AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control

Abstract

Synthesizing graceful and life-like behaviors for physically simulated characters has been a fundamental challenge in computer animation. Data-driven methods that leverage motion tracking are a prominent class of techniques for producing high fidelity motions for a wide range of behaviors. However, the effectiveness of these tracking-based methods often hinges on carefully designed objective functions, and when applied to large and diverse motion datasets, these methods require significant additional machinery to select the appropriate motion for the character to track in a given scenario. In this work, we propose to obviate the need to manually design imitation objectives and mechanisms for motion selection by utilizing a fully automated approach based on adversarial imitation learning. High-level task objectives that the character should perform can be specified by relatively simple reward functions, while the low-level style of the character's behaviors can be specified by a dataset of unstructured motion clips, without any explicit clip selection or sequencing. These motion clips are used to train an adversarial motion prior, which specifies style-rewards for training the character through reinforcement learning (RL). The adversarial RL procedure automatically selects which motion to perform, dynamically interpolating and generalizing from the dataset. Our system produces high-quality motions that are comparable to those achieved by state-of-the-art tracking-based techniques, while also being able to easily accommodate large datasets of unstructured motion clips. Composition of disparate skills emerges automatically from the motion prior, without requiring a high-level motion planner or other task-specific annotations of the motion clips. We demonstrate the effectiveness of our framework on a diverse cast of complex simulated characters and a challenging suite of motor control tasks.

References in corpus (3)

• DeepMind Control Suite
• Character Controllers Using Motion VAEs
• Learning and Transfer of Modulated Locomotor Controllers

Cited by in corpus (44)

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• A Comprehensive Review of Data-Driven Co-Speech Gesture Generation
• ControlVAE: Model-Based Learning of Generative Controllers for Physics-Based Characters
• Transflower: probabilistic autoregressive dance generation with multimodal attention
• CALM: Conditional Adversarial Latent Models for Directable Virtual Characters
• Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
• C$\cdot$ASE: Learning Conditional Adversarial Skill Embeddings for Physics-based Characters
• From Screens to Scenes: A Survey of Embodied AI in Healthcare
• HumanMimic: Learning Natural Locomotion and Transitions for Humanoid Robot via Wasserstein Adversarial Imitation
• PADL: Language-Directed Physics-Based Character Control
• Contact-Implicit Model Predictive Control: Controlling Diverse Quadruped Motions Without Pre-Planned Contact Modes or Trajectories
• Composite Motion Learning with Task Control
• A GAN-Like Approach for Physics-Based Imitation Learning and Interactive Character Control
• AdaptNet: Policy Adaptation for Physics-Based Character Control
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• Bidirectional GaitNet: A Bidirectional Prediction Model of Human Gait and Anatomical Conditions
• Diffuse-CLoC: Guided Diffusion for Physics-based Character Look-ahead Control
• MAAIP: Multi-Agent Adversarial Interaction Priors for imitation from fighting demonstrations for physics-based characters
• Learning to Walk and Fly with Adversarial Motion Priors
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• Adversarial Skill Chaining for Long-Horizon Robot Manipulation via Terminal State Regularization
• SuperPADL: Scaling Language-Directed Physics-Based Control with Progressive Supervised Distillation
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• SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control
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• AMOR: Adaptive Character Control through Multi-Objective Reinforcement Learning
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• Modeling human intention inference in continuous 3D domains by inverse planning and body kinematics
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• CHOICE: Coordinated Human-Object Interaction in Cluttered Environments for Pick-and-Place Actions
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• HHI-Assist: A Dataset and Benchmark of Human-Human Interaction in Physical Assistance Scenario
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