Overview of the Robot Table Tennis Agent
- The research presents a robot agent capable of playing competitive table tennis at an amateur human level.
- The robot achieved a 45% win rate against human opponents of varying skill levels in 29 matches.
- Key contributions include a hierarchical policy architecture, techniques for zero-shot sim-to-real transfer, and real-time adaptation to opponents.
Contributions of the Study
- The architecture consists of low-level controllers (LLCs) for specific skills (e.g., forehand topspin, backhand targeting) and a high-level controller (HLC) that selects the appropriate LLC based on game context.
- Each LLC is trained to handle different aspects of table tennis, allowing for efficient skill specialization and adaptation.
- Hierarchical and Modular Policy Architecture Low-level controllers with skill descriptors to model agent capabilities. High-level controller for selecting low-level skills.
- Zero-shot Sim-to-Real Techniques Iterative task distribution approach grounded in real-world scenarios. Automatic curriculum definition for training.
- Real-Time Adaptation Ability to adapt to unseen opponents during matches.
- A hybrid training approach combines reinforcement learning (RL) and imitation learning (IL) to develop the robot's skills.
- Initial training data is collected from human-human play, which is then used to create a realistic task distribution for simulation training.
- The robot iteratively refines its skills through a cycle of simulated training and real-world evaluation, enhancing its performance over time.
Real-Time Adaptation and Performance Assessment
- The robot adapts to unseen human opponents by tracking match statistics and adjusting its strategy in real-time.
- Conducted 29 matches between the robot and human players.
- Robot won 45% of the matches (13 out of 29).
- Performance breakdown: 100% win rate against beginner players. 55% win rate against intermediate players. Lost all matches against advanced players.
- Participants reported a positive experience, finding the robot engaging and fun to play against.
?Limitations and Future Work
- The robot struggles with fast balls, low balls, and high spins, indicating areas for improvement in its capabilities.