RINSE evaluates demonstration quality via trajectory smoothness, achieving 16% higher success on RoboMimic with one-sixth data.

Soham Kulkarni, Raayan Dhar, Yuchen Cui

RecoverFormer employs causal Transformer with latent mode and contact prediction, achieving 100% success in humanoid recovery, zero-shot transfer, and robustness under unseen disturbances.

Zihui Liu

GCImOpt learns efficient goal-conditioned policies by imitating optimal trajectories, significantly improving control task success rates and efficiency.

Jon Goikoetxea, Jesús F. Palacián

GazeVLA learns human intention to enhance robotic manipulation, significantly outperforming baseline methods.

Chengyang Li, Kaiyi Xiong, Yuan Xu et al.

RedVLA identifies physical safety risks in VLA models through a two-stage process, achieving an ASR of 95.5%.

Yuhao Zhang, Borong Zhang, Jiaming Fan et al.

LeHome simulation environment achieves high-fidelity manipulation of deformable objects in household scenarios using PBD and FEM.

Zeyi Li, Yushi Yang, Shawn Xie et al.

dWorldEval uses a discrete diffusion world model for scalable robotic policy evaluation, outperforming WorldEval and others.

Yaxuan Li, Zhongyi Zhou, Yefei Chen et al.

Proposes a biologically-inspired weightlessness mechanism integrating imitation and reinforcement learning for humanoid non-self-stabilizing motions, achieving robust environmental interaction.

Yucheng Xin, Jiacheng Bao, Haoran Yang et al.

VLA Foundry: A unified framework for training Vision-Language-Action models, enhancing multi-task tabletop manipulation policies.

Jean Mercat, Sedrick Keh, Kushal Arora et al.

Mask World Model predicts semantic masks instead of pixels, enhancing robust robot policy learning, excelling in LIBERO and RLBench.

Yunfan Lou, Xiaowei Chi, Xiaojie Zhang et al.

MATCH method improves peg-in-hole task success rate by 35% under high noise, reducing average force by 30%.

Hunter L. Brown, Geoffrey Hollinger, Stefan Lee

RAPIDDS framework enhances human-robot teaming efficiency through multi-cycle spatio-temporal adaptation, significantly improving plan fluency and user preference.

Alex Cuellar, Michael Hagenow, Julie Shah

Gesture recognition using OpenCLIP visual learning model improves AcoustoBot swarm interaction accuracy to 87.8%.

Alex Lin, Lei Gao, Narsimlu Kemsaram et al.

The ESKF-PRE-VMPC framework reduces RMSE by 52.63% and 75.04% in UAV pipeline inspection without wind.

Wen Li, Hui Wang, Jinya Su et al.

LiveVLN breaks the stop-and-go loop in vision-language navigation, reducing waiting time by up to 77.7%.

Xiangchen Wang, Weiye Zhu, Teng Wang et al.

DAG-STL framework achieves zero-shot trajectory planning under Signal Temporal Logic (STL) constraints, significantly enhancing complex task planning capabilities.

Ruijia Liu, Ancheng Hou, Xiao Yu et al.

Enhancing glass surface reconstruction using depth prior improves robot navigation accuracy.

Jiamin Zheng, Jingwen Yu, Guangcheng Chen et al.

Relative state estimation using event-based propeller sensing with error under 3%.

Ravi Kumar Thakur, Luis Granados Segura, Jan Klivan et al.

EmbodiedLGR-Agent integrates lightweight graph representation and retrieval for efficient semantic-spatial memory in robots.

Paolo Riva, Leonardo Gargani, Matteo Frosi et al.

COFFAIL dataset includes successful and anomalous robot skill executions in coffee preparation, supporting imitation learning.

Alex Mitrevski, Ayush Salunke