# Rocket Landing Control with Grid Fins and Path-following using MPC

arXiv: https://arxiv.org/abs/2405.16191 · HTML: https://mlsanity.com/paper/2405.16191/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 25 May 2024.

## Builds on (most cited first, 12 of 12)

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- [TAX-Pose: Task-Specific Cross-Pose Estimation for Robot Manipulation](https://mlsanity.com/paper/2211.09325/) (2022)
- [Personalization of End-to-end Speech Recognition On Mobile Devices For Named Entities](https://mlsanity.com/paper/1912.09251/) (2019)
- [Planar Robot Casting with Real2Sim2Real Self-Supervised Learning](https://mlsanity.com/paper/2111.04814/) (2021)
- [FlowBot++: Learning Generalized Articulated Objects Manipulation via Articulation Projection](https://mlsanity.com/paper/2306.12893/) (2023)
- [DiffCLIP: Leveraging Stable Diffusion for Language Grounded 3D Classification](https://mlsanity.com/paper/2305.15957/) (2023)
- [Robots of the Lost Arc: Self-Supervised Learning to Dynamically Manipulate Fixed-Endpoint Cables](https://mlsanity.com/paper/2011.04840/) (2020)
- [Orienting Novel 3D Objects Using Self-Supervised Learning of Rotation Transforms](https://mlsanity.com/paper/2105.14246/) (2021)
- [6-DoF Grasp Planning using Fast 3D Reconstruction and Grasp Quality CNN](https://mlsanity.com/paper/2009.08618/) (2020)
- [ART/ATK: A research platform for assessing and mitigating the sim-to-real gap in robotics and autonomous vehicle engineering](https://mlsanity.com/paper/2211.04886/) (2022)
- [APLA: Additional Perturbation for Latent Noise with Adversarial Training Enables Consistency](https://mlsanity.com/paper/2308.12605/) (2023)
- [Safety Augmented Value Estimation from Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic Tasks](https://mlsanity.com/paper/1905.13402/) (2019)

## Abstract (arXiv)

In this project, we attempt to optimize a landing trajectory of a rocket. The goal is to minimize the total fuel consumption during the landing process using different techniques. Once the optimal and feasible trajectory is generated using batch approach, we attempt to follow the path using a Model Predictive Control (MPC) based algorithm, called Trajectory Optimizing Path following Estimation from Demonstration (TOPED), in order to generalize to similar initial states and models, where we introduce a novel cost function for the MPC to solve. We further show that TOPED can follow a demonstration trajectory well in practice under model mismatch and different initial states.