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.

OpenEAI-Platform integrates low-cost robotic arm and VLA model, achieving success rates comparable to large-scale pretrained baselines.

Jinyuan Zhang, Luoyi Fan, Leiyu Wang et al.

GTP-FA framework improves robotic manipulation success rates by 30% through failure attribution.

Jiahao Xu, Peiyuan Wang, Hanzhuo Zhang et al.

EaDex uses low-cost demonstrations for dexterous manipulation, achieving a 55.3% improvement.

Qian Zhao, Xin Tong, Chengdong Wu 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

Set-Supervised Diffusion Policy learns action-chunking diffusion through human corrections, enhancing robotic manipulation performance.

Zhaoting Li, Gang Chen, Javier Alonso-Mora et al.

Trans2Occ predicts voxel occupancy of transparent objects from single-view RGB images, achieving 85% grasp success rate.

Yixuan Yang, Sha Zhang, Rui Li et al.

Hierarchical object representation with points, meshes, superquadrics, and inflated superquadrics improves robot scene understanding and navigation accuracy.

Ceng Zhang, Wan Su, Mohamed Samshad et al.

$τ_0$-WM is a unified video-action world model excelling in long-horizon robotic tasks.

Pengfei Zhou, Shengcong Chen, Di Chen et al.

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.

Introduced a physics-grounded tactile representation CoP for zero-shot sim-to-real transfer in complex manipulation tasks.

Jiahe Pan, Stelian Coros, Jitendra Malik 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

Proposed a provably guaranteed polytopic uncertainty quantification framework for 3D SLAM, ensuring true poses are within certified sets with high probability.

Guangyang Zeng, Yulong Gao, Yuan Shen et al.

Introduces EQA-Decision dataset with over 4 million multimodal QA pairs, covering static scene, spatial understanding, task dynamics, and instant decision, for embodied reasoning.

Xicheng Gong, Qiwei Li, Peiran Xu et al.

TapSampling combines Action-VAE candidate sampling with task-progress verification, raising real-robot success from 78.3% to 83.3%.

Sizhe Zhao, Shengping Zhang, Shuo Yang et al.

Lightweight confidence-aware language model achieves SOTA success in autonomous driving decision-making, with low latency and high robustness.

Ruoyu Yao, Ruiguo Zhong, Pei Liu et al.

HumanEgo uses entity-level hand-object interaction representation and flow matching to enable zero-shot robot learning from minutes of human egocentric videos, achieving 92.5% success.

Zhi Wang, Botao He, Kelin Yu et al.