Robots with Attitude: Singularity-Free Quaternion-Based Model-Predictive Control for Agile Legged Robots

John Z. Zhang; Shuo Yang; Zachary Manchester; Zixin Zhang

Robots with Attitude: Singularity-Free Quaternion-Based Model-Predictive Control for Agile Legged Robots

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2409.09940 v2 pith:2IC3JBGO submitted 2024-09-16 cs.RO

Zixin Zhang , John Z. Zhang , Shuo Yang , Zachary Manchester This is my paper · ORCID

classification cs.RO

keywords robotsattitudealgorithmcontrolleggedmodel-predictivesingularity-freeabstraction

verification ladder T0 review T1 audit T2 compute T3 formal

0 comments

read the original abstract

We present a model-predictive control (MPC) framework for legged robots that avoids the singularities associated with common three-parameter attitude representations like Euler angles during large-angle rotations. Our method parameterizes the robot's attitude with singularity-free unit quaternions and makes modifications to the iterative linear-quadratic regulator (iLQR) algorithm to deal with the resulting geometry. The derivation of our algorithm requires only elementary calculus and linear algebra, deliberately avoiding the abstraction and notation of Lie groups. We demonstrate the performance and computational efficiency of quaternion MPC in several experiments on quadruped and humanoid robots.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Continual Learning and Lifting of Koopman Dynamics for Linear Control of Legged Robots
cs.RO 2024-11 conditional novelty 6.0 of 10

An incremental Koopman algorithm that grows both dataset and latent dimension enables linear-MPC walking on five simulated legged robots, but its monotonic convergence theorem assumes the learned embedding is already ...

Pith tools