Whole-Body Coordination for Dynamic Object Grasping with Legged Manipulators

Abstract

We introduce DQ-Bench, the first benchmark for dynamic object grasping with quadruped robots, supporting realistic dynamics, diverse objects, multi-level task difficulty, and comprehensive evaluation. Building upon this benchmark, we propose DQ-Net, a teacher–student framework combining a Grasp Fusion Module and lightweight dual-view student network for stable and efficient whole-body dynamic grasping. Extensive experiments show DQ-Net outperforms baselines in both success rate and responsiveness.

DQ-Bench

• Built on Isaac Gym for high-performance simulation.
• Four difficulty levels: Low-Speed 2D → High-Speed 3D with rough terrain.
• Diverse seen/unseen YCB objects for generalization testing.
• Evaluation metrics: GSR, OSSR, TSC.

DQ-Net Framework

DQ-Net integrates a Grasp Fusion Module (GFM) with a hierarchical teacher–student structure:

1. Teacher: Uses privileged info (pose, velocity, point cloud) + GFM to output optimal grasp poses.
2. Student: Lightweight Transformer-based network with dual-perspective depth & mask inputs.
3. Low-level controller ensures coordinated locomotion and manipulation.

Results

DQ-Net achieves the highest grasp success rates across all difficulty levels and unseen object categories.

Video

Citation

@misc{liang2025wholebodycoordinationdynamicobject,
      title={Whole-Body Coordination for Dynamic Object Grasping with Legged Manipulators}, 
      author={Qiwei Liang and Boyang Cai and Rongyi He and Hui Li and Tao Teng and Haihan Duan and Changxin Huang and Runhao Zeng},
      year={2025},
      eprint={2508.08328},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2508.08328}, 
}