Explore Where It Matters: Targeted Exploration for Efficient Rover Adaptation
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
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
TERRA introduces a targeted exploration framework for efficient online adaptation of rover world models. The paper presents a rigorous and impactful solution to the sim-to-real gap in planetary rover mobility, combining reward-weighted dynamics learning with a loss-predictor-guided exploration strategy that demonstrates significant efficiency gains in both simulation and real-world hardware trials.
Methodology
The paper introduces TERRA, a framework for online adaptation of simulation-trained world models for planetary rovers. The core methodological contributions are twofold: (1) a reward-weighted prediction loss that prioritizes model errors in observation dimensions that have high sensitivity to the task reward, and (2) a learned "loss predictor" that forecasts where these reward-relevant errors are likely to occur in candidate action sequences, thereby guiding exploration within a Model Predictive Control (MPC) loop. The use of Low-Rank Adaptation (LoRA) for efficient onboard updating of the dynamics model is a practical engineering choice. The approach is logically sound, addressing the specific bottleneck of sim-to-real gaps in deformable terrain where standard prediction-error-based exploration is inefficient.
Results & evaluation
The evaluation is strong, featuring both extensive simulation studies (100 seeds, multiple OOD pretraining conditions) and real-world validation on the ERNEST rover. The simulation results show significant improvements over baselines like RND and Plan2Explore in terms of success rate, completion time, and energy efficiency. The real-world results are particularly compelling, demonstrating the recovery of coordinated active-suspension locomotion within 3-5 trials on a Mars-simulant slope, a task that is critical for future missions. The ablation studies effectively isolate the contributions of the reward-weighted loss and the targeted exploration.
Reproducibility
The paper provides detailed descriptions of the simulation environment, pretraining conditions, and hyperparameters. However, as is common in robotics papers involving proprietary hardware (ERNEST) and specific simulators (DARTS), full reproducibility by external groups may be limited. The project website is provided, which likely contains code or further details.
Limitations
The method relies on the availability of a task reward during deployment, which may not always be easily defined or observable in complex real-world scenarios. The choice of loss-bin thresholds for the loss predictor is a hyperparameter that requires tuning. The evaluation is limited to a single rover platform (ERNEST) and a specific type of terrain (deformable slopes), so generalizability to other platforms or terrain types is not fully established.
Broader impact
This work has high relevance to the field of planetary robotics and autonomous systems operating in unstructured environments. The technique of reward-weighted model adaptation and targeted exploration is applicable beyond rovers to any mobile robot facing sim-to-real gaps in contact-rich or deformable environments. It provides a practical pathway for deploying learned policies in high-stakes, low-trial-count scenarios. TERRA introduces a targeted exploration framework for efficient online adaptation of rover world models. The paper presents a rigorous and impactful solution to the sim-to-real gap in planetary rover mobility, combining reward-weighted dynamics learning with a loss-predictor-guided exploration strategy that demonstrates significant efficiency gains in both simulation and real-world hardware trials.
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