# Learning Accurate Whole-body Throwing with High-frequency Residual Policy and Pullback Tube Acceleration

arXiv: https://arxiv.org/abs/2506.16986 · HTML: https://mlsanity.com/paper/2506.16986/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2025.

## Built on by (most cited first, 4 of 4)

- [Humanoid Goalkeeper: Learning from Position Conditioned Task-Motion Constraints](https://mlsanity.com/paper/2510.18002/) (2025)
- [Humanoid Whole-Body Badminton via an Annealed Reinforcement Learning Curriculum](https://mlsanity.com/paper/2511.11218/) (2025)
- [Learning to Throw-Flip](https://mlsanity.com/paper/2510.10357/) (2025)
- [Generation of Custom Solvers in Rust for Convex Optimization](https://mlsanity.com/paper/2609.13875/) (2026)

## Abstract (arXiv)

Throwing is a fundamental skill that enables robots to manipulate objects in ways that extend beyond the reach of their arms. We present a control framework that combines learning and model-based control for prehensile whole-body throwing with legged mobile manipulators. Our framework consists of three components: a nominal tracking policy for the end-effector, a high-frequency residual policy to enhance tracking accuracy, and an optimization-based module to improve end-effector acceleration control. The proposed controller achieved the average of 0.28 m landing error when throwing at targets located 6 m away. Furthermore, in a comparative study with university students, the system achieved a velocity tracking error of 0.398 m/s and a success rate of 56.8%, hitting small targets randomly placed at distances of 3-5 m while throwing at a specified speed of 6 m/s. In contrast, humans have a success rate of only 15.2%. This work provides an early demonstration of prehensile throwing with quantified accuracy on hardware, contributing to progress in dynamic whole-body manipulation.