# Local Trajectory Stabilization for Dexterous Manipulation via Piecewise Affine Approximations

arXiv: https://arxiv.org/abs/1909.08045 · HTML: https://mlsanity.com/paper/1909.08045/

## Key facts

- Cited by 4 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 17 Sep 2019.

## Built on by (most cited first, 4 of 4)

- [Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics](https://mlsanity.com/paper/2205.02835/) (2022)
- [Robust Pivoting: Exploiting Frictional Stability Using Bilevel Optimization](https://mlsanity.com/paper/2203.11412/) (2022)
- [Robust Pivoting Manipulation using Contact Implicit Bilevel Optimization](https://mlsanity.com/paper/2303.08965/) (2023)
- [Non-Gaussian Uncertainty Minimization Based Control of Stochastic Nonlinear Robotic Systems](https://mlsanity.com/paper/2303.01628/) (2023)

## Abstract (arXiv)

We propose a model-based approach to design feedback policies for dexterous robotic manipulation. The manipulation problem is formulated as reaching the target region from an initial state for some non-smooth nonlinear system. First, we use trajectory optimization to find a feasible trajectory. Next, we characterize the local multi-contact dynamics around the trajectory as a piecewise affine system, and build a funnel around the linearization of the nominal trajectory using polytopes. We prove that the feedback controller at the vicinity of the linearization is guaranteed to drive the nonlinear system to the target region. During online execution, we solve linear programs to track the system trajectory. We validate the algorithm on hardware, showing that even under large external disturbances, the controller is able to accomplish the task.