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Learning Whole-Body Kinetic Coordination for Wheeled-Quadruped Long-Range Throwing

Learning Whole-Body Kinetic Coordination for Wheeled-Quadruped Long-Range Throwing

Qinglian Wu, Yidian Hu, Weiqi Fan, Yutian Zhang, Siyuan Ma, Jiaqi Zhai, Wei Dong, Li Xu · N/A · 2026

locomotion reinforcement-learning
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Summary

Long-range throwing on unstructured terrain is a key capability for mobile robots in delivery and retrieval tasks, but it requires high release velocity, coordinated whole-body motion, and robustness to terrain-induced disturbances.

Abstract Summary

Long-range throwing on unstructured terrain is a key capability for mobile robots in delivery and retrieval tasks, but it requires high release velocity, coordinated whole-body motion, and robustness to terrain-induced disturbances. We present a whole-body kinetic-coordination framework for a wheeled-quadruped manipulator that combines kinetic-chain arm modeling with SQP-based trajectory optimization and a reference-guided reinforcement learning policy for coordinated base motion. The manipulator is first reduced to a kinetic-chain model and optimized under kinematic and dynamic constraints to produce a high-speed throwing reference. On a stationary base, the optimized arm trajectory reaches a release speed of 12.75 m/s and a throwing distance of 16.68 m. When whole-body coordination is enabled, the system further increases the release speed to 16.64 m/s and the throwing distance to 28.25 m. We then integrate the mobility of the wheeled-quadruped platform and validate the complete policy through hardware experiments on flat ground, grass, slopes, and rough roads. The results show that kinetic-chain optimization and whole-body coordination together enable robust long-range throwing across diverse unstructured terrains.

Key Points

  • Long-range throwing on unstructured terrain is a key capability for mobile robots in delivery and retrieval tasks, but it requires high release velocity, coordinated whole-body motion, and robustness to terrain-induced disturbances.
  • We present a whole-body kinetic-coordination framework for a wheeled-quadruped manipulator that combines kinetic-chain arm modeling with SQP-based trajectory optimization and a reference-guided reinforcement learning policy for coordinated base motion.
  • The manipulator is first reduced to a kinetic-chain model and optimized under kinematic and dynamic constraints to produce a high-speed throwing reference.
  • On a stationary base, the optimized arm trajectory reaches a release speed of 12.75 m/s and a throwing distance of 16.68 m.
  • When whole-body coordination is enabled, the system further increases the release speed to 16.64 m/s and the throwing distance to 28.25 m.

Method

As shown in Fig. 2 , the proposed framework addresses terrain-robust dynamic throwing in two stages: kinetic-chain arm optimization and whole-body RL control. We first model the inertial coupling among the active throwing joints and parameterize the motion with sinusoidal basis functions.

The trajectory parameters and release time are then optimized by SQP to maximize the weighted end-effector release velocity under physical constraints.

Method — Learning Whole-Body Kinetic Coordination for Wheeled-Quadruped Long-Range Throwing
Fig. 1: (a) High-speed throwing by the wheeled-quadruped robot with whole-body coordination. (b) Schematic of manipulator kinetic-chain energy transfer.

Experiments and Results

We evaluate the proposed framework from three aspects. First, we analyze the SQP-optimized throwing reference and the resulting proximal-to-distal kinetic energy transfer. Second, we conduct simulation experiments and ablation studies to validate the effectiveness of the kinetic-chain reference and whole-body coordination.

Third, we perform hardware experiments on different terrains to evaluate the robustness of the learned policy. Fig.

Experiments and Results — Learning Whole-Body Kinetic Coordination for Wheeled-Quadruped Long-Range Throwing
Fig. 2: Overview of the proposed control framework, which integrates arm trajectory optimization, whole-body reinforcement learning, and real-world deployment. The manipulator reference is optimized to enhance kinetic-chain energy transfer, while the wheeled-quadruped base is trained jointly in simulation with a history-based latent representation for terrain adaptation. The resulting policy enables terrain-robust long-range throwing and is evaluated across multiple real-world scenarios.

Sources and Demonstrations

Figures are reproduced from Wu et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.

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