SkillMimic: Learning Basketball Interaction Skills from Demonstrations

Abstract: Traditional reinforcement learning methods for human-object interaction (HOI) rely on labor-intensive, manually designed skill rewards that do not generalize well across different interactions. We introduce SkillMimic, a unified data-driven framework that fundamentally changes how agents learn interaction skills by eliminating the need for skill-specific rewards. Our key insight is that a unified HOI imitation reward can effectively capture the essence of diverse interaction patterns from HOI datasets. This enables SkillMimic to learn a single policy that not only masters multiple interaction skills but also facilitates skill transitions, with both diversity and generalization improving as the HOI dataset grows. For evaluation, we collect and introduce two basketball datasets containing approximately 35 minutes of diverse basketball skills. Extensive experiments show that SkillMimic successfully masters a wide range of basketball skills including stylistic variations in dribbling, layup, and shooting. Moreover, these learned skills can be effectively composed by a high-level controller to accomplish complex and long-horizon tasks such as consecutive scoring, opening new possibilities for scalable and generalizable interaction skill learning. Project page: https://ingrid789.github.io/SkillMimic/

• The paper introduces a unified learning framework that reuses skills from human demonstrations to enable smooth transitions between basketball maneuvers.
• It utilizes two datasets, BallPlay-V and BallPlay-M, capturing 35 minutes of diverse basketball moves to eliminate manual reward engineering.
• Experimental results show that its hierarchical policy and contact graph reward achieve robust performance in dribbling, layup scoring, and rebound retrieval tasks.

Overview of "SkillMimic: Learning Reusable Basketball Skills from Demonstrations"

The paper "SkillMimic: Learning Reusable Basketball Skills from Demonstrations" introduces a novel approach for learning diverse basketball skills through a data-driven paradigm inspired by human demonstrations. SkillMimic aims to address the challenges associated with traditional reinforcement learning (RL) methods that rely on manually designed rewards, which are often labor-intensive and fail to generalize across different skills.

Key Contributions and Methodology

The main contribution of the paper is the development of SkillMimic, which enables a simulated humanoid to learn and switch between various basketball skills using human-ball interaction datasets. The approach leverages a unified configuration to train a single policy capable of learning multiple skills, allowing for smooth skill transitions that are not present in the reference dataset. This characteristic is crucial for achieving complex tasks such as layup scoring, dribbling, and retrieving rebounds autonomously.

To evaluate SkillMimic, the authors introduce two basketball datasets: BallPlay-V and BallPlay-M. BallPlay-V estimates motions from monocular RGB videos, while BallPlay-M utilizes advanced motion capture equipment, collectively covering about 35 minutes of diverse basketball skills.

Experimental Validation

The paper reports several experiments demonstrating that SkillMimic can effectively learn and generalize various basketball skills with a unified configuration. Notably, the method achieves success in tasks such as directional dribbling and layup scoring by reusing acquired skills through a high-level control policy. This hierarchical approach significantly outperforms existing methods in terms of simplicity and efficiency, primarily due to the elimination of skill-specific reward designs.

Results and Implications

SkillMimic achieves robust performance, showing strong numerical results in learning basketball skills included in both datasets. The experiments highlight the method's ability to generalize learned skills across varying datasets, exhibiting resilience to inaccuracies in the data. The contact graph reward (CGR) is particularly effective, enabling precise contact imitation critical for interaction skills.

The implications of this research extend beyond basketball, suggesting that similar data-driven methodologies could be applied to other sports or complex object-interaction tasks, bridging the gap between human and robotic skill learning. This could lead to advancements in robotics and humanoid simulations in dynamic environments.

Future Directions

The paper opens avenues for future research on scalable, data-driven learning frameworks in sports and other fields requiring intricate interaction with objects. Potential developments could explore expanding datasets, integrating multiple object interactions, and enhancing generalization to accommodate different environments and physical properties.

In conclusion, SkillMimic represents a significant step towards more efficient learning of complex interaction skills, reducing the reliance on manually designed rewards and improving the adaptability and reuse of learned skills in dynamic tasks.