# Robust Optimal Planning and Control of Non-Periodic Bipedal Locomotion with A Centroidal Momentum Model

arXiv: https://arxiv.org/abs/1708.06345 · HTML: https://mlsanity.com/paper/1708.06345/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2017.

## Built on by (most cited first, 1 of 1)

- [EmoBipedNav: Emotion-aware Social Navigation for Bipedal Robots with Deep Reinforcement Learning](https://mlsanity.com/paper/2503.12538/) (2025)

## Abstract (arXiv)

This study presents a theoretical method for planning and controlling agile bipedal locomotion based on robustly tracking a set of non-periodic keyframe states. Based on centroidal momentum dynamics, we formulate a hybrid phase-space planning and control method which includes the following key components: (i) a step transition solver that enables dynamically tracking non-periodic keyframe states over various types of terrains, (ii) a robust hybrid automaton to effectively formulate planning and control algorithms, (iii) a steering direction model to control the robot's heading, (iv) a phase-space metric to measure distance to the planned locomotion manifolds, and (v) a hybrid control method based on the previous distance metric to produce robust dynamic locomotion under external disturbances. Compared to other locomotion methodologies, we have a large focus on non-periodic gait generation and robustness metrics to deal with disturbances. Such focus enables the proposed control method to robustly track non-periodic keyframe states over various challenging terrains and under external disturbances as illustrated through several simulations.