Proposed a sim-and-real co-training framework using Unbalanced Optimal Transport, achieving up to 30% success rate improvement in real-world tasks.

Shuo Cheng, Liqian Ma, Zhenyang Chen et al.

GD2P uses geometry-aware dexterous hands for nonprehensile pushing and pulling, enhancing manipulation efficiency.

Yunshuang Li, Yiyang Ling, Gaurav S. Sukhatme et al.

LAD-VF leverages formal verification feedback with AutoDiff for prompt optimization, achieving over 90% compliance without model fine-tuning.

Yunhao Yang, Junyuan Hong, Gabriel Jacob Perin et al.

Proposed a network flow-based online multi-robot task allocation method incorporating task precedence, coalition effects, and robustness, outperforming offline approaches.

Walker Gosrich, Saurav Agarwal, Kashish Garg et al.

This paper introduces BIM-informed visual SLAM, reducing trajectory error by 25.23% and map inaccuracies by 7.14%, leveraging structural priors for construction site mapping.

Asier Bikandi-Noya, Miguel Fernandez-Cortizas, Muhammad Shaheer et al.

Proposes Any2Track, a two-stage RL framework enabling humanoid robots to robustly track diverse motions under multiple disturbances with zero-shot sim2real transfer.

Zhikai Zhang, Jun Guo, Chao Chen et al.

Proposes a deep learning-based co-pilot framework enabling real-time generation of task-specific robot morphologies, reducing design time from hours to seconds.

Jonathan Külz, Sehoon Ha, Matthias Althoff

ImMimic combines DTW mapping and MixUp, reaching 1.00 success on several tasks with only five robot demonstrations.

Yangcen Liu, Woo Chul Shin, Yunhai Han et al.

SMapper introduces an open-hardware, multi-sensor platform integrating LiDAR, cameras, and IMU for high-precision SLAM data collection and benchmarking.

Pedro Miguel Bastos Soares, Ali Tourani, Miguel Fernandez-Cortizas et al.

Proposes GF-VLA framework combining scene graphs and information theory for dual-arm robot manipulation, achieving over 95% graph accuracy.

Shunlei Li, Longsen Gao, Jiuwen Cao et al.

RaC method enhances robot efficiency and robustness in long-horizon tasks by scaling recovery and correction, reducing data collection time by 10x.

Zheyuan Hu, Robyn Wu, Naveen Enock et al.

Proposed FPTE framework improves grasp success by planning multiple targets and evaluating post-planning success.

Martin Matak, Mohanraj Devendran Shanthi, Karl Van Wyk et al.

Grasp-MPC uses a learned value function inside MPPI control, reaching 67.2% simulated and 74.4% overall real-world grasp success.

Jun Yamada, Adithyavairavan Murali, Ajay Mandlekar et al.

Proposes MM-ITF, a transformer-based multimodal model, achieving 90% accuracy in monocular RGB-based pointing target estimation.

Luca Müller, Hassan Ali, Philipp Allgeuer et al.

U-Arm, a low-cost ($50-$56) universal teleoperation platform, uses mechanical and control optimizations to enable efficient data collection across most commercial robots.

Yanwen Zou, Zhaoye Zhou, Chenyang Shi et al.

ZeST uses LLMs for zero-shot traversability navigation in unknown environments, enhancing safety and efficiency.

Shreya Gummadi, Mateus V. Gasparino, Gianluca Capezzuto et al.

FlowVLA introduces explicit motion reasoning via Visual CoT, significantly improving long-term visual prediction and robot policy efficiency.

Zhide Zhong, Haodong Yan, Junfeng Li et al.

LHM-Humanoid achieves long-horizon human motion control for continuous object transport in cluttered scenes, outperforming baselines significantly.

Haozhuo Zhang, Jingkai Sun, Michele Caprio et al.

GelSLAM employs surface normals and curvature for real-time, high-precision tactile SLAM, achieving submillimeter accuracy over long trajectories.

Hung-Jui Huang, Mohammad Amin Mirzaee, Michael Kaess et al.

GraspQP optimizes diverse, stable dexterous grasps using differentiable force closure, enhancing grasp diversity and stability.

René Zurbrügg, Andrei Cramariuc, Marco Hutter