# A GP-based Robust Motion Planning Framework for Agile Autonomous Robot Navigation and Recovery in Unknown Environments

arXiv: https://arxiv.org/abs/2402.01617 · HTML: https://mlsanity.com/paper/2402.01617/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 2 Feb 2024.

## Builds on (most cited first, 10 of 10)

- [Hamilton-Jacobi Reachability: A Brief Overview and Recent Advances](https://mlsanity.com/paper/1709.07523/) (2017)
- [BADGR: An Autonomous Self-Supervised Learning-Based Navigation System](https://mlsanity.com/paper/2002.05700/) (2020)
- [How Should a Robot Assess Risk? Towards an Axiomatic Theory of Risk in Robotics](https://mlsanity.com/paper/1710.11040/) (2017)
- [LaND: Learning to Navigate from Disengagements](https://mlsanity.com/paper/2010.04689/) (2020)
- [Geometrically Constrained Trajectory Optimization for Multicopters](https://mlsanity.com/paper/2103.00190/) (2021)
- [FASTER: Fast and Safe Trajectory Planner for Navigation in Unknown Environments](https://mlsanity.com/paper/2001.04420/) (2020)
- [Proactive Anomaly Detection for Robot Navigation with Multi-Sensor Fusion](https://mlsanity.com/paper/2204.01146/) (2022)
- [Data-Driven Reachable Set Computation using Adaptive Gaussian Process Classification and Monte Carlo Methods](https://mlsanity.com/paper/1910.02500/) (2019)
- [Bubble Planner: Planning High-speed Smooth Quadrotor Trajectories using Receding Corridors](https://mlsanity.com/paper/2202.12177/) (2022)
- [MINVO Basis: Finding Simplexes with Minimum Volume Enclosing Polynomial Curves](https://mlsanity.com/paper/2010.10726/) (2020)

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

- [Soft Actor-Critic-based Control Barrier Adaptation for Robust Autonomous Navigation in Unknown Environments](https://mlsanity.com/paper/2503.08479/) (2025)

## Abstract (arXiv)

For autonomous mobile robots, uncertainties in the environment and system model can lead to failure in the motion planning pipeline, resulting in potential collisions. In order to achieve a high level of robust autonomy, these robots should be able to proactively predict and recover from such failures. To this end, we propose a Gaussian Process (GP) based model for proactively detecting the risk of future motion planning failure. When this risk exceeds a certain threshold, a recovery behavior is triggered that leverages the same GP model to find a safe state from which the robot may continue towards the goal. The proposed approach is trained in simulation only and can generalize to real world environments on different robotic platforms. Simulations and physical experiments demonstrate that our framework is capable of both predicting planner failures and recovering the robot to states where planner success is likely, all while producing agile motion.