  Robotics has proved to be an indispensable tool in many industrial as well as
social applications, such as warehouse automation, manufacturing, disaster
robotics, etc. In most of these scenarios, damage to the agent while
accomplishing mission-critical tasks can result in failure. To enable robotic
adaptation in such situations, the agent needs to adopt policies which are
robust to a diverse set of damages and must do so with minimum computational
complexity. We thus propose a damage aware control architecture which diagnoses
the damage prior to gait selection while also incorporating domain
randomization in the damage space for learning a robust policy. To implement
damage awareness, we have used a Long Short Term Memory based supervised
learning network which diagnoses the damage and predicts the type of damage.
The main novelty of this approach is that only a single policy is trained to
adapt against a wide variety of damages and the diagnosis is done in a single
trial at the time of damage.
