AdaPT: Towards Professional Tennis Styles for Humanoid Robots with Adaptive Motion Planning and Tracking
AdaPT jointly models motion planning and tracking with explicit speed adaptation, bringing professional tennis styles to a Unitree G1 humanoid for both rally and serving.
1 Noitom Robotics 2 Shanghai AI Laboratory 3 Dobot Robotics 4 Shanghai Jiao Tong University * Equal Contribution † Equal Advising ‡ Project Lead We propose AdaPT, an Adaptive motion Planning and Tracking framework that learns professional tennis serve and rally styles. Tennis Motions from Video & MoCap · Rally on G1 & Atom P3 · In-the-wild Serve CoRL 2026 Overview Tennis Rally Rally hitting styles from different players, deployed on Unitree G1 and Atom P3. Player Robot Tennis Serve Tennis serving styles from multiple players, validated in both Mocap systems and in-the-wild…
saved by
related reading
- Introducing Reinforced Planning (RP-1)pantheon.inc
- ServeSense - Sense Your Gameservesense.ai
- Humanoid Atlas | Humanoid Robot Supply Chain Map, OEM Database & Industry Analysishumanoids.fyi
- UniTracker: Learning Universal Whole-Body Motion Tracker for Humanoid Robotsarxiv.org
- ACT-1: A Robot Foundation Model Trained on Zero Robot Data | Sunday Robotics | The helpful robotics companysunday.ai
- GitHub - adam-maj/robotics: A deep dive on the history of robotics and the future of humanoidsgithub.com
- A Unified Model for Motion-Conditioned Robot Co-designtransformer-transformer.github.io
- How Claude Performs on Robotics Tasks \ Anthropicanthropic.com
- Neural MP: A Generalist Neural Motion Plannerarxiv.org
- how we accidentally solved robotics by watching 1 million hours of YouTube – atharva's blogksagar.bearblog.dev
- BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusionarxiv.org
- Precise Manipulation with Efficient Online RLpi.website