Reward-Free Continual Adaptation for Resilient Space Robots

The evolution of space robotics has transitioned from pre-programmed control to adaptive learning-based systems. Early methods relied on handcrafted control laws, but these struggled in unstructured environments. Deep reinforcement learning (RL), exemplified by algorithms like DDPG and SAC, introduced the ability for robots to learn complex behaviors from data. Recent models such as DreamerV3 have demonstrated high sample efficiency by learning latent representations that encode environment dynamics and rewards. However, deploying these models in space faces challenges: the absence of external reward signals, environmental uncertainties, and hardware degradation. Prior works have addressed domain shifts using domain randomization and offline fine-tuning, but continuous adaptation without external rewards remains unresolved. This gap limits long-term autonomy in space missions, where manual retraining is costly and impractical.

The core issue is enabling space robots to adapt autonomously after severe hardware failures without relying on external reward signals. Hardware degradation causes dynamics shifts that invalidate pre-trained policies. Existing RL approaches depend on explicit reward feedback, which is often unavailable in space due to limited sensing and tracking. This results in a critical bottleneck: how to maintain operational performance and safety in the face of unpredictable failures, with minimal human intervention. The challenge is compounded by resource constraints, requiring methods that are both sample-efficient and computationally feasible for space-grade hardware. Addressing this problem is essential for the future of resilient autonomous space systems.

The key innovation is leveraging a pre-trained latent world model to encode a reward landscape, enabling reward-free online adaptation. The approach involves: • Pretraining the DreamerV3 model on diverse simulated environments with domain randomization, capturing environment dynamics and reward signals in the latent space. • During deployment, freezing the encoder and reward head, and only updating the transition dynamics via KL divergence loss, based on unsupervised rollouts. • Training the policy entirely on imagined trajectories generated by the updated model, allowing rapid adaptation without external rewards. This method maintains the integrity of the reward landscape while adjusting to new dynamics, addressing the challenge of unobservable rewards in space robotics.

• �� Pretraining: The model is trained on multiple simulated environments with domain randomization, optimizing the RSSM components—sequence model, encoder, decoder, reward predictor, and continuity predictor—using joint loss functions including KL divergence. • During deployment:• Freeze the encoder and reward predictor to preserve the latent reward landscape.• Update only the transition dynamics by minimizing KL divergence between the posterior and prior distributions of the latent states.• Collect simulated trajectories via rollouts with added exploration noise.• Re-train the policy within the imagined trajectories, iteratively refining the policy to adapt to new dynamics.• Use a small learning rate for the world model to prevent catastrophic forgetting.• Limit adaptation to 60 minutes, simulating resource constraints.• Evaluate performance against baselines including zero-shot, reward-informed, and scratch-trained policies.

Experiments involve three simulated tasks: planetary traversal with wheel failure, orbital navigation with thruster failure, and precision assembly with tool misalignment. The models are pretrained on diverse environments with randomized physical parameters. Deployment introduces severe failures, and adaptation is limited to 60 minutes. Performance metrics include success rate, task completion time, and robustness across three random seeds. The models are compared with zero-shot policies, policies trained with true rewards, and from-scratch training. Results show rapid performance recovery, with the reward-free model reaching 70-80% of reward-informed upper bounds, significantly outperforming zero-shot baselines. The experiments validate the approach's robustness across failure modes and environments.

The reward-free approach achieves performance recovery within 60 minutes, reaching 70-80% of the reward-supervised upper bound, with performance improvements over 70% compared to zero-shot policies. It demonstrates strong robustness across failure modes like wheel lock, thruster failure, and tool misalignment. The models maintain stability and generalization, confirming the latent reward landscape's effectiveness for guiding adaptation. Compared to from-scratch training, it reduces sample complexity and accelerates recovery, showing promise for real-world space applications where data collection is costly and limited.

This framework is directly applicable to autonomous space robots operating in environments with unpredictable hardware failures. It enables rapid, reward-free adaptation, reducing reliance on external feedback and human intervention. Suitable for planetary rovers, orbital servicing, and lunar construction robots, it can enhance mission resilience and longevity. Long-term, it supports fully autonomous deep space exploration, where real-time adaptation to unforeseen failures is critical. The approach also opens avenues for AI systems in other resource-constrained, high-stakes environments such as underwater or disaster response robotics.

The method's performance diminishes over extended periods due to latent space drift, impacting long-term stability. It relies heavily on simulation-based pretraining, which may not fully capture real-world complexities, leading to sim-to-real transfer challenges. The online update process is computationally intensive, potentially exceeding space-grade hardware capabilities. Future work should focus on localized latent-space adaptation, transfer robustness, and computational efficiency to address these limitations and enable practical deployment in real space missions.