Introduces AV-ALOHA, a low-cost bimanual robot system with 7-DoF active vision, significantly improving manipulation success rates in occlusion and detail-critical tasks.

Ian Chuang, Andrew Lee, Dechen Gao et al.

GRACE combines LLMs and human explanations, boosting socially appropriate robot actions.

Fethiye Irmak Dogan, Umut Ozyurt, Gizem Cinar et al.

Decentralized multi-agent RL with soft collision avoidance achieves near-time-optimal drone flight, max speed 13.65 m/s, collision rate 5.9%.

Xian Wang, Jin Zhou, Yuanli Feng et al.

DROP employs online sampling predictive control with vision-based pose estimation to reorient objects, achieving performance comparable to RL methods without extensive training.

Albert H. Li, Preston Culbertson, Vince Kurtz et al.

ReMEmbR employs long-term spatio-temporal memory with retrieval mechanisms for robot navigation QA.

Abrar Anwar, John Welsh, Joydeep Biswas et al.

This study systematically compares 13 radar point-cloud extraction algorithms for ICP odometry in autonomous driving, with K-strongest outperforming others by ~14-25%.

Elliot Preston-Krebs, Daniil Lisus, Timothy D. Barfoot

Proposes a bi-objective ant colony algorithm for robot path planning, maximizing expected reward and survival probability in hazardous environments.

Cory M. Simon, Jeffrey Richley, Lucas Overbey et al.

This paper introduces an uncertainty-aware GNN framework for high-level spatial concept generation, improving indoor SLAM accuracy by over 20%.

Jose Andres Millan-Romera, Muhammad Shaheer, Miguel Fernandez-Cortizas et al.

RoboTwin leverages generative digital twins and large language models to enhance dual-arm robot training data diversity and evaluation.

Yao Mu, Tianxing Chen, Shijia Peng et al.

Combines parameter-efficient prompt tuning with flow matching for multi-task robot manipulation, improving stability and inference speed.

Fan Zhang, Michael Gienger

PALO leverages vision-language models for task decomposition, enabling few-shot adaptation of robot policies with only five demonstrations, outperforming traditional fine-tuning.

Vivek Myers, Bill Chunyuan Zheng, Oier Mees et al.

This study introduces a physics-aware, data-free action masking reinforcement learning framework for assembly sequence planning, achieving 100% success on 250+ LEGO structures.

Ruixuan Liu, Alan Chen, Weiye Zhao et al.

ContactSDF employs signed distance functions (SDF) to model multi-contact dynamics, enabling efficient collision detection and time-stepping for dexterous manipulation.

Wen Yang, Wanxin Jin

RAVE checklist offers 15 recommendations to tackle challenges in automated driving system safety studies.

John M. Scanlon, Eric R. Teoh, David G. Kidd et al.

UniT uses VQGAN to learn tactile features from one simple object and transfers zero-shot to multiple tasks.

Zhengtong Xu, Raghava Uppuluri, Xinwei Zhang et al.

Deep generative models (VAE, GAN, DM) enable robots to learn multimodal demonstrations, improving generalization and task diversity.

Julen Urain, Ajay Mandlekar, Yilun Du et al.

R+X leverages vision-language models for retrieval and in-context imitation learning, enabling robots to learn from unlabelled human videos without training.

Georgios Papagiannis, Norman Di Palo, Pietro Vitiello et al.

Proposes TARGO-Net with shape completion and Transformer architecture, achieving robust target grasping under occlusion, evaluated on large synthetic and real datasets.

Yan Xia, Ran Ding, Ziyuan Qin et al.

Proposes MDT, a diffusion transformer that learns versatile multimodal goal behaviors with minimal language annotations, achieving 15% improvement on CALVIN benchmarks.

Moritz Reuss, Ömer Erdinç Yağmurlu, Fabian Wenzel et al.

Open-TeleVision integrates active stereo vision and immersive VR to enable high-precision remote teleoperation, improving data collection for imitation learning.

Xuxin Cheng, Jialong Li, Shiqi Yang et al.