Kavli Affiliate: Wei Gao
| First 5 Authors: Bo Ai, Wei Gao, Vinay, David Hsu,
| Summary:
How can a robot navigate successfully in a rich and diverse environment,
indoors or outdoors, along an office corridor or a trail in the park, on the
flat ground, the staircase, or the elevator, etc.? To this end, this work aims
at three challenges: (i) complex visual observations, (ii) partial
observability of local sensing, and (iii) multimodal navigation behaviors that
depend on both the local environment and the high-level goal. We propose a
novel neural network (NN) architecture to represent a local controller and
leverage the flexibility of the end-to-end approach to learn a powerful policy.
To tackle complex visual observations, we extract multiscale spatial
information through convolution layers. To deal with partial observability, we
encode rich history information in LSTM-like modules. Importantly, we integrate
the two into a single unified architecture that exploits convolutional memory
cells to track the observation history at multiple spatial scales, which can
capture the complex spatiotemporal dependencies between observations and
controls. We additionally condition the network on the high-level goal in order
to generate different navigation behavior modes. Specifically, we propose to
use independent memory cells for different modes to prevent mode collapse in
the learned policy. We implemented the NN controller on the SPOT robot and
evaluate it on three challenging tasks with partial observations: adversarial
pedestrian avoidance, blind-spot obstacle avoidance, and elevator riding. Our
model significantly outperforms CNNs, conventional LSTMs, or the ablated
versions of our model. A demo video will be publicly available, showing our
SPOT robot traversing many different locations on our university campus.
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