Achieving Human Level Competitive Robot Table Tennis

The agent consists of a library of low-level skills and high-level controller that selects the most effective skill. Each low-level skill policy specializes in a specific aspect of table tennis, such as forehand topspin, backhand targeting, or forehand serve. In addition to training the policy itself, we collect and store information both offline and online about the strengths, weaknesses, and limitations of each low-level skill. The resulting skill descriptors provide the robot with important information regarding its abilities and shortcomings. In turn, a high-level controller, responsible for orchestrating the low-level skills, selects the optimal skill given the current game statistics, skill descriptors and the opponent's capabilities. We collect a small amount of human-human play data to seed the initial task conditions. We then train an agent in simulation using RL and employ a number of techniques (known and novel) to deploy the policy zero-shot to real hardware. This agent pl ... Read full article.