Strategy and Skill Learning for Physics-based Table Tennis Animation (2407.16210v1)

Abstract: Recent advancements in physics-based character animation leverage deep learning to generate agile and natural motion, enabling characters to execute movements such as backflips, boxing, and tennis. However, reproducing the selection and use of diverse motor skills in dynamic environments to solve complex tasks, as humans do, still remains a challenge. We present a strategy and skill learning approach for physics-based table tennis animation. Our method addresses the issue of mode collapse, where the characters do not fully utilize the motor skills they need to perform to execute complex tasks. More specifically, we demonstrate a hierarchical control system for diversified skill learning and a strategy learning framework for effective decision-making. We showcase the efficacy of our method through comparative analysis with state-of-the-art methods, demonstrating its capabilities in executing various skills for table tennis. Our strategy learning framework is validated through both agent-agent interaction and human-agent interaction in Virtual Reality, handling both competitive and cooperative tasks.

• The paper presents a hierarchical control system that separates strategic decision-making from skill execution to enhance motion fidelity and address mode collapse.
• Deep learning augmented with staged imitation training enables precise replication of five distinct table tennis skills and smooth action transitions.
• Evaluations demonstrate superior performance over baselines with higher Discriminator Scores, Skill Accuracy, and enhanced diversity of motion.

Strategy and Skill Learning for Physics-Based Table Tennis Animation

The integration of deep learning with physics-based character animation has heralded a new era of generating agile and lifelike motion for digital characters. However, mimicking the diverse motor skills and decision-making capabilities humans exhibit in dynamic sports such as table tennis remains challenging. The paper, authored by Jiashun Wang, Jessica Hodgins, and Jungdam Won, presents an advanced hierarchical control system for physics-based table tennis animation, aiming to address the prevalent issues of mode collapse and insufficient skill utilization in such scenarios.

Methodological Framework

The authors propose a robust hierarchical control system divided into a skill-level controller and a strategy-level controller. This bifurcated approach allows for the efficient management of both motion generation and strategic decision-making.

Skill-Level Controller

The skill-level controller is designed to ensure the agent can perform a variety of table tennis skills with agility and precision. The training process for this controller is divided into three stages:

1. Imitation Policy Training: Utilizing motion capture data, the authors train distinct imitation policies for five categorized table tennis skills (forehand drive, push, smash, and backhand drive and push), alongside a universal imitation policy. These policies enable the accurate reproduction of human-like motions.
2. Ball Control Policy: Each skill-specific imitation policy is further refined to handle ball control tasks. Rewards are crafted based on the paddle-ball proximity and the accuracy of directing the ball to a target location, thus ensuring precise ball handling during gameplay.
3. Mixer Policy: To facilitate seamless transitions between different skills, a mixer policy is introduced. This policy blends skill actions in a joint-wise manner, effectively addressing limitations and ensuring fluid transitions between skills during dynamic gameplay.

Strategy-Level Controller

The strategy-level controller is responsible for making high-level decisions such as selecting appropriate skills and target ball landing locations based on the states of the agent, the opponent, and the ball. The controller is trained using an iterative behavior cloning approach, derived from competitive and cooperative interaction scenarios in both agent-agent and human-agent environments.

Experimental Results and Evaluation

The paper meticulously evaluates both the skill controller and strategy controller against several benchmarks, highlighting the efficacy of their approach in enhancing motion quality and gameplay performance.

Motion Quality Assessment

Motion quality is gauged using three metrics: Discriminator Score, Skill Accuracy, and Diversity Score. Compared to baseline methods such as ASE, CASE, and explicit transition models, the proposed method demonstrates superior performance. The authors report a Discriminator Score of 5.72, significantly higher than other methods, indicating a high fidelity to reference motions. Additionally, the Skill Accuracy of 0.76 and Diversity Score of 8.01 reflect robust skill identification and the diverse execution of subtler movements like drive and push.

Task Performance

Task performance is evaluated through sustainability (average number of successful returns) and accuracy (distance to target landing locations). The results exhibit that their method sustains longer rallies and maintains high accuracy even in challenging scenarios. Furthermore, visualization of skill distribution and target landing locations underscore the algorithm's closer alignment to human gameplay dynamics.

Agent-Agent and Human-Agent Interactions

Through extensive evaluations in agent-agent interaction environments, the proposed strategy-level controller achieves a higher win rate and longer rally averages compared to strategies derived from reinforcement learning baselines. The human-agent interaction scenarios demonstrate adaptive learning, with the agent showing improved competitiveness after iterative strategy refinements.

Implications and Future Directions

The research introduces significant improvements in physics-based character animation, particularly in the domain of interactive sports simulations. By addressing the key issues of mode collapse and skill diversity, the paper paves the way for more realistic and competitive digital sports agents.

Future research could extend this framework to accommodate larger datasets with numerous skills, potentially combining supervised learning with unsupervised techniques to enhance scalability. Additionally, fine-tuning the simulation's physical accuracy, especially regarding the ball's interaction with environmental factors such as air resistance, could further enhance realism.

In conclusion, the hierarchical approach proposed by Wang, Hodgins, and Won represents a notable advancement in physics-based table tennis animation, demonstrating practical applicability in both autonomous agent interaction and real-time human-agent interfaces. This methodology holds significant promise for future exploration within the broader field of AI-driven sports simulations.