# Self-Reflective Terrain-Aware Robot Adaptation for Consistent Off-Road Ground Navigation

arXiv: https://arxiv.org/abs/2111.06742 · HTML: https://mlsanity.com/paper/2111.06742/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 136 references that are in the map.
- First posted on arXiv 12 Nov 2021.

## Builds on (most cited first, 22 of 22)

- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [RMA: Rapid Motor Adaptation for Legged Robots](https://mlsanity.com/paper/2107.04034/) (2021)
- [RELLIS-3D Dataset: Data, Benchmarks and Analysis](https://mlsanity.com/paper/2011.12954/) (2020)
- [BADGR: An Autonomous Self-Supervised Learning-Based Navigation System](https://mlsanity.com/paper/2002.05700/) (2020)
- [Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation](https://mlsanity.com/paper/1709.10489/) (2017)
- [Generalizing Koopman Theory to allow for inputs and control](https://mlsanity.com/paper/1602.07647/) (2016)
- [Introduction to Tensor Decompositions and their Applications in Machine Learning](https://mlsanity.com/paper/1711.10781/) (2017)
- [Efficient Iterative Linear-Quadratic Approximations for Nonlinear Multi-Player General-Sum Differential Games](https://mlsanity.com/paper/1909.04694/) (2019)
- [Bi-directional Domain Adaptation for Sim2Real Transfer of Embodied Navigation Agents](https://mlsanity.com/paper/2011.12421/) (2020)
- [APPLI: Adaptive Planner Parameter Learning From Interventions](https://mlsanity.com/paper/2011.00400/) (2020)
- [Human-Robot Mutual Adaptation in Shared Autonomy](https://mlsanity.com/paper/1701.07851/) (2017)
- [Mapless Navigation among Dynamics with Social-safety-awareness: a reinforcement learning approach from 2D laser scans](https://mlsanity.com/paper/1911.03074/) (2019)
- [Adaptive Model Predictive Control for High-Accuracy Trajectory Tracking in Changing Conditions](https://mlsanity.com/paper/1807.05290/) (2018)
- [Modeling, Identification and Control of Model Jet Engines for Jet Powered Robotics](https://mlsanity.com/paper/1909.13296/) (2019)
- [NimbRo Rescue: Solving Disaster-Response Tasks through Mobile Manipulation Robot Momaro](https://mlsanity.com/paper/1810.01345/) (2018)
- [Constrained Differential Dynamic Programming Revisited](https://mlsanity.com/paper/2005.00985/) (2020)
- [An Iterative LQR Controller for Off-Road and On-Road Vehicles using a Neural Network Dynamics Model](https://mlsanity.com/paper/2007.14492/) (2020)
- [Uncertainty-Constrained Differential Dynamic Programming in Belief Space for Vision Based Robots](https://mlsanity.com/paper/2012.00218/) (2020)
- [On orthogonal tensors and best rank-one approximation ratio](https://mlsanity.com/paper/1707.02569/) (2017)
- [Adaptive Robot Navigation with Collision Avoidance subject to 2nd-order Uncertain Dynamics](https://mlsanity.com/paper/2005.12599/) (2020)
- [Sequence-based Multimodal Apprenticeship Learning For Robot Perception and Decision Making](https://mlsanity.com/paper/1702.07475/) (2017)

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

- [Situationally-Aware Dynamics Learning](https://mlsanity.com/paper/2505.19574/) (2025)

## Abstract (arXiv)

Ground robots require the crucial capability of traversing unstructured and unprepared terrains and avoiding obstacles to complete tasks in real-world robotics applications such as disaster response. When a robot operates in off-road field environments such as forests, the robot's actual behaviors often do not match its expected or planned behaviors, due to changes in the characteristics of terrains and the robot itself. Therefore, the capability of robot adaptation for consistent behavior generation is essential for maneuverability on unstructured off-road terrains. In order to address the challenge, we propose a novel method of self-reflective terrain-aware adaptation for ground robots to generate consistent controls to navigate over unstructured off-road terrains, which enables robots to more accurately execute the expected behaviors through robot self-reflection while adapting to varying unstructured terrains. To evaluate our method's performance, we conduct extensive experiments using real ground robots with various functionality changes over diverse unstructured off-road terrains. The comprehensive experimental results have shown that our self-reflective terrain-aware adaptation method enables ground robots to generate consistent navigational behaviors and outperforms the compared previous and baseline techniques.