# An Efficient Locally Reactive Controller for Safe Navigation in Visual Teach and Repeat Missions

arXiv: https://arxiv.org/abs/2201.03938 · HTML: https://mlsanity.com/paper/2201.03938/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2022.

## Built on by (most cited first, 14 of 14)

- [Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion](https://mlsanity.com/paper/2401.17583/) (2024)
- [Fast Traversability Estimation for Wild Visual Navigation](https://mlsanity.com/paper/2305.08510/) (2023)
- [RoboHop: Segment-based Topological Map Representation for Open-World Visual Navigation](https://mlsanity.com/paper/2405.05792/) (2024)
- [Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-End](https://mlsanity.com/paper/2310.03581/) (2023)
- [Wild Visual Navigation: Fast Traversability Learning via Pre-Trained Models and Online Self-Supervision](https://mlsanity.com/paper/2404.07110/) (2024)
- [One Filter to Deploy Them All: Robust Safety for Quadrupedal Navigation in Unknown Environments](https://mlsanity.com/paper/2412.09989/) (2024)
- [Gaitor: Learning a Unified Representation Across Gaits for Real-World Quadruped Locomotion](https://mlsanity.com/paper/2405.19452/) (2024)
- [TANGO: Traversability-Aware Navigation with Local Metric Control for Topological Goals](https://mlsanity.com/paper/2509.08699/) (2025)
- [Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation](https://mlsanity.com/paper/2204.08647/) (2022)
- [ObjectReact: Learning Object-Relative Control for Visual Navigation](https://mlsanity.com/paper/2509.09594/) (2025)
- [Roll-Drop: accounting for observation noise with a single parameter](https://mlsanity.com/paper/2304.13150/) (2023)
- [TOP-Nav: Legged Navigation Integrating Terrain, Obstacle and Proprioception Estimation](https://mlsanity.com/paper/2404.15256/) (2024)
- [Toward Reliable Sim-to-Real Predictability for MoE-based Robust Quadrupedal Locomotion](https://mlsanity.com/paper/2602.00678/) (2026)
- [E2DRL: End-to-End Differentiable Reinforcement Learning for 3D Obstacle Avoidance in Quadruped Robots](https://mlsanity.com/paper/2610.19627/) (2026)

## Abstract (arXiv)

To achieve successful field autonomy, mobile robots need to freely adapt to changes in their environment. Visual navigation systems such as Visual Teach and Repeat (VT&R) often assume the space around the reference trajectory is free, but if the environment is obstructed path tracking can fail or the robot could collide with a previously unseen obstacle. In this work, we present a locally reactive controller for a VT&R system that allows a robot to navigate safely despite physical changes to the environment. Our controller uses a local elevation map to compute vector representations and outputs twist commands for navigation at 10 Hz. They are combined in a Riemannian Motion Policies (RMP) controller that requires <2 ms to run on a CPU. We integrated our controller with a VT&R system onboard an ANYmal C robot and tested it in indoor cluttered spaces and a large-scale underground mine. We demonstrate that our locally reactive controller keeps the robot safe when physical occlusions or loss of visual tracking occur such as when walking close to walls, crossing doorways, or traversing narrow corridors. Video: https://youtu.be/G_AwNec5AwU