot tasks.

Zeyi Liu, Guangyao Liu, Yinuo Qu et al.

Introducing FWAV-Sim, a high-fidelity Unity-based simulation platform integrating quasi-steady blade-element aerodynamics, fractal turbulence, and realistic sensor models, enabling robust FWAV autonomous system development.

Haichuan Li, Tomi Westerlund

Pure tactile-based deep RL enables multi-finger robot to control small object separation with success rates close to 94%, validated in real-world transfer.

Ulf Kasolowsky, Berthold Bäuml

IDOL employs inverse dynamics to decode future scene transitions into motion features, significantly improving autonomous driving trajectory planning.

Chenghao Zhang, Timin Li, Dongmei Li

REIS combines lightweight scene gating and KV-guided inference, significantly reducing robotic reasoning redundancy for real-time decision-making.

Joonhee Lee, Hyunseung Shin, Hyunmi Kim et al.

Proposes an adaptive control framework combining artificial time delay estimation with barrier Lyapunov functions for Euler-Lagrange robots, effectively handling state-dependent uncertainties and time-varying constraints.

Saksham Gupta, Rishabh Dev Yadav, Sarthak Mishra et al.

This study reveals architecture-specific failure signatures in VLA models via black-box action monitoring, emphasizing the importance of architecture-matched monitors.

Krishnam Gupta

AwareVLN introduces self-aware reasoning for VLN, achieving NE 4.02 on R2R-CE Val-Unseen, outperforming prior SOTA.

Wenxuan Guo, Xiuwei Xu, Yichen Liu et al.

GesVLA integrates gesture into Vision-Language-Action models, achieving 94.3% target grounding accuracy in complex real-world tasks.

Wenxuan Guo, Ziyuan Li, Meng Zhang et al.

League-based multi-agent RL achieves 22 m/s quadrotor racing with 50% collision reduction vs. single-agent baselines.

Ismail Geles, Leonard Bauersfeld, Markus Wulfmeier et al.

Proposed Branch-Stochastic MPC with scenario clustering improves safety and real-time performance in multi-modal uncertainty motion planning.

Zekun Xing, Ramkrishna Chaudhari, Marion Leibold et al.

SafeManip uses LTLf to evaluate temporal safety in robotic manipulation, revealing task success does not equal safe execution.

Chengyue Huang, Khang Vo Huynh, Sebastian Elbaum et al.

Real-time whole-body teleoperation using Virdyn IMU motion capture, validated on Unitree G1 robot with Sim2Sim and Sim2Real.

Hamza Ahmed Durrani, Suleman Khan

Introduced a passage-aware structural mapping method for RGB-D Visual SLAM to effectively detect doors and traversable openings.

Ali Tourani, Miguel Fernandez-Cortizas, Saad Ejaz et al.

MoT-HRA framework learns human-intention priors from large-scale demonstrations, enhancing motion plausibility and control robustness in robotic manipulation.

Yifan Xie, YuAn Wang, Guangyu Chen et al.

Radar-KISSICP and Radar-IMU improve trajectory estimation in off-road environments.

Shaunak Kolhe, Peng Jiang, Maggie Wigness et al.

ACO-MoE recovers 95.3% performance under dynamic perturbations, enhancing visual RL robustness.

Zhengru Fang, Yu Guo, Fei Liu et al.