Bi-Level Optimization for Contact and Motion Planning in Rope-Assisted Legged Robots
2026-04-29 • Robotics
Robotics
AI summaryⓘ
The authors developed a planning method to help rope-climbing robots decide where to place their feet and how to pull on their ropes when climbing walls. Their method uses two levels of math optimization: one to pick safe spots to land, and another to find the best movements and forces to use. They tested this method on a new robot called ALPINE, which successfully climbed tricky surfaces. This shows their approach can help robots climb more effectively using ropes.
bi-level optimizationCross-Entropy Methodmixed-integer problemnonlinear optimizationrope-assisted robotslocomotion planningrobot climbingdynamically feasible motionsterrain selectioncontrol inputs
Authors
Ruben Malacarne, Ioannis Tsikelis, Enrico Mingo Hoffman, Michele Focchi
Abstract
This paper presents a planning pipeline framework for locomotion in rope-assisted robots climbing vertical surfaces. The proposed framework is formulated as a bi-level optimization scheme that addresses a mixed-integer problem: selecting feasible terrain regions for landing while simultaneously optimizing the control inputs, namely rope tensions and leg forces, and landing location. The outer level of the optimization is solved using the Cross-Entropy Method, while the inner level relies on gradient-based nonlinear optimization to compute dynamically feasible motions. The approach is validated on a novel climbing robot platform, ALPINE, across a variety of challenging terrain configurations.