Robotic Table Tennis: A Case Study into a High Speed Learning System
This addresses the problem of real-world robotic control in dynamic, high-speed environments for robotics researchers, though it is incremental as it builds on previous work with detailed system analysis.
The paper tackles the challenge of building a high-speed robotic learning system for table tennis, achieving hundreds of rallies with a human and precise ball returns to targets by integrating optimized perception, low-latency control, simulation for safe training, and automated resets.
We present a deep-dive into a real-world robotic learning system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the ability to precisely return the ball to desired targets. This system puts together a highly optimized perception subsystem, a high-speed low-latency robot controller, a simulation paradigm that can prevent damage in the real world and also train policies for zero-shot transfer, and automated real world environment resets that enable autonomous training and evaluation on physical robots. We complement a complete system description, including numerous design decisions that are typically not widely disseminated, with a collection of studies that clarify the importance of mitigating various sources of latency, accounting for training and deployment distribution shifts, robustness of the perception system, sensitivity to policy hyper-parameters, and choice of action space. A video demonstrating the components of the system and details of experimental results can be found at https://youtu.be/uFcnWjB42I0.