Proposed an integrated path planning framework with spatiotemporal prediction, improving target tracking accuracy by 25% in dynamic environments.

Sanjeev Ramkumar Sudha, Marija Popović, Erlend M. Coates

Contact surface representation enabling closed-form proximity computation accelerates whole-body manipulation planning by 77%.

Victor Levé, João Moura, Sachiya Fujita et al.

SwarmVLM integrates VLM and impedance control for heterogeneous robots, achieving 92% success in dynamic warehouse navigation.

Malaika Zafar, Roohan Ahmed Khan, Faryal Batool et al.

D3P: A reinforcement learning-based adaptive diffusion policy accelerates robotic inference by 2.2× without performance loss, through dynamic denoising step allocation.

Shu-Ang Yu, Feng Gao, Yi Wu et al.

S-Path exploits hierarchical 3D scene graphs with semantic path decomposition, achieving 6× faster planning while maintaining path optimality.

Saad Ejaz, Marco Giberna, Muhammad Shaheer et al.

Introduces RTI-inspired scheme for diffusion policy inference, reducing runtime by over 85% while maintaining performance.

Yufei Duan, Hang Yin, Danica Kragic

Proposes OSG Navigator, a structure-based open-world ObjectNav system leveraging foundation models and open scene graphs for zero-shot generalization.

Joel Loo, Zhanxin Wu, David Hsu

Proposed a failure-aware multi-robot target tracking framework combining partial centralized and decentralized optimization, enhancing robustness under various failure scenarios.

Peihan Li, Jiazhen Liu, Yuwei Wu et al.

Diffusion Policy enables 79.3% success in multi-object prosthetic grasping via imitation learning.

Carlo Alessi, Federico Vasile, Federico Ceola et al.

XRoboToolkit is a cross-platform XR-based robot teleoperation framework with low-latency stereoscopic feedback and optimized inverse kinematics.

Zhigen Zhao, Liuchuan Yu, Ke Jing et al.

LAN2CB leverages large language models to convert natural language instructions into executable robot control code, reducing manual effort and enhancing flexibility.

Zhehui Huang, Guangyao Shi, Yuwei Wu et al.

Inter-LLM integrates hierarchical scene graph reasoning with real-time motion cost feedback, achieving 30% success rate improvement in large-scale multi-object collection tasks.

Ruochu Yang, Yu Zhou, Fumin Zhang et al.

GraspGen, a diffusion transformer with on-generator discriminator, achieves 94.7% AUC in 6-DOF grasping, outperforming prior methods.

Adithyavairavan Murali, Balakumar Sundaralingam, Yu-Wei Chao et al.

RCG framework integrates real-world crash semantics into adversarial scenario generation, boosting safety and realism in autonomous driving testing.

Benjamin Stoler, Juliet Yang, Jonathan Francis et al.

Extending Diffusion Policy for multi-task robot models (LBMs), showing pretraining boosts success and robustness.

TRI LBM Team, Jose Barreiros, Andrew Beaulieu et al.

RoboTwin dual-arm challenge, using SEM and AnchorDP3, achieved over 97% success in simulation.

Tianxing Chen, Kaixuan Wang, Zhaohui Yang et al.

This study introduces a post-training framework for VLA models based on human motor learning constraints, improving perception, embodiment, and task understanding, with 15% success rate gains.

Tian-Yu Xiang, Ao-Qun Jin, Xiao-Hu Zhou et al.

Dex1B uses generative models to create 1 billion demonstrations for dexterous manipulation, enhancing diversity and quality in grasping and articulation tasks.

Jianglong Ye, Keyi Wang, Chengjing Yuan et al.

CR-DAgger combines compliant intervention and residual policy learning to improve contact-rich manipulation success by 64% with minimal human corrections.

Xiaomeng Xu, Yifan Hou, Chendong Xin et al.

Introduces Diffusion Steering via Reinforcement Learning (DSRL), enabling sample-efficient online adaptation of diffusion-based robotic policies.

Andrew Wagenmaker, Mitsuhiko Nakamoto, Yunchu Zhang et al.