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CITATION.cff
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# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: 'DIAL-MPC: Diffusion-Inspired Annealing For Legged MPC'
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Haoru
family-names: Xue
email: [email protected]
affiliation: University of California Berkeley
orcid: 'https://orcid.org/0009-0009-1195-2254'
- given-names: Chaoyi
family-names: Pan
email: [email protected]
affiliation: Carnegie Mellon University
- given-names: Zeji
family-names: Yi
email: [email protected]
affiliation: Carnegie Mellon University
- given-names: Guannan
family-names: Qu
email: [email protected]
affiliation: Carnegie Mellon University
orcid: 'https://orcid.org/0000-0002-5466-3550'
- given-names: Guanya
family-names: Shi
email: [email protected]
affiliation: Carnegie Mellon University
orcid: 'https://orcid.org/0000-0002-9075-3705'
identifiers:
- type: doi
value: 10.48550/arXiv.2409.15610
repository-code: 'https://github.com/LeCAR-Lab/dial-mpc'
url: 'https://lecar-lab.github.io/dial-mpc/'
abstract: >-
Due to high dimensionality and non-convexity, real-time
optimal control using full-order dynamics models for
legged robots is challenging. Therefore, Nonlinear Model
Predictive Control (NMPC) approaches are often limited to
reduced-order models. Sampling-based MPC has shown
potential in nonconvex even discontinuous problems, but
often yields suboptimal solutions with high variance,
which limits its applications in high-dimensional
locomotion. This work introduces DIAL-MPC
(Diffusion-Inspired Annealing for Legged MPC), a
sampling-based MPC framework with a novel diffusion-style
annealing process. Such an annealing process is supported
by the theoretical landscape analysis of Model Predictive
Path Integral Control (MPPI) and the connection between
MPPI and single-step diffusion. Algorithmically, DIAL-MPC
iteratively refines solutions online and achieves both
global coverage and local convergence. In quadrupedal
torque-level control tasks, DIAL-MPC reduces the tracking
error of standard MPPI by 13.4 times and outperforms
reinforcement learning (RL) policies by 50% in challenging
climbing tasks without any training. In particular,
DIAL-MPC enables precise real-world quadrupedal jumping
with payload. To the best of our knowledge, DIAL-MPC is
the first training-free method that optimizes over
full-order quadruped dynamics in real-time.
keywords:
- mpc
- optimal control
- diffusion
- humanoid
- legged robot
- quadruped
license: Apache-2.0
preferred-citation:
type: generic
authors:
- given-names: Haoru
family-names: Xue
email: [email protected]
affiliation: University of California Berkeley
orcid: 'https://orcid.org/0009-0009-1195-2254'
- given-names: Chaoyi
family-names: Pan
email: [email protected]
affiliation: Carnegie Mellon University
- given-names: Zeji
family-names: Yi
email: [email protected]
affiliation: Carnegie Mellon University
- given-names: Guannan
family-names: Qu
email: [email protected]
affiliation: Carnegie Mellon University
orcid: 'https://orcid.org/0000-0002-5466-3550'
- given-names: Guanya
family-names: Shi
email: [email protected]
affiliation: Carnegie Mellon University
orcid: 'https://orcid.org/0000-0002-9075-3705'
url: https://arxiv.org/abs/2409.15610
month: 9
year: 2024
title: Full-Order Sampling-Based MPC for Torque-Level Locomotion Control via Diffusion-Style Annealing