Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planning

TL;DR

This paper introduces a deep reinforcement learning-based viewpoint recommendation policy that guides trajectory planning for efficient information gathering, balancing computational speed and environmental uncertainty reduction.

Contribution

It presents a novel, safety-aware, deep RL approach that guides local trajectory optimization, outperforming greedy policies and matching MCTS performance with much faster execution.

Findings

Outperforms greedy next-best-view policies in simulation.

Achieves similar information gain and coverage time as MCTS.

Reduces execution time by three orders of magnitude.

Abstract

Search missions require motion planning and navigation methods for information gathering that continuously replan based on new observations of the robot's surroundings. Current methods for information gathering, such as Monte Carlo Tree Search, are capable of reasoning over long horizons, but they are computationally expensive. An alternative for fast online execution is to train, offline, an information gathering policy, which indirectly reasons about the information value of new observations. However, these policies lack safety guarantees and do not account for the robot dynamics. To overcome these limitations we train an information-aware policy via deep reinforcement learning, that guides a receding-horizon trajectory optimization planner. In particular, the policy continuously recommends a reference viewpoint to the local planner, such that the resulting dynamically feasible and collision-free trajectories lead to observations that maximize the information gain and reduce the uncertainty about the environment. In simulation tests in previously unseen environments, our method consistently outperforms greedy next-best-view policies and achieves competitive performance compared to Monte Carlo Tree Search, in terms of information gains and coverage time, with a reduction in execution time by three orders of magnitude.