Introduced DA² dataset with 9M dual-arm grasp pairs, incorporating multi-metric labels, enabling end-to-end evaluation models for robotic manipulation.

Guangyao Zhai, Yu Zheng, Ziwei Xu et al.

Introduces 'Curse of Rarity' concept, analyzing how low frequency of safety-critical events in high-dimensional driving environments hampers autonomous vehicle safety.

Henry X. Liu, Shuo Feng

Deep learning-based 6DoF grasp synthesis using sampling, regression, RL, and exemplars, greatly improving robotic grasp success rates.

Rhys Newbury, Morris Gu, Lachlan Chumbley et al.

ROT algorithm accelerates imitation learning with regularized optimal transport, achieving 7.8x faster to 90% expert performance.

Siddhant Haldar, Vaibhav Mathur, Denis Yarats et al.

Proposes a global planning framework combining local smoothing of contact models with RRT, achieving efficient contact-rich manipulation with less computation.

Tao Pang, H. J. Terry Suh, Lujie Yang et al.

SafeBench integrates 8 safety-critical scenarios and 4 generation algorithms for comprehensive autonomous vehicle safety evaluation.

Chejian Xu, Wenhao Ding, Weijie Lyu et al.

Proposes 'Adaptive MB' method that estimates residual mode to improve robustness and convergence in multivariate least-squares problems, outperforming traditional RLFs.

Thomas Hitchcox, James Richard Forbes

CALIPSO is a differentiable solver integrating interior-point and augmented Lagrangian methods, supporting second-order cone and complementarity constraints, enhancing robotics trajectory optimization.

Taylor A. Howell, Simon Le Cleac'h, Kevin Tracy et al.

Factory integrates SDF collision detection, contact reduction, and Gauss-Seidel solver to simulate 1000 contact-rich scenes in real-time.

Yashraj Narang, Kier Storey, Iretiayo Akinola et al.

RoboCraft uses graph networks to learn dynamics of elasto-plastic objects, achieving complex shape deformation with just 10 minutes of data.

Haochen Shi, Huazhe Xu, Zhiao Huang et al.

Integrating optimal transport-based contact point discovery (CPDeform) significantly improves multi-stage soft-body manipulation, overcoming local minima and enabling automatic contact switching.

Sizhe Li, Zhiao Huang, Tao Du et al.

Presented Google Scanned Objects, a dataset of 1030 high-quality 3D scanned household items, enhancing robotic simulation and perception tasks.

Laura Downs, Anthony Francis, Nate Koenig et al.

Proposes HULC, a hierarchical model with multimodal transformer and contrastive learning, achieving 85% success on long-horizon language-conditioned robot tasks, outperforming previous SOTA.

Oier Mees, Lukas Hermann, Wolfram Burgard

Pure tactile in-hand manipulation using torque-controlled hand trained with SAC in 600 CPU hours, achieving over 46 rotations.

Leon Sievers, Johannes Pitz, Berthold Bäuml

Proposes rhythmic precision-modulation active inference to enhance robotic perception-action coupling, improving state estimation and path planning.

Ajith Anil Meera, Filip Novicky, Thomas Parr et al.

Virtual binocular vision-based Tac3D sensor achieves real-time 3D contact deformation and force distribution measurement.

Lunwei Zhang, Yue Wang, Yao Jiang

Integrating classical and data-driven methods, this work proposes a multi-layered robotic grasping framework, significantly improving stability and adaptability.

Hanbo Zhang, Jian Tang, Shiguang Sun et al.

This survey categorizes autonomous driving scenario generation into data-driven, adversarial, and knowledge-based methods, highlighting five key challenges.

Wenhao Ding, Chejian Xu, Mansur Arief et al.

Active inference using variational Bayesian inference enhances robot state estimation and control robustness under uncertainty.

Pablo Lanillos, Cristian Meo, Corrado Pezzato et al.

Proposes a data-driven multi-agent simulation framework enabling zero-shot transfer of autonomous driving policies, validated on real vehicles with high success rates.

Tsun-Hsuan Wang, Alexander Amini, Wilko Schwarting et al.