taskadaptation

Task adaptation, sometimes written as taskadaptation, is the process by which a learning system adjusts its behavior to perform new tasks or operate under conditions that differ from those encountered during initial training. It aims to reuse existing representations and policies to minimize retraining, data collection, and computational cost. Task adaptation is a broad concept that encompasses adjustments across perception, decision-making, and action.

Common approaches include transfer learning, where a model trained on one set of tasks is fine-tuned on a related task; meta-learning, where the system learns how to learn new tasks quickly; and few-shot learning, which operates with very limited task data. Efficient adaptation often relies on modular architectures, adapters, or task-conditioned components that inject task context without altering the entire model. In reinforcement learning, task adaptation can be achieved by conditioning the policy on a task descriptor or by continual learning to update capabilities while preserving prior skills.

Task adaptation is used in robotics for adapting to new manipulation tasks or novel environments; in natural language processing for task-specific prompts and multi-task learning; in computer vision for recognizing new object categories with few examples; in recommender systems for changing user intents; and in healthcare for adjusting to new patient populations or protocols.

Key evaluation criteria include adaptation speed, data efficiency, and generalization to unseen tasks. Challenges include catastrophic forgetting of earlier tasks, negative transfer, and safety concerns when new tasks introduce risks. Additional difficulties arise from distribution shifts between training and deployment, limited labeled data, and the need for interpretable behavior.

Task adaptation intersects with domain adaptation, transfer learning, lifelong learning, and meta-learning. It is distinct from, yet complementary to, learning-to-learn approaches that optimize an agent's ability to acquire new skills. Ethical and deployment considerations emphasize transparency and governance.