EMERGE-Policy employs a graph-structured multi-agent framework, coordinating specialized sub-agents and skills to achieve system-level robot policies without fine-tuning.

Zhirui Fang, Qingchi Yu, Ziyang Chen et al.

VeloBins learns velocity and uncertainty via bin classification, reducing errors by 3-53% across four datasets.

Maulana Bisyir Azhari, Seungwook Lee, Donghun Han et al.

Developed Aero Hand Open with a MuJoCo-based tendon-driven simulation model enabling zero-shot transfer of reinforcement learning policies for dexterous grasping.

Nan Wang, Mohit Yadav, Jonathan Wulff et al.

ChainSplat uses screw theory to jointly learn 3D geometry and dynamics of deformable linear objects from multi-view RGB videos, enabling accurate prediction and high-fidelity rendering.

Seungyeon Kim, Noémie Jaquier

Proposes a game-theoretic, ecology-inspired cooperative exploration framework with altruistic utility shaping, using relatedness weights to optimize heterogeneous multi-robot teams.

Brooks A. Butler, Jair Certório, João P. Hespanha et al.

MaCoPlanner uses knowledge compilation and proactive safety verification, boosting task success to 84.4% with a 2.7% violation rate.

Guipeng Xin, Jiahe Xua, Mohammad Deghat et al.

Proposes STEGNav, a spatio-temporal event graph framework, boosting success rate to 66.3% on GOAT-Bench.

Yang Chen, Zhenyu Huang, Wenbo Fu et al.

Proposes ESRP task with egocentric observations and top-down layouts; introduces ESRP-Bench with 5400+ scene pairs; baseline success only 30.2%.

Canzhi Chen, Zan Wang, Siqi Zhu et al.

RA-VLA integrates behavior-aligned retrieval with grounded execution, enabling training-free robotic adaptation with 17.6% success rate improvement on unseen tasks.

Sanghwan Jang, Minjin Jeon, Minsoo Kim et al.

LAWA encodes future intentions as compact latent actions, reducing inference latency by 42.9%, achieving 80.8% success on RoboCasa with superior generalization.

Xiang Li, Yupeng Zheng, Songen Gu et al.

Proposes a contact-rich robot learning method using explicit physical interaction recognition, enabling robust one-shot task reproduction.

A. H. G. Overbeek, H. van der Kooij, M. Vlutters

GaussianWAM uses 3D Gaussian fields to enhance WAM's geometric and semantic representations, boosting success rates from 52.05% to 71.29%.

Zijian Zhang, Yuqing Jiang, Weitao Zhou et al.

Proposes GVLA, integrating multi-gripper encoding and adapter routing, trained on MiGA dataset with 103,000 demonstrations, achieving 7.62% improvement over baselines.

Hanyi Zhang, Zihong Luo, Tianyu Li et al.

Proposes INDI, a method that distills behavior-level intent into vision-language-action models, boosting success rates by over 20% on benchmarks.

Sangoh Lee, Sangwoo Mo, Wook-Shin Han

Reward-free continual learning with DreamerV3-based latent world models enables space robots to adapt after hardware failures without external rewards.

Andrej Orsula, Miguel Olivares-Mendez, Carol Martinez

This work adapts Hugging Face's SmolVLA for industrial lightweight robots (UR10e), enabling edge-based real-time vision-language-action control.

Nils Mandischer, Noah Böckmann, Ludwig Holl et al.

Biomimetic layered skin with sensor integration achieves ~3° joint angle estimation; multimodal fusion improves accuracy.

Akihiro Miki, Shun Hasegawa, Yoshimoto Ribayashi et al.

Introduced Physical Agentic AI framework to coordinate heterogeneous robot teams using LLMs, reducing error dispatch rates from 23%-29% to 0%.

Xinyuan Liu, Eren Sadikoglu, Riana Chatterjee et al.

BehaviorWorldGen uses BehaviorFlow to generate controllable interactive rollouts; gains are reported, but exact benchmark percentages are absent.

Jiaqi Wang, Zhuo Zhang, Haining Guan et al.

ViTacPhys uses multimodal visual-tactile modeling and VLM priors to predict object mass, stiffness, and friction with 97.2% accuracy, enabling adaptive robotic grasping.

Yiwen Liu, Yujun Zhu, Kui Jia et al.