# Is Linear Feedback on Smoothed Dynamics Sufficient for Stabilizing Contact-Rich Plans?

arXiv: https://arxiv.org/abs/2411.06542 · HTML: https://mlsanity.com/paper/2411.06542/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 35 references that are in the map.
- First posted on arXiv 10 Nov 2024.

## Builds on (most cited first, 18 of 18)

- [Do Differentiable Simulators Give Better Policy Gradients?](https://mlsanity.com/paper/2202.00817/) (2022)
- [Global Planning for Contact-Rich Manipulation via Local Smoothing of Quasi-dynamic Contact Models](https://mlsanity.com/paper/2206.10787/) (2022)
- [Optimization-Based Control for Dynamic Legged Robots](https://mlsanity.com/paper/2211.11644/) (2022)
- [Dojo: A Differentiable Physics Engine for Robotics](https://mlsanity.com/paper/2203.00806/) (2022)
- [Bundled Gradients through Contact via Randomized Smoothing](https://mlsanity.com/paper/2109.05143/) (2021)
- [Warm Start of Mixed-Integer Programs for Model Predictive Control of Hybrid Systems](https://mlsanity.com/paper/1910.08251/) (2019)
- [Non-prehensile Planar Manipulation via Trajectory Optimization with Complementarity Constraints](https://mlsanity.com/paper/2109.13145/) (2021)
- [Learning Mixed-Integer Convex Optimization Strategies for Robot Planning and Control](https://mlsanity.com/paper/2004.03736/) (2020)
- [Robust Pivoting: Exploiting Frictional Stability Using Bilevel Optimization](https://mlsanity.com/paper/2203.11412/) (2022)
- [Real-Time Multi-Contact Model Predictive Control via ADMM](https://mlsanity.com/paper/2109.07076/) (2021)
- [Application of Wrench based Feasibility Analysis to the Online Trajectory Optimization of Legged Robots](https://mlsanity.com/paper/1712.06833/) (2017)
- [Contact-Implicit Trajectory Optimization using Orthogonal Collocation](https://mlsanity.com/paper/1809.06436/) (2018)
- [Kinodynamic Motion Planning for Multi-Legged Robot Jumping via Mixed-Integer Convex Program](https://mlsanity.com/paper/2011.01809/) (2020)
- [Simultaneous Contact-Rich Grasping and Locomotion via Distributed Optimization Enabling Free-Climbing for Multi-Limbed Robots](https://mlsanity.com/paper/2207.01418/) (2022)
- [Reactive Planar Manipulation with Convex Hybrid MPC](https://mlsanity.com/paper/1710.05724/) (2017)
- [Contact-Implicit Trajectory Optimization Based on a Variable Smooth Contact Model and Successive Convexification](https://mlsanity.com/paper/1810.10462/) (2018)
- [Robust Pivoting Manipulation using Contact Implicit Bilevel Optimization](https://mlsanity.com/paper/2303.08965/) (2023)
- [Covariance Steering for Uncertain Contact-rich Systems](https://mlsanity.com/paper/2303.13382/) (2023)

## Built on by (most cited first, 2 of 2)

- [Hierarchical Contact-Rich Trajectory Optimization for Multi-Modal Manipulation using Tight Convex Relaxations](https://mlsanity.com/paper/2503.07963/) (2025)
- [Learning Pivoting Manipulation with Force and Vision Feedback Using Optimization-based Demonstrations](https://mlsanity.com/paper/2508.01082/) (2025)

## Abstract (arXiv)

Designing planners and controllers for contact-rich manipulation is extremely challenging as contact violates the smoothness conditions that many gradient-based controller synthesis tools assume. Contact smoothing approximates a non-smooth system with a smooth one, allowing one to use these synthesis tools more effectively. However, applying classical control synthesis methods to smoothed contact dynamics remains relatively under-explored. This paper analyzes the efficacy of linear controller synthesis using differential simulators based on contact smoothing. We introduce natural baselines for leveraging contact smoothing to compute (a) open-loop plans robust to uncertain conditions and/or dynamics, and (b) feedback gains to stabilize around open-loop plans. Using robotic bimanual whole-body manipulation as a testbed, we perform extensive empirical experiments on over 300 trajectories and analyze why LQR seems insufficient for stabilizing contact-rich plans. The video summarizing this paper and hardware experiments is found here: https://youtu.be/HLaKi6qbwQg?si=_zCAmBBD6rGSitm9.