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OffRIPP: Offline RL-based Informative Path Planning

Srikar Babu Gadipudi, Srujan Deolasee, Siva Kailas, Wenhao Luo, Katia Sycara, Woojun Kim

TL;DR

This work addresses informative path planning (IPP) under resource constraints by introducing OffRIPP, an offline RL-based IPP framework that trains solely on pre-collected datasets to maximize information gain without environment interaction during training. OffRIPP leverages batch-constrained reinforcement learning with a behavior policy approximator and a Q-function, integrating a GP-based environment model and graph attention to propagate information across a PRM-structured search space. The framework can be plugged into existing online IPP methods (e.g., CAtNIPP, vashisth2024deep) and demonstrates superior performance and fast planning in both 2D light-intensity and 3D fruit identification tasks, including a real-robot experiment. These results illustrate the practical value of offline RL for safe, cost-effective IPP deployment in real-world robotics and lay groundwork for extending to multi-agent IPP scenarios.

Abstract

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.

OffRIPP: Offline RL-based Informative Path Planning

TL;DR

This work addresses informative path planning (IPP) under resource constraints by introducing OffRIPP, an offline RL-based IPP framework that trains solely on pre-collected datasets to maximize information gain without environment interaction during training. OffRIPP leverages batch-constrained reinforcement learning with a behavior policy approximator and a Q-function, integrating a GP-based environment model and graph attention to propagate information across a PRM-structured search space. The framework can be plugged into existing online IPP methods (e.g., CAtNIPP, vashisth2024deep) and demonstrates superior performance and fast planning in both 2D light-intensity and 3D fruit identification tasks, including a real-robot experiment. These results illustrate the practical value of offline RL for safe, cost-effective IPP deployment in real-world robotics and lay groundwork for extending to multi-agent IPP scenarios.

Abstract

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.
Paper Structure (23 sections, 5 equations, 4 figures, 2 tables)

This paper contains 23 sections, 5 equations, 4 figures, 2 tables.

Table of Contents

  1. INTRODUCTION
  2. Background and Related works
  3. Offline Reinforcement Learning
  4. Informative Path Planning
  5. Our approach
  6. Problem Formulation
  7. Offline RL-based Informative path planning
  8. Behavior Policy Approximation
  9. Policy Learning
  10. Network Architecture.
  11. EXPERIMENTS
  12. Experiment Setup
  13. Light-intensity Environment
  14. 3D Fruit Identification
  15. Training Details
  16. ...and 8 more sections

Figures (4)

  • Figure 1: Overview of the flow for the proposed framework: training an RL policy using a dataset generated by arbitrary algorithms, followed by deployment in the test environment.
  • Figure 2: The architecture of OffRIPP: A graph augmented by environment modeling (the output of GP) and the remaining budget are used as input. The approximated behavior policy and Q-function are used to determine an action.
  • Figure 3: Three experiments settings: (a) Heatmap representing light intensity. (b) Green stars and blue structures represent the target fruits and tree, respectively. (c) Real-world experiment featuring a robot (dotted circle) with the intensity map projected onto the arena.
  • Figure 4: Performance of OffRIPP with respect to various sizes of the dataset trained on the expert dataset and tested using a budget of 10 in the light-intensity environment. A lower value on the Y-axis is better. RAOr does not require a dataset since it is a non-learning method.