DribbleBot: Dynamic Legged Manipulation in the Wild

Yandong Ji*     Gabriel B. Margolis*     Pulkit Agrawal

International Conference on Robotics and Automation (ICRA) 2023

MIT Improbable AI


Coverage: MIT News | IEEE Spectrum | TechCrunch


Abstract

DribbleBot (Dexterous Ball Manipulation with a Legged Robot) is a legged robotic system that can dribble a soccer ball under the same real-world conditions as humans (i.e., in-the-wild). We adopt the paradigm of training policies in simulation using reinforcement learning and transferring them into the real world. We overcome critical challenges of accounting for variable ball motion dynamics on different terrains and perceiving the ball using body-mounted cameras under the constraints of onboard computing. Our results provide evidence that current quadruped platforms are well-suited for studying dynamic whole-body control problems involving simultaneous locomotion and manipulation directly from sensory observations.


Paper

DribbleBot: Dynamic Legged Manipulation in the Wild
Yandong Ji*, Gabriel B. Margolis*, Pulkit Agrawal
International Conference on Robotics and Automation (ICRA), 2023
paper / project page / code / bibtex


Real World Highlights




Overview

Measures to Mitigate the Sim-to-Real Gap
Sim-to-Real Measures


Integrated System Architecture
System Architecture


Task Specification




In-the-Wild Evaluation and Ablations




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Conference on Robot Learning (CoRL), 2022 (Oral Presentation)
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Yandong Ji*, Zhongyu Li*, Yinan Sun, Xue Bin Peng, Sergey Levine, Glen Berseth, Koushil Sreenath
International Conference on Intelligent Robots and Systems (IROS), 2022 (Best RoboCup Paper Award Finalist)
paper / project page / bibtex
 
Rapid Locomotion via Reinforcement Learning
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Robotics: Science and Systems (RSS), 2022
paper / project page / code / bibtex


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