Robots should prioritize curiosity-driven exploration when the environment or task structure makes extrinsic reward signals sparse, deceptive, or costly to obtain. Reinforcement learning foundations articulated by Richard Sutton at University of Alberta emphasize learning from reward but also highlight the exploration-exploitation tradeoff; where explicit rewards are insufficient, intrinsic motivations can guide discovery of useful behaviors. Pioneering theoretical work by Jürgen Schmidhuber at IDSIA formalized curiosity as seeking reductions in prediction error, and empirical methods such as the Intrinsic Curiosity Module developed by Pathak, Agrawal, Efros, and Darrell at UC Berkeley show practical gains in sparse-reward benchmarks.
Theoretical foundations
Curiosity provides an internal objective that encourages agents to visit novel or informative states, improving representation learning and long-term performance when external rewards fail to shape useful policies. Research by Pierre-Yves Oudeyer at Inria on intrinsic motivation in developmental robotics connects these mechanisms to human-like staged learning, where exploration scaffolds later goal-directed skills. In practice, curiosity complements reward maximization rather than replacing it: intrinsic drives can bootstrap capabilities that later allow efficient exploitation of task rewards.
Practical criteria for prioritizing curiosity
Prioritize curiosity-driven exploration when the task has sparse or delayed rewards, the environment is highly novel or non-stationary, or when sample efficiency in simulation is affordable relative to real-world costs. Consequences include faster discovery of reward-yielding strategies and more robust behavior under distributional shift, but also risks: unchecked novelty-seeking can produce unsafe or culturally insensitive actions in human environments, increase energy use, or damage sensitive ecosystems. Deployment in inhabited or protected territories therefore requires constraints informed by local cultural norms, legal frameworks, and environmental stewardship.
Curiosity is especially valuable during early training phases, transfer learning between domains, and in research settings focused on open-ended skill acquisition. As training converges or in safety-critical operations, agents should increasingly weight extrinsic rewards and constraints derived from human oversight or formal safety specifications. Combining intrinsic and extrinsic objectives with risk-aware filters produces systems that learn broadly while respecting human values and territorial sensitivities, aligning technical performance with ethical and environmental considerations.