A Model-Based Planning and Control Framework for Parkour-Style Legged Locomotion
Name
chignoli-chignoli-phd-meche-2025-thesis.pdf
Description
Thesis PDF
Size
32.42 MB
Format
Adobe PDF
Checksum (MD5)
3c36101caf83525afdf1e752fa9616d1
Author(s)
Chignoli, Matthew T.
Advisor(s)
Kim, Sangbae
Date Issued
May 2025
Publisher
Massachusetts Institute of Technology
Abstract
Legged robots have long been envisioned as a means of expanding robotic capabilities beyond structured environments, yet achieving high-agility locomotion remains a fundamental challenge. This thesis presents a model-based framework for parkour-style locomotion, enabling robots to execute highly dynamic maneuvers such as jumps, rolls, and flips with precision and robustness. A key challenge in planning these motions is selecting an appropriate dynamic model that balances computational efficiency with physical accuracy. To address this, a model assessment strategy is introduced to determine the simplest model capable of capturing task-relevant dynamics. Even with well-chosen models, solving long-horizon trajectory optimization problems for dynamic motions is computationally demanding. This thesis introduces graduated optimization techniques, which improve solver efficiency and reliability by generating high-quality initial guesses through progressively refined problem formulations. Additionally, a novel formulation of rigid-body dynamics algorithms for systems with kinematic loops accelerates trajectory optimization and simulation. Finally, two control strategies are proposed to execute planned motions on hardware: a model-based tracking controller for real-time adjustments and an imitation learning policy trained on optimal trajectories to enhance robustness. Extensive experiments on hardware validate the framework, demonstrating the successful execution of complex, high-impact locomotion behaviors. By integrating advanced planning, optimization, and control techniques, this work establishes a foundation for high-agility legged locomotion, pushing beyond conventional automation toward real-world, dynamic robotic movement.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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