PARC: Physics-based Augmentation with Reinforcement Learning for Character Controllers (2505.04002v1)

Abstract: Humans excel in navigating diverse, complex environments with agile motor skills, exemplified by parkour practitioners performing dynamic maneuvers, such as climbing up walls and jumping across gaps. Reproducing these agile movements with simulated characters remains challenging, in part due to the scarcity of motion capture data for agile terrain traversal behaviors and the high cost of acquiring such data. In this work, we introduce PARC (Physics-based Augmentation with Reinforcement Learning for Character Controllers), a framework that leverages machine learning and physics-based simulation to iteratively augment motion datasets and expand the capabilities of terrain traversal controllers. PARC begins by training a motion generator on a small dataset consisting of core terrain traversal skills. The motion generator is then used to produce synthetic data for traversing new terrains. However, these generated motions often exhibit artifacts, such as incorrect contacts or discontinuities. To correct these artifacts, we train a physics-based tracking controller to imitate the motions in simulation. The corrected motions are then added to the dataset, which is used to continue training the motion generator in the next iteration. PARC's iterative process jointly expands the capabilities of the motion generator and tracker, creating agile and versatile models for interacting with complex environments. PARC provides an effective approach to develop controllers for agile terrain traversal, which bridges the gap between the scarcity of motion data and the need for versatile character controllers.

Overview of PARC: Physics-based Augmentation with Reinforcement Learning for Character Controllers

The paper "PARC: Physics-based Augmentation with Reinforcement Learning for Character Controllers" presents a novel framework aimed at overcoming the limitations often encountered in the simulation of agile movements for virtual characters, particularly in complex terrains. The framework leverages the intersection of reinforcement learning, machine learning, and physics-based simulation to expand motion dataset capabilities iteratively and reliably generate agile and versatile animated character models.

Problem Addressed

Parkour and other dynamic maneuvers demonstrate human agility in navigating complex environments. However, reproducing these movements in simulations is challenging, particularly due to the scarcity of specific motion capture data required for such tasks. Motion capture data acquisition is expensive and often limited, which constrains the development of versatile character controllers capable of performing diverse terrain traversal tasks.

Methodology

The paper proposes the PARC framework, which combines reinforcement learning with physics-based simulations to iteratively enhance character controllers. The framework comprises two main components:

1. Motion Generator: Initialized with a small dataset, the motion generator learns terrain traversal skills and creates additional synthetic motions. It utilizes diffusion models within the paradigm of generating kinematic motions conditioned on terrain heightmaps and target directions.
2. Physics-based Motion Tracker: This component mitigates errors and artifacts, such as incorrect contacts or discontinuities in synthetic data, by employing imitation learning in a simulated environment. Corrected data from the motion tracker is employed to refine the dataset.

The iterative process of alternating between the motion generator and the motion tracker ensures the progressive improvement and expansion of the motion dataset while refining the tracking capabilities.

Results

The iterative training process demonstrated efficacy in synthesizing new terrain traversal behaviors that were not present in the original dataset, providing the simulated characters with the ability to navigate through terrains that require combining multiple agile maneuvers, such as jumping and climbing. Quantitative and qualitative assessments indicated substantial improvements across multiple iterations of the PARC framework. Metrics such as waypoint distance, terrain penetration loss, and contact loss visibly improved, validating the contributions of the iterative framework.

Implications and Future Work

The PARC framework establishes a robust approach to bridging the gap between the limited availability of motion datasets and the demand for sophisticated animated character controllers. This method reveals significant promise for practical applications in game development and robotics, where agile terrain traversal is requisite. Future research could explore more complex terrains and higher-dimensional space navigation, alongside optimizing the computational demands to enable real-time closed-loop planning.

Overall, the paper presents a compelling approach for developing versatile animated character controllers. By progressively expanding motion datasets and leveraging physics-based corrections, PARC offers substantial improvements over existing methods constrained by limited motion data, paving the way for more sophisticated simulations of human-like agility in virtual environments.