ADD: Physics-Based Motion Imitation with Adversarial Differential Discriminators
Ziyu Zhang, Sergey Bashkirov, Dun Yang, Michael Taylor, Xue Bin Peng·May 08, 2025
Summary
A novel adversarial multi-objective optimization technique simulates agile, athletic skills for characters, achieving high-fidelity results. This broadly applicable approach advances physics-based character animation without manual reward engineering. It discusses Adaptive Difficulty Detection (ADD) for a 1D regression task, focusing on approximating a cosine function with varying frequencies. ADD adapts to different task difficulties, prioritizing challenging parts as training progresses. The technique covers penalties and rewards for various aspects of a quadrupedal task, including angular velocity, orientation, root height, joint dynamics, action rate, and feet air time. ADD outperforms a manually designed reward function on torso stability and control smoothness metrics for a quadruped task, matching its sample efficiency, final performance, and consistency.
Introduction
Background
Overview of current challenges in physics-based character animation
Importance of high-fidelity results in character animation
Objective
To present a novel adversarial multi-objective optimization technique that simulates agile, athletic skills for characters
To demonstrate the technique's ability to achieve high-fidelity results without manual reward engineering
Method
Adaptive Difficulty Detection (ADD) for 1D Regression Task
Description of the ADD framework
Application to approximating a cosine function with varying frequencies
Multi-Objective Optimization
Explanation of the optimization process
Integration of penalties and rewards for various aspects of a quadrupedal task
Consideration of angular velocity, orientation, root height, joint dynamics, action rate, and feet air time
Adaptive Training Prioritization
Mechanism for ADD to adapt to different task difficulties
Focus on prioritizing challenging parts as training progresses
Results
Quadrupedal Task Performance
Comparison of ADD against a manually designed reward function
Metrics for torso stability and control smoothness
Sample Efficiency, Final Performance, and Consistency
Evaluation of ADD's performance in terms of sample efficiency
Analysis of final performance and consistency relative to the manually designed reward function
Conclusion
Summary of Findings
Recap of ADD's effectiveness in simulating agile, athletic skills for characters
Implications for Physics-Based Character Animation
Potential applications and future research directions
Contribution to the Field
Contribution of the novel adversarial multi-objective optimization technique to physics-based character animation
Basic info
papers
computer vision and pattern recognition
robotics
graphics
artificial intelligence
Advanced features
Insights
In what ways does ADD outperform manually designed reward functions in quadrupedal tasks?
What are the key components of the Adaptive Difficulty Detection (ADD) method in the context of a 1D regression task?
How does the adversarial multi-objective optimization technique improve physics-based character animation?
What penalties and rewards are considered in the quadrupedal task using ADD?