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This article focuses on InstantMimic, a system designed to accelerate the training of physics-based character control and robotic skills by optimizing the entire machine learning pipeline for GPU execution. By eliminating CPU bottlenecks in simulation and policy updates, it reduces training times from hours to seconds, directly addressing efficiency challenges in deep reinforcement learning and imitation learning.
InstantMimic: A High Performance System for Learning Physics-based Skills in Seconds
arXiv:2609.09821v1 Announce Type: cross Abstract: Physics-based character control is a long-standing challenge in computer graphics and robotics, requiring policies that satisfy complex dynamics while producing realistic motion. Recent Deep RL approaches, particularly imitation learning methods such as DeepMimic, have had broad impact beyond animation, influencing robotics by enabling agile and expressive behaviors. While these approaches achieve impressive results, they remain computationally inefficient to train in practice. Despite GPU-accelerated simulation, we find that end-to-end pipelines often underutilize hardware due to overheads outside the physics solver, caused by fragmented GPU kernels and CPU memory access in the critical path. We present InstantMimic, a system that addresses these inefficiencies by making the entire training loop GPU-native. Built on a GPU-native physics backend, our unified pipeline integrates simulation, environment computation, policy inference, and policy updates within a single execution flow. As a result, InstantMimic reduces training time for diverse physics-based skills to a few seconds and makes LLM-agent-driven hyperparameter search practical.
Abstract:Physics-based character control is a long-standing challenge in computer graphics and robotics, requiring policies that satisfy complex dynamics while producing realistic motion. Recent Deep RL approaches, particularly imitation learning methods such as DeepMimic, have had broad impact beyond animation, influencing robotics by enabling agile and expressive behaviors. While these approaches achieve impressive results, they remain computationally inefficient to train in practice. Despite GPU-accelerated simulation, we find that end-to-end pipelines often underutilize hardware due to overheads outside the physics solver, caused by fragmented GPU kernels and CPU memory access in the critical path. We present InstantMimic, a system that addresses these inefficiencies by making the entire training loop GPU-native. Built on a GPU-native physics backend, our unified pipeline integrates simulation, environment computation, policy inference, and policy updates within a single execution flow. As a result, InstantMimic reduces training time for diverse physics-based skills to a few seconds and makes LLM-agent-driven hyperparameter search practical.
Accepted to SIGGRAPH Asia 2026 Conference Papers. 11 pages, 11 figures. Project page: this https URL
Graphics (cs.GR); Robotics (cs.RO)
arXiv:2609.09821 [cs.GR]
(or arXiv:2609.09821v1 [cs.GR] for this version)
https://doi.org/10.48550/arXiv.2609.09821 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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