# Catch It! Learning to Catch in Flight with Mobile Dexterous Hands

arXiv: https://arxiv.org/abs/2409.10319 · HTML: https://mlsanity.com/paper/2409.10319/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2024.

## Built on by (most cited first, 16 of 16)

- [ArticuBot: Learning Universal Articulated Object Manipulation Policy via Large Scale Simulation](https://mlsanity.com/paper/2503.03045/) (2025)
- [Humanoid Goalkeeper: Learning from Position Conditioned Task-Motion Constraints](https://mlsanity.com/paper/2510.18002/) (2025)
- [MomaGraph: State-Aware Unified Scene Graphs with Vision-Language Model for Embodied Task Planning](https://mlsanity.com/paper/2512.16909/) (2025)
- [Versatile Loco-Manipulation through Flexible Interlimb Coordination](https://mlsanity.com/paper/2506.07876/) (2025)
- [ViTaS: Visual Tactile Soft Fusion Contrastive Learning for Visuomotor Learning](https://mlsanity.com/paper/2602.11643/) (2026)
- [AugVLA-3D: Depth-Driven Feature Augmentation for Vision-Language-Action Models](https://mlsanity.com/paper/2602.10698/) (2026)
- [Humanoid Whole-Body Badminton via an Annealed Reinforcement Learning Curriculum](https://mlsanity.com/paper/2511.11218/) (2025)
- [Closed-Loop Action Chunks with Dynamic Corrections for Training-Free Diffusion Policy](https://mlsanity.com/paper/2603.01953/) (2026)
- [DynamicManip: Enabling Dynamic Manipulation from a Single Static Demonstration](https://mlsanity.com/paper/2608.01452/) (2026)
- [HAFO: A Force-Adaptive Control Framework for Humanoid Robots in Intense Interaction Environments](https://mlsanity.com/paper/2511.20275/) (2025)
- [TEMPO: Learning Temporal Context for Dynamic Robot Manipulation](https://mlsanity.com/paper/2609.16864/) (2026)
- [SpikePingpong: Spike Vision-based Fast-Slow Pingpong Robot System](https://mlsanity.com/paper/2506.06690/) (2025)
- [FastGrasp: Learning-based Whole-Body Control Method for Fast Dexterous Grasping with Mobile Manipulators](https://mlsanity.com/paper/2604.12879/) (2026)
- [Spatiotemporal Agility: Time-Constrained Reinforcement Learning for Vision-Guided Dynamic Quadrupedal Interception](https://mlsanity.com/paper/2608.06907/) (2026)
- [DyG2T: Modeling Object Dynamics with 3D Gaussian Temporal-Spatial Particle Graph Transformer](https://mlsanity.com/paper/2608.18498/) (2026)
- [Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance](https://mlsanity.com/paper/2610.20607/) (2026)

## Abstract (arXiv)

Catching objects in flight (i.e., thrown objects) is a common daily skill for humans, yet it presents a significant challenge for robots. This task requires a robot with agile and accurate motion, a large spatial workspace, and the ability to interact with diverse objects. In this paper, we build a mobile manipulator composed of a mobile base, a 6-DoF arm, and a 12-DoF dexterous hand to tackle such a challenging task. We propose a two-stage reinforcement learning framework to efficiently train a whole-body-control catching policy for this high-DoF system in simulation. The objects' throwing configurations, shapes, and sizes are randomized during training to enhance policy adaptivity to various trajectories and object characteristics in flight. The results show that our trained policy catches diverse objects with randomly thrown trajectories, at a high success rate of about 80% in simulation, with a significant improvement over the baselines. The policy trained in simulation can be directly deployed in the real world with onboard sensing and computation, which achieves catching sandbags in various shapes, randomly thrown by humans. Our project page is available at https://mobile-dex-catch.github.io/.