# Humanoid Whole-Body Badminton via an Annealed Reinforcement Learning Curriculum

arXiv: https://arxiv.org/abs/2511.11218 · HTML: https://mlsanity.com/paper/2511.11218/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 24 of its 28 references that are in the map.
- First posted on arXiv 14 Nov 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 24 of 24)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning](https://mlsanity.com/paper/2109.11978/) (2021)
- [Asymmetric Actor Critic for Image-Based Robot Learning](https://mlsanity.com/paper/1710.06542/) (2017)
- [Real-World Humanoid Locomotion with Reinforcement Learning](https://mlsanity.com/paper/2303.03381/) (2023)
- [BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion](https://mlsanity.com/paper/2508.08241/) (2025)
- [Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotion](https://mlsanity.com/paper/2210.10044/) (2022)
- [Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control](https://mlsanity.com/paper/2401.16889/) (2024)
- [Humanoid Parkour Learning](https://mlsanity.com/paper/2406.10759/) (2024)
- [HOVER: Versatile Neural Whole-Body Controller for Humanoid Robots](https://mlsanity.com/paper/2410.21229/) (2024)
- [Humanoid Policy ~ Human Policy](https://mlsanity.com/paper/2503.13441/) (2025)
- [UMI on Legs: Making Manipulation Policies Mobile with Manipulation-Centric Whole-body Controllers](https://mlsanity.com/paper/2407.10353/) (2024)
- [Combining Learning-based Locomotion Policy with Model-based Manipulation for Legged Mobile Manipulators](https://mlsanity.com/paper/2201.03871/) (2022)
- [HITTER: A HumanoId Table TEnnis Robot via Hierarchical Planning and Learning](https://mlsanity.com/paper/2508.21043/) (2025)
- [Achieving Human Level Competitive Robot Table Tennis](https://mlsanity.com/paper/2408.03906/) (2024)
- [Learning coordinated badminton skills for legged manipulators](https://mlsanity.com/paper/2505.22974/) (2025)
- [Catch It! Learning to Catch in Flight with Mobile Dexterous Hands](https://mlsanity.com/paper/2409.10319/) (2024)
- [Robotic Table Tennis: A Case Study into a High Speed Learning System](https://mlsanity.com/paper/2309.03315/) (2023)
- [Athletic Mobile Manipulator System for Robotic Wheelchair Tennis](https://mlsanity.com/paper/2210.02517/) (2022)
- [Arm-Constrained Curriculum Learning for Loco-Manipulation of the Wheel-Legged Robot](https://mlsanity.com/paper/2403.16535/) (2024)
- [Integrating Learning-Based Manipulation and Physics-Based Locomotion for Whole-Body Badminton Robot Control](https://mlsanity.com/paper/2504.17771/) (2025)
- [Learning Accurate Whole-body Throwing with High-frequency Residual Policy and Pullback Tube Acceleration](https://mlsanity.com/paper/2506.16986/) (2025)
- [Efficient Learning of A Unified Policy For Whole-body Manipulation and Locomotion Skills](https://mlsanity.com/paper/2507.04229/) (2025)
- [Multi-critic Learning for Whole-body End-effector Twist Tracking](https://mlsanity.com/paper/2507.08656/) (2025)

## Built on by (most cited first, 3 of 3)

- [TaskNPoint: How to Teach Your Humanoid to Hit a Backhand in Minutes](https://mlsanity.com/paper/2606.26215/) (2026)
- [Play Like Champions: Counterfactual Feedback Generation in Latent Space](https://mlsanity.com/paper/2607.00190/) (2026)
- [Towards Professional Tennis Styles for Humanoid Robots with Adaptive Motion Planning and Tracking](https://mlsanity.com/paper/2608.20087/) (2026)

## Abstract (arXiv)

Humanoid robots have demonstrated strong capabilities for interacting with static scenes across locomotion and manipulation, yet dynamic real-world interactions remain challenging. As a step toward fast-moving object interactions, we present an RL training pipeline that yields a unified whole-body controller for humanoid badminton, coordinating footwork and striking without motion priors or expert demonstrations. In badminton, locomotion and striking are tightly coupled, making the final hitting objective difficult to optimize directly due to sparse rewards and conflicting gradients. We address this with an annealed curriculum that first stabilizes learning through auxiliary locomotion objectives, then progressively removes them to focus optimization on the final hitting objective. For deployment, we use an Extended Kalman Filter (EKF) to estimate and predict shuttlecock trajectories for target striking, and also develop a prediction-free variant that removes the EKF and explicit prediction. We validate the framework in simulation and on hardware. In simulation, two robots sustain a rally of 21 consecutive hits. In real-world tests with both machine-fed shuttles and human-robot rallies, the robot achieves outgoing shuttle speeds up to 19.1 m/s. Moreover, the prediction-free variant attains comparable performance to the EKF-based policy. Overall, our approach enables dynamic yet precise goal striking in humanoid badminton and suggests a path toward more dynamics-critical whole-body interaction tasks.