OffRIPP: Offline RL-based Informative Path Planning
This addresses the safety and cost-efficiency challenges in robotics for tasks requiring data collection under resource constraints, though it is incremental as it builds on existing offline RL methods.
The paper tackles the problem of informative path planning in robotics by proposing an offline reinforcement learning framework that avoids real-time environment interactions during training, achieving superior performance and faster computation compared to baselines.
Informative path planning (IPP) is a crucial task in robotics, where agents must design paths to gather valuable information about a target environment while adhering to resource constraints. Reinforcement learning (RL) has been shown to be effective for IPP, however, it requires environment interactions, which are risky and expensive in practice. To address this problem, we propose an offline RL-based IPP framework that optimizes information gain without requiring real-time interaction during training, offering safety and cost-efficiency by avoiding interaction, as well as superior performance and fast computation during execution -- key advantages of RL. Our framework leverages batch-constrained reinforcement learning to mitigate extrapolation errors, enabling the agent to learn from pre-collected datasets generated by arbitrary algorithms. We validate the framework through extensive simulations and real-world experiments. The numerical results show that our framework outperforms the baselines, demonstrating the effectiveness of the proposed approach.