---
title: "AdaPT: Towards Professional Tennis Styles for Humanoid Robots with Adaptive Motion Planning and Tracking"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.20087v1"
permalink: "https://feed.astralor.com/items/itm_d209575210cd712739709c99"
published: "2026-08-20T14:19:36.000Z"
score: 70
channel: "tech"
tags: ["AdaPT","Humanoid Robots","Sim-to-Real Transfer","Unitree","Adaptive Motion Planning","Dobot"]
---

# AdaPT: Towards Professional Tennis Styles for Humanoid Robots with Adaptive Motion Planning and Tracking

## AI commentary

> What makes this paper worth reading is that it does not merely chase fancy motions in simulation but seriously addresses the snowballing errors that complex styles introduce on real robots. For teams working on robot control and sim2real, AdaPT's hierarchical thinking and speed-adapter design offer a directly useful reference; for those watching embodied AI products, the real-machine demos on Unitree and Dobot show this technology is closer to practical use than one might expect.

## At a glance

Humanoid robots have shown promising capabilities in real-world ball sports, but achieving professional motion styles while maintaining strong task performance remains challenging. This paper introduces AdaPT, an Adaptive Motion Planning and Tracking framework that learns professional tennis serving and rally styles directly from broadcast videos. The hierarchical design lets a planner generate stylistic kinematic motions while a tracker executes them with minimal interference. To address the sim-to-real gap, the adaptation mechanism learns to track randomized execution speeds and conditions the planner on a learned motion-speed adapter to mitigate compounding errors. Real-world experiments on the Unitree G1 and the full-size Dobot Atom humanoid robot demonstrate the effectiveness of the approach and provide insights for future humanoid ball-sports systems.

## Key points

- AdaPT uses a hierarchical design where a planner generates stylistic kinematic motions and a tracker executes them with minimal interference with planning goals.
- An adaptation mechanism trains the tracker with randomized execution speeds and conditions the planner on a learned motion-speed adapter to reduce compounding errors.
- Real-world validation on Unitree G1 and the 1.7m full-size Dobot Atom humanoid robot achieves in-the-wild serving without motion capture.

## Full text

Humanoid robots have recently demonstrated promising capabilities in real-world ball sports. However, achieving professional motion styles while maintaining strong task performance remains challenging. In this work, we propose AdaPT, an Adaptive Motion Planning and Tracking framework that learns professional tennis serving and rally styles directly from broadcast videos. This hierarchical design is motivated by the key insight that the planner generates stylistic kinematic motions, while the tracker executes them with minimal interference with planning. Despite its effectiveness in simulation, a substantial sim-to-real gap emerges: tracking performance inevitably degrades on real robots, and this degradation is partially overlooked by autoregressive planning and further compounded by noisy perception. To address these issues, our adaptation mechanism improves tracking robustness by learning to track randomized execution speeds, while conditioning the planner on a learned motion-speed adapter to mitigate compounding errors. Real-world experiments on the Unitree G1 demonstrate the effectiveness of our adaptation mechanism in bridging the sim-to-real gap. We further deploy AdaPT policies on the full-size Dobot Atom humanoid robot (1.7m) and demonstrate in-the-wild serving without motion capture. Beyond these results, our real-world experiments reveal both algorithmic and engineering insights for future humanoid ball-sports systems. Videos and code are available on our \href{https://humanoidtennis.github.io/AdaPT/}{project website}.

---

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