# Koopman global linearization of contact dynamics for robot locomotion and manipulation enables elaborate control

arXiv: https://arxiv.org/abs/2511.06515 · HTML: https://mlsanity.com/paper/2511.06515/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2025.

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

- [Efficient Reinforcement Learning using Linear Koopman Dynamics for Nonlinear Robotic Systems](https://mlsanity.com/paper/2604.19980/) (2026)

## Abstract (arXiv)

Controlling robots that dynamically engage in contact with their environment is a pressing challenge. Whether a legged robot making-and-breaking contact with a floor, or a manipulator grasping objects, contact is everywhere. Unfortunately, the switching of dynamics at contact boundaries makes control difficult. Predictive controllers face non-convex optimization problems when contact is involved. Here, we overcome this difficulty by applying Koopman operators to subsume the segmented dynamics due to contact changes into a unified, globally-linear model in an embedding space. We show that viscoelastic contact at robot-environment interactions underpins the use of Koopman operators without approximation to control inputs. This methodology enables the convex Model Predictive Control of a legged robot, and the real-time control of a manipulator engaged in dynamic pushing. In this work, we show that our method allows robots to discover elaborate control strategies in real-time over time horizons with multiple contact changes, and the method is applicable to broad fields beyond robotics.