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Tianli Ding

Tianli Ding

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    Robotic Table Tennis: A Case Study into a High Speed Learning System
    Jon Abelian
    Saminda Abeyruwan
    Michael Ahn
    Justin Boyd
    Erwin Johan Coumans
    Omar Escareno
    Wenbo Gao
    Navdeep Jaitly
    Juhana Kangaspunta
    Satoshi Kataoka
    Gus Kouretas
    Yuheng Kuang
    Corey Lynch
    Thinh Nguyen
    Ken Oslund
    Barney J. Reed
    Anish Shankar
    Avi Singh
    Grace Vesom
    Peng Xu
    Robotics: Science and Systems (2023)
    Preview abstract We present a deep-dive into a learning robotic 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 and novel 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 ablation studies that clarify the importance of mitigating various sources of latency, accounting for training and deployment distribution shifts, robustness of the perception system, and sensitivity to policy hyper-parameters and choice of action space. A video demonstrating the components of our system and details of experimental results is included in the supplementary material. View details
    GoalsEye: Learning High Speed Precision Table Tennis on a Physical Robot
    Saminda Wishwajith Abeyruwan
    Anish Shankar
    Corey Harrison Lynch
    International Conference on Intelligent Robots and Systems (IROS) (2022)
    Preview abstract Learning goal conditioned control in the real world is a challenging open problem in robotics. Reinforcement learning systems have the potential to learn autonomously via trial-and-error, but in practice the costs of manual reward design, ensuring safe exploration, and hyperparameter tuning are often enough to preclude real world deployment. Imitation learning approaches, on the other hand, offer a simple way to learn control in the real world, but typically require costly curated demonstration data and lack a mechanism for continuous improvement. Recently, iterative imitation techniques have been shown to learn goal directed control from undirected demonstration data, and improve continuously via self-supervised goal reaching, but results thus far have been limited to simulated environments. In this work, we present evidence that iterative imitation learning can scale to goal-directed behavior on a real robot in a dynamic setting: high speed, precision table tennis (e.g. "land the ball on this particular target"). We find that this approach offers a straightforward way to do continuous on-robot learning, without complexities such as reward design or sim-to-real transfer. It is also scalable -- sample efficient enough to train on a physical robot in just a few hours. In real world evaluations, we find that the resulting policy can perform on par or better than amateur humans (with players sampled randomly from a robotics lab) at the task of returning the ball to specific targets on the table. Finally, we analyze the effect of an initial undirected bootstrap dataset size on performance, finding that a modest amount of unstructured demonstration data provided up-front drastically speeds up the convergence of a general purpose goal-reaching policy. View details
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