# SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots

arXiv: https://arxiv.org/abs/2108.01262 · HTML: https://mlsanity.com/paper/2108.01262/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 3 Aug 2021.

## Builds on (most cited first, 8 of 8)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Emergent Tool Use From Multi-Agent Autocurricula](https://mlsanity.com/paper/1909.07528/) (2019)
- [Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic](https://mlsanity.com/paper/1404.2334/) (2014)
- [FASTER: Fast and Safe Trajectory Planner for Flights in Unknown Environments](https://mlsanity.com/paper/1903.03558/) (2019)
- [A Scalable Framework For Real-Time Multi-Robot, Multi-Human Collision Avoidance](https://mlsanity.com/paper/1811.05929/) (2018)
- [Risk-Aware Motion Planning for a Limbed Robot with Stochastic Gripping Forces Using Nonlinear Programming](https://mlsanity.com/paper/2006.02656/) (2020)
- [Visual-Inertial Object Detection and Mapping](https://mlsanity.com/paper/1806.08498/) (2018)
- [Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance](https://mlsanity.com/paper/2007.14035/) (2020)

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

- [Real-to-Sim: Predicting Residual Errors of Robotic Systems with Sparse Data using a Learning-based Unscented Kalman Filter](https://mlsanity.com/paper/2209.03210/) (2022)
- [Chance-Constrained Optimization in Contact-Rich Systems for Robust Manipulation](https://mlsanity.com/paper/2203.02616/) (2022)
- [RoboAtlas: Contextual Active SLAM](https://mlsanity.com/paper/2606.26046/) (2026)
- [OptiState: State Estimation of Legged Robots using Gated Networks with Transformer-based Vision and Kalman Filtering](https://mlsanity.com/paper/2401.16719/) (2024)

## Abstract (arXiv)

We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal while avoiding obstacles in uncertain environments. First, we use stochastic model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. Second, recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution, which are trained on uncertainty outputs of various simultaneous localization and mapping algorithms. When two or more robots are in communication range, these uncertainties are then updated using a distributed Kalman filtering approach. Lastly, a Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal. Our complete methods are demonstrated on a ground and aerial robot simultaneously (code available at: https://github.com/AlexS28/SABER).