DynaMOMA: Instantaneous Prediction of Grasp Poses for Mobile Manipulation of Dynamic Objects

2026-06-23

AI Summary

This work proposes a dynamic mobile manipulation framework to address the challenges of coordinating a mobile base with an arm and generating temporally consistent grasping trajectories in real time when interacting with dynamic objects. The framework innovatively couples an anchor-based diffusion model with a whole-body reinforcement learning policy: the diffusion model predicts instantaneous grasp trajectories and encodes them into compact features, while the reinforcement learning policy leverages a forward-looking reward mechanism to anticipate dynamic targets and coordinate precise grasps. Evaluated in Isaac Gym simulations across diverse dynamic scenarios and multiple grasping metrics, the system demonstrates strong generalization capabilities and successfully transfers to real-world robotic platforms.

Abstract

Mobile manipulation is a fundamental robotics task and has advanced rapidly in recent years, enabling robots to navigate, reach, and interact with objects in complex environments. However, mobile manipulation of dynamic objects remains highly challenging, as robots must coordinate the mobile base and arm while adapting to continuously evolving target poses. A key challenge lies in predicting temporally consistent short-horizon grasp trajectories from dynamic observations. In this work, we propose \ours{}, a dynamic mobile manipulation framework that couples instantaneous grasp trajectory prediction with whole-body control policy. Our predictor uses an anchor-based diffusion model to generate temporally consistent short-horizon grasp trajectories conditioned on historical observations. The predicted trajectories are then encoded as compact features and fed to a whole-body reinforcement learning policy, which controls the mobile manipulator for dynamic grasping. We further introduce a anticipation-guided reward that equips the policy with an anticipatory grasping horizon by adaptively shifting the target from the current grasp observation to the instantaneously predicted grasp trajectory. Through extensive experiments in Isaac Gym simulation, we show that our method achieves strong performance in mobile manipulation of dynamic objects across diverse settings and grasping metrics. Furthermore, our predictor and policy demonstrate strong generalizability in real-world experiments.

Problem

• mobile manipulation
• dynamic objects
• grasp trajectory prediction
• temporal consistency
• whole-body control

Innovation

• diffusion model
• mobile manipulation
• dynamic object grasping
• whole-body control
• anticipatory planning