Geometry-Aware Predictive Control for Bipedal Obstacle Crossing
Geometry-Aware Predictive Control for Walk-to-Run Bipedal Obstacle Crossing
A framework enables smooth walk-to-run transitions for bipedal robots crossing obstacles. It uses five phases and MPC to ensure clearance and stability, achieving success on four out of five obstacles.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- Environment
- SimulationOpen source S4
Reported numbers
body-obstacle clearances
0.036
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minimum body-obstacle clearances of 0.036, 0.036, 0.059, and 0.017 m
Open source S5body-obstacle clearances
0.059
View original evidence
minimum body-obstacle clearances of 0.036, 0.036, 0.059, and 0.017 m
Open source S5body-obstacle clearances
0.017
View original evidence
minimum body-obstacle clearances of 0.036, 0.036, 0.059, and 0.017 m
Open source S5obstacles
>5
clearances
0.036
View original evidence
minimum body-obstacle clearances of 0.036, 0.036, 0.059, and 0.017 m
Open source S5clearances
0.059
View original evidence
minimum body-obstacle clearances of 0.036, 0.036, 0.059, and 0.017 m
Open source S5
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
es smooth walk-to-run transition without discrete controller switching. The generated references are tracked using MPC to regulate motion smoothness, clearance feasibility, touchdown behavior, and lateral admissibility. The framework is evaluated in reduced-order simulation over five progressively difficult obstacles under walking and running conditions. The robot successfully crosses the first four obstacles, achiev
Open source S4
ing minimum body-obstacle clearances of 0.036, 0.036, 0.059, and 0.017 m, respectively. The fifth and most demanding obstacle leads to collision, identifying the current operating boundary of the proposed reduced-order formulation. Overall, the method provides a compact, interpretable, and computationally efficient basis for intelligent obstacle-aware bipedal locomotion.
Open source S5
Source：arXiv Robotics — research abstracts · arxiv.org