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@@ -216,8 +216,9 @@ We tackle the problem of perceptive locomotion in dynamic environments. In this
   We introduce a control hierarchy where the high-level controller, trained with imitation learning, sets navigation commands and the low-level gait controller, trained with reinforcement learning, realizes the target commands through joint-space actuation. This combination enables us to effectively deploy the entire hierarchy on quadrupedal robots in real-world environments.
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 <table align=center width=800px><tr><td> <p align="justify" width="20%">The high-level navigation policy generates the target velocity command at 10Hz from the onboard RGB-D camera observation and robot heading. The target velocity command, including linear and angular velocities, is used as input to the low-level gait controller along with the buffer of recent robot states. The low-level gait policy predicts the joint-space actions as the desired joint positions at 38Hz and sends them to the quadruped robot for actuation. More implementation details can be found in <a href="https://github.com/UT-Austin-RPL/PRELUDE/blob/main/implementation.md">this page</a>.
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 <h1 align="center">Deploying in Unseen Environments</h1>
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