Laura Graesser
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Robotic Table Tennis: A Case Study into a High Speed Learning System
David B. D'Ambrosio
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
Pierre Sermanet
Avi Singh
Grace Vesom
Peng Xu
Robotics: Science and Systems (2023)
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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.
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GoalsEye: Learning High Speed Precision Table Tennis on a Physical Robot
Saminda Wishwajith Abeyruwan
David B. D'Ambrosio
Anish Shankar
Pierre Sermanet
Corey Harrison Lynch
International Conference on Intelligent Robots and Systems (IROS) (2022)
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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.
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The State of Sparse Training in Deep Reinforcement Learning
Erich Elsen
Proceedings of the 39th International Conference on Machine Learning, PMLR (2022)
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The use of sparse neural networks has seen a rapid growth in recent years, particularly in computer vision; their appeal stems largely due to the reduced number of parameters required to train and store, as well as in an increase in learning efficiency. Somewhat surprisingly, there have been very few efforts exploring their use in deep reinforcement learning (DRL). In this work we perform a systematic investigation into applying a number of existing sparse training techniques on a variety of DRL agents and environments. Our results highlight the overall challenge that reinforcement learning poses for sparse training methods, complemented by detailed analyses on how the various components in DRL are affected by the use of sparse networks. We conclude by suggesting some promising avenues for improving the effectiveness of general sparse training methods, as well as for advancing their use in DRL.
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i-Sim2Real: Reinforcement Learning of Robotic Policies in Tight Human-Robot Interaction Loops
Saminda Wishwajith Abeyruwan
David Bryan D'Ambrosio
Avi Singh
Anish Shankar
Conference on Robot Learning (Oral) (2022)
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Sim-to-real transfer is a powerful paradigm for robotic reinforcement learning. The ability to train policies in simulation enables safe exploration and large-scale data collection quickly at low cost. However, prior works in sim-to-real transfer of robotic policies typically do not involve any human-robot interaction because accurately simulating human behavior is an open problem. In this work, our goal is to leverage the power of simulation to train robotic policies that are proficient at interacting with humans upon deployment. This presents a chicken-and-egg problem --- how to gather examples of a human interacting with a physical robot so as to model human behavior in simulation without already having a robot that is able to interact with a human? Our proposed method, Iterative-Sim-to-Real i-S2R), attempts to address this. i-S2R bootstraps from a simple model of human behavior and alternates between training in simulation and deploying in the real world. In each iteration, both the human behavior model and the policy are improved. We evaluate our method on a real world robotic table tennis setting, where the objective for the robot is to play cooperatively with a human player for as long as possible. Table tennis is a high-speed, dynamic task that requires the two players to react quickly to each other’s moves, making for a challenging test bed for research on human-robot interaction. We present results on a physical industrial robotic arm that is able to cooperatively play table tennis against human players, achieving rallies of 22 successive hits on average and 150 at best. Further, for 80% of players, rally lengths are 70% to 175% longer compared to the sim-to-real (S2R) baseline.
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Robotic Table Tennis with Model-Free Reinforcement Learning
Wenbo Gao
Navdeep Jaitly
International Conference on Intelligent Robots and Systems (IROS) (2020)
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We propose a model-free algorithm for learning efficient policies capable of returning table tennis balls by controlling robot joints at a rate of 100Hz. We demonstrate that evolutionary search (ES) methods acting on CNN-based policy architectures for non-visual inputs and convolving across time learn compact controllers leading to smooth motions. Furthermore, we show that with appropriately tuned curriculum learning on the task and rewards, policies are capable of developing multi-modal styles, specifically forehand and backhand stroke, whilst achieving 80\% return rate on a wide range of ball throws. We observe that multi-modality does not require any architectural priors, such as multi-head architectures or hierarchical policies.
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