PhysAgent employs multi-agent reasoning and visual feedback to automate physics-based 4D scene synthesis, surpassing traditional gradient-based optimization.

Chunji Lv, Jiaxi Ye, Yuchen Jiang et al.

GraspFoM leverages SAM3D-based 3D priors for reconstruction-driven robotic grasping, achieving state-of-the-art AP and reconstruction metrics.

Dongli Wu, Xiaobao Wei, Hao Wang et al.

EmbodimentSemantic introduces a spatial scene-graph dataset to enhance spatial understanding in robotic vision-language models.

Hassan Jaber, Refinath S N, Luca Cagliero et al.

Proposes Transferability and Predictability as extensions to ISO 26262, enhancing autonomous vehicle controllability and predictability with measurable metrics.

Chaitanya Shinde, Hadi Hajieghrary, Paul Schmitt et al.

Proposes a simulation-based imitation learning framework that automatically generates diverse reach-to-grasp demonstrations, achieving over 90% grasp success in real-world tests.

Kaijie Shi, Wanglong Lu, Huiling Chen et al.

TOAD applies test-time trajectory search using learned reward functions, boosting six planners; NAVSIM-v2 reaches 56.3 EPDMS.

Yihong Xu, Eloi Zablocki, Yuan Yin et al.

Task-Edit method generates diverse trajectories via task editing, enhancing 3D visuomotor policy generalization.

Jian-Jian Jiang, YiHan Yang, Lan Wei et al.

LIMMT uses physics feasibility, diversity, and complexity to select high-quality motion data, outperforming full datasets with only 3%.

Yu Guan, Zekun Qi, Chenghuai Lin et al.

Proposed a cross-view fusion framework significantly enhancing 6-DoF grasp pose estimation robustness, excelling on GraspNet-1Billion.

Kangjian Zhu, Haobo Jiang, Jianjun Qian et al.

Study co-trains robot manipulation policies using everyday human videos, achieving a 29.7% success rate improvement.

Richard Li, Aditya Prakash, Andrew Wen et al.

VOLT leverages vision-language models for trajectory segmentation, enabling robots to execute tasks up to 2.57× faster while maintaining success rates.

Robert Ramirez Sanchez, Daniel J. Evans, Dylan P. Losey et al.

CLEAR integrates Drive-JEPA and single-step latent drift, achieving 93.7 PDMS at 99FPS for efficient multi-modal autonomous driving path prediction.

Yining Xing, Zehong Ke, Zhiyuan Liu et al.

MotionDisco employs LLM-guided evolutionary search combined with sequential kinodynamic optimization to autonomously discover long-horizon humanoid loco-manipulation skills.

Ilyass Taouil, Michal Ciebelski, Shafeef Omar et al.

L-SDPPO integrates spiking neural networks and diffusion policies, achieving high success and 30% energy savings in microgravity space robot tasks.

Liwen Zhang, Dong Zhou, Guanghui Sun et al.

Proposes iCEM+TL framework integrating transfer learning to boost low-level robotic motion planning success rate by 23%, enabling zero-shot transfer for complex tasks.

Yuanzhi He, Victor Romero-Cano, José J. Patiño et al.

RealDexUMI enables dexterous robot learning via a wearable interface, achieving an 88.75% success rate.

Chaoyi Xu, Yixuan Jiang, Jiahui Huan et al.

Discrete-WAM employs shared discrete vision-action tokens for unified world-policy modeling, significantly improving autonomous driving planning performance.

Ziyang Yao, Haochen Liu, Yuncheng Jiang et al.

HORIZON uses recoverability-governed curriculum to expand physical domains, enhancing quadruped robots' zero-shot transfer abilities.

Chenhao Bai, Liqin Lu, Kaijun Wang et al.

PerceptTwin reconstructs semantic scenes from perception data, enabling interactive simulation for robot plan verification, improving success rates by 39%.

Charlie Gauthier, Sacha Morin, Liam Paull

Humanoid-GPT employs a 2B-frame large-scale motion dataset and GPT-style causal Transformer to achieve zero-shot high-dynamic motion tracking, surpassing shallow MLP trackers.

Zekun Qi, Xuchuan Chen, Dairu Liu et al.