  This paper focuses on inverse reinforcement learning (IRL) to enable safe and
efficient autonomous navigation in unknown partially observable environments.
The objective is to infer a cost function that explains expert-demonstrated
navigation behavior while relying only on the observations and state-control
trajectory used by the expert. We develop a cost function representation
composed of two parts: a probabilistic occupancy encoder, with recurrent
dependence on the observation sequence, and a cost encoder, defined over the
occupancy features. The representation parameters are optimized by
differentiating the error between demonstrated controls and a control policy
computed from the cost encoder. Such differentiation is typically computed by
dynamic programming through the value function over the whole state space. We
observe that this is inefficient in large partially observable environments
because most states are unexplored. Instead, we rely on a closed-form
subgradient of the cost-to-go obtained only over a subset of promising states
via an efficient motion-planning algorithm such as A* or RRT. Our experiments
show that our model exceeds the accuracy of baseline IRL algorithms in robot
navigation tasks, while substantially improving the efficiency of training and
test-time inference.
