# Explore Where It Matters: Targeted Exploration for Efficient Rover Adaptation

**Authors:** Jacob Levy, Arthur Bouton, Tristan D. Hassler, Hari Nayar, David Fridovich-Keil  
**Lab:** California Institute of Technology · **Published:** 28 Oct 2026  
**Links:** [arXiv](https://arxiv.org/abs/2610.38657) · [PDF](https://arxiv.org/pdf/2610.38657) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2610.38657.html)

## Key numbers

- **90%**: TERRA reaches approximately 90% slope-climbing success within five trials under severe out-of-distribution pretraining conditions

## Abstract

Future planetary rovers will traverse steep, deformable terrain where slip, sinkage, and terrain deformation can threaten mobility. Although simulation can capture some wheel-soil effects, unmodeled deformable-terrain phenomena create sim-to-real gaps requiring deployment-time adaptation. Yet, rover operations leave little room for extensive trial-and-error. We introduce TERRA (Targeted Exploration for Reward-Relevant Adaptation), an online adaptation framework that efficiently seeks deployment data exposing task-relevant model errors and corrects them. TERRA adapts a simulation-trained world model with a reward-weighted prediction loss and trains a loss predictor to estimate where future performance-relevant errors are likely to occur, using this signal to guide exploration during MPC. In simulation, TERRA reaches approximately 90% slope-climbing success within five trials under severe out-of-distribution pretraining conditions, outperforming exploration baselines in reward, completion time, and energy to first success. On the ERNEST rover, TERRA recovers coordinated active-suspension locomotion on a deformable Mars-simulant slope within three to five real-world trials. Project website: https://terra-explore.github.io

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