FIPER combines RND and action-chunk entropy for early failure prediction of generative robot policies without failure data, outperforming existing methods.

Ralf Römer, Adrian Kobras, Luca Worbis et al.

TrackVLA++ enhances visual tracking with spatial reasoning and memory modules, achieving 5.1% and 12% improvements.

Jiahang Liu, Yunpeng Qi, Jiazhao Zhang et al.

Combining large language models with multi-layer scene graphs enables active semantic perception, improving indoor scene understanding speed and accuracy.

Huayi Tang, Pratik Chaudhari

BC-MPPI enhances safety in path planning by integrating a Bayesian constraint layer, ensuring constraint adherence in complex environments.

Odichimnma Ezeji, Michael Ziegltrum, Giulio Turrisi et al.

Proposes SAC Flow, reparameterizing velocity with modern sequence models to stabilize flow policies, achieving state-of-the-art results.

Yixian Zhang, Shu'ang Yu, Tonghe Zhang et al.

dVLA, a diffusion-based multimodal model with chain-of-thought, achieves 96.4% success in robot tasks.

Junjie Wen, Minjie Zhu, Jiaming Liu et al.

Proposes SARM: a video-based stage-aware reward model that improves long-horizon robot manipulation, achieving 83% success in T-shirt folding.

Qianzhong Chen, Justin Yu, Mac Schwager et al.

Proposes CLAP framework combining task decomposition and 3D keypoint prediction, achieving 12% higher success rate on GemBench with only 1/5 training data.

Jianshu Hu, Lidi Wang, Shujia Li et al.

ARMimic uses XR headsets for passive demonstrations, combining hand tracking and virtual robots to improve data collection efficiency and generalization.

Rohan Walia, Yusheng Wang, Ralf Römer et al.

WoW: a 14B-parameter generative world model trained on 2 million robot trajectories, demonstrating physical intuition and causal reasoning.

Xiaowei Chi, Peidong Jia, Chun-Kai Fan et al.

Proposes AURA, an auction-based multi-robot task allocation algorithm that models task requirement uncertainty, improving expected mission value by up to 15%.

Ben Rossano, Jaein Lim, Jonathan P. How

MimicDreamer aligns human videos with robot actions via diffusion-based video synthesis, stabilizes egocentric views, and maps human trajectories to robot joints, boosting VLA performance.

Haoyun Li, Ivan Zhang, Runqi Ouyang et al.

DHAGrasp synthesizes dual-hand grasps guided by text, improving grasp quality on unseen objects.

Quanzhou Li, Zhonghua Wu, Jingbo Wang et al.

Using EgoScaler to automatically extract 6DoF object trajectories from unlabeled egocentric videos significantly improves VLA pre-training, achieving over 20% success rate gains.

Tomoya Yoshida, Shuhei Kurita, Taichi Nishimura et al.

MTRDrive combines memory retrieval and tool interaction, boosting autonomous driving robustness in corner cases by 20% on key metrics.

Ziang Luo, Kangan Qian, Jiahua Wang et al.

RuN framework uses residual policy for natural humanoid locomotion, achieving 0-2.5m/s velocity range with improved training efficiency.

Qingpeng Li, Chengrui Zhu, Yanming Wu et al.

GUIDE integrates global graph inference and diffusion policy, achieving 18.3% faster coverage and 34.9% fewer redundant moves.

Zijun Che, Yinghong Zhang, Shengyi Liang et al.

EgoBridge improves robot imitation learning success rate by 44% through domain adaptation.

Ryan Punamiya, Dhruv Patel, Patcharapong Aphiwetsa et al.

Terra: a hierarchical, terrain-aware 3D scene graph for large-scale outdoor mapping, combining LiDAR SLAM, CLIP-based semantic recognition, and multi-level clustering.

Chad R. Samuelson, Abigail Austin, Seth Knoop et al.

BiGraspFormer is an end-to-end transformer framework that directly generates coordinated bimanual grasps from point clouds, achieving high success and efficiency.

Kangmin Kim, Seunghyeok Back, Geonhyup Lee et al.