Proposed a multi-robot acoustic levitation system with phase control and FPGA IR synchronization, validated for stable contactless airborne transport.

Narsimlu Kemsaram, Akin Delibasi, James Hardwick et al.

KungfuBot employs physics-based whole-body control with adaptive reward tuning, achieving 30% lower tracking error than state-of-the-art, successfully deployed on Unitree G1.

Weiji Xie, Jinrui Han, Jiakun Zheng et al.

The BumbleBee framework employs behavior clustering and iterative sim-to-real transfer to enable robust, general humanoid whole-body control, achieving success rates over 89%.

Yuxuan Wang, Ming Yang, Ziluo Ding et al.

DoublyAware decomposes planning and policy uncertainties, improving sample efficiency and robustness in humanoid locomotion via conformal prediction and group-relative policy constraints.

Khang Nguyen, An T. Le, Jan Peters et al.

Proposes R2SE framework combining hard-case identification and reinforcement fine-tuning to enhance end-to-end autonomous driving.

Haochen Liu, Tianyu Li, Haohan Yang et al.

Fast ECoT accelerates embodied reasoning by 7.5× via caching high-level thoughts and parallel generation, enabling real-time robotic control.

Zhekai Duan, Yuan Zhang, Shikai Geng et al.

Proposes GTRS, a unified framework combining diffusion-based trajectory generation and vocabulary regularization, significantly improving end-to-end multimodal planning robustness.

Zhenxin Li, Wenhao Yao, Zi Wang et al.

Proposes terrain-aware outdoor 3D scene graph generation combining LiDAR and semantic mapping, enabling multi-layer environment understanding.

Chad R Samuelson, Timothy W McLain, Joshua G Mangelson

Proposes pseudo-simulation combining real data and synthetic observations via 3D Gaussian Splatting, achieving R²=0.8 correlation with closed-loop evaluation, outperforming open-loop methods.

Wei Cao, Marcel Hallgarten, Tianyu Li et al.

Using Stochastic Activity Network model to quantify AV safety at intersections, achieving accident rates below 10^-5 per hour.

Peter Popov, Lorenzo Strigini, Cornelius Buerkle et al.

RoboMoRe significantly enhances robot design efficiency via joint optimization of morphology and reward.

Jiawei Fang, Yuxuan Sun, Chengtian Ma et al.

TrackVLA introduces a unified VLA model trained on 1.7 million samples, outperforming SOTA in embodied visual tracking and recognition.

Shaoan Wang, Jiazhao Zhang, Minghan Li et al.

EgoZero leverages egocentric human demonstrations captured via Project Aria glasses to learn robot manipulation policies with zero robot data, using point cloud-based state-action representations.

Vincent Liu, Ademi Adeniji, Haotian Zhan et al.

Robust planning for autonomous driving via mixed adversarial diffusion predictions, enhancing safety.

Albert Zhao, Stefano Soatto

TartanGround is a large-scale multi-modal simulation dataset with 1.44 million samples, supporting perception and navigation tasks for ground robots in diverse environments.

Manthan Patel, Fan Yang, Yuheng Qiu et al.

Integrating the ATACOM safety layer with geometric inductive biases ensures formal safety guarantees for robot foundation models without extensive safe demonstrations.

Maximilian Tölle, Theo Gruner, Daniel Palenicek et al.

Proposes EnerVerse-AC, a multi-level action-conditioned generative model for robotic environment simulation, reducing reliance on physical robots.

Yuxin Jiang, Shengcong Chen, Siyuan Huang et al.

Integrates real-time motion-contact planning with tactile feedback tracking, boosting robustness and precision in multi-finger in-hand manipulation.

Yongpeng Jiang, Mingrui Yu, Xinghao Zhu et al.

AMO combines sim-to-real RL and trajectory optimization to enable high-DoF humanoid whole-body control with expanded workspace and O.O.D. adaptation.

Jialong Li, Xuxin Cheng, Tianshu Huang et al.

VIDEOMIMIC converts monocular videos into environment-conditioned humanoid control policies, enabling robots to perform complex behaviors like stair climbing and sitting in diverse settings.

Arthur Allshire, Hongsuk Choi, Junyi Zhang et al.