From Motor Control to Team Play in Simulated Humanoid Football (2105.12196v1)

Abstract: Intelligent behaviour in the physical world exhibits structure at multiple spatial and temporal scales. Although movements are ultimately executed at the level of instantaneous muscle tensions or joint torques, they must be selected to serve goals defined on much longer timescales, and in terms of relations that extend far beyond the body itself, ultimately involving coordination with other agents. Recent research in artificial intelligence has shown the promise of learning-based approaches to the respective problems of complex movement, longer-term planning and multi-agent coordination. However, there is limited research aimed at their integration. We study this problem by training teams of physically simulated humanoid avatars to play football in a realistic virtual environment. We develop a method that combines imitation learning, single- and multi-agent reinforcement learning and population-based training, and makes use of transferable representations of behaviour for decision making at different levels of abstraction. In a sequence of stages, players first learn to control a fully articulated body to perform realistic, human-like movements such as running and turning; they then acquire mid-level football skills such as dribbling and shooting; finally, they develop awareness of others and play as a team, bridging the gap between low-level motor control at a timescale of milliseconds, and coordinated goal-directed behaviour as a team at the timescale of tens of seconds. We investigate the emergence of behaviours at different levels of abstraction, as well as the representations that underlie these behaviours using several analysis techniques, including statistics from real-world sports analytics. Our work constitutes a complete demonstration of integrated decision-making at multiple scales in a physically embodied multi-agent setting. See project video at https://youtu.be/KHMwq9pv7mg.

• The paper presents a novel three-stage training framework that bridges low-level motor control with high-level team play using imitation and reinforcement learning.
• The methodology employs imitation learning for natural movement, reinforcement learning for skill acquisition, and multi-agent strategies for emergent, coordinated team behaviors.
• Results demonstrate enhanced realism, improved football-specific skills, and strategic decision-making, setting a new benchmark for embodied AI research.

Intelligent Motor Control and Team Dynamics in Simulated Humanoid Football

The paper, "From Motor Control to Team Play in Simulated Humanoid Football," presents an extensive paper on the integration of hierarchical motor control and multi-agent cooperation within a physically embodied AI framework. Utilizing reinforcement learning and imitation learning, the research focuses on training humanoid avatars to play football in a simulated environment.

The primary aim of the paper is to bridge the gap between low-level motor control, typically seen in milliseconds, and high-level, goal-directed team behaviors that unfold over tens of seconds. This is achieved through a three-stage training method that incorporates imitation learning, single and multi-agent reinforcement learning, and population-based training. The resulting model allows humanoid players to perform complex, coordinated actions, such as dribbling, shooting, and team play, highlighting an effective framework for multi-scale decision-making in AI.

Summary of Key Contributions

1. Multi-Scale Behavioral Integration: The paper tackles the challenge of integrating motor control with cognitive-level decisions. Previous AI research has often treated these aspects separately, but this work shows they can be combined effectively to yield complex, coordinated behavior in a multi-agent setting.
2. Detailed Training Methodology:
   • Initial Stage (Motor Control): The research begins by applying imitation learning to train agents to perform basic human-like movements using motion capture data. This stage focuses on the physical actuation of humanoid bodies.
   • Mid-Level Skills Acquisition: Following motor control, agents partake in simulated drills designed to hone football-specific skills such as shooting and dribbling. This stage employs reinforcement learning to advance skill complexity without losing the human-like realism achieved in the first stage.
   • Team Play via Multi-Agent RL: The final stage involves multi-agent reinforcement learning to simulate 2v2 football matches. By devising an auto-curriculum through self-play, agents learn strategic team behaviors that emerge naturally rather than being explicitly programmed.
3. Analysis of Emergent Behaviors and Representations: The paper provides an in-depth analysis of emergent behaviors at different abstraction levels and investigates the interplay of these behaviors. The representation learning aspect demonstrated through various analytical techniques reveals how agents internalize and manifest football strategies.

Numerical Results and Claims

The humanoid agents demonstrate significant advancements in coordinated play due to the structured training methodology. The paper reports improvements in movement realism, efficiency of football-specific skills, and strategic team play. The training framework is evaluated through quantitative analyses comparing agent behaviors and performance metrics, correlating them with coordination metrics from real-world sports analytics.

Implications and Future Directions

The paper's successful integration of hierarchical motor control and strategic team play in AI systems has vast implications for the fields of robotics, gaming, and embodied AI. The modularity of the framework indicates potential applicability in various domains requiring high-level abstraction and decision-making in conjunction with low-level motor skills. Future developments could involve extending this framework to more complex scenarios, including larger teams and fully integrated football rules, as well as leveraging this work in real-world robot applications.

In summary, this work presents a comprehensive approach to achieving coordinated and adaptable behaviors in multi-agent systems, demonstrating significant progress in the field of embodied AI. By seamlessly integrating multiple levels of control and abstraction, the paper sets a foundation for future research in both AI and cognitive sciences.