mlsanityData to 14 Oct 2026

MimicKit: A Reinforcement Learning Framework for Motion Imitation and Control

arXiv 2510.13794 ↗First posted 15 Oct 2025cs.GR cs.LG cs.ROcited by 10

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

Abstract (arXiv)

MimicKit is an open-source framework for training motion controllers using motion imitation and reinforcement learning. The codebase provides implementations of commonly-used motion-imitation techniques and RL algorithms. This framework is intended to support research and applications in computer graphics and robotics by providing a unified training framework, along with standardized environment, agent, and data structures. The codebase is designed to be modular and easily configurable, enabling convenient modification and extension to new characters and tasks. The open-source codebase is available at: https://github.com/xbpeng/MimicKit.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 2 of its 10 references that are in the map. First posted on arXiv 15 Oct 2025.

Builds on

2 of 10 references.

References · 2

  1. 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 998
  2. 2019Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learningcited by 782

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 10.

10 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2019Relational Mimic for Visual Adversarial Imitation Learning
  2. 2023MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations
  3. 2025RoboManipBaselines: A Unified Framework for Imitation Learning in Robotic Manipulation across Real and Simulation Environments
  4. 2023MimicPlay: Long-Horizon Imitation Learning by Watching Human Play
  5. 2021RAIL: A modular framework for Reinforcement-learning-based Adversarial Imitation Learning
  6. 2022Interactive Imitation Learning in Robotics: A Survey
  7. 2022imitation: Clean Imitation Learning Implementations
  8. 2023Programmatic Imitation Learning from Unlabeled and Noisy Demonstrations