Deep Reinforcement Learning for Single-Shot Diagnosis and Adaptation in Damaged Robots
This work addresses the challenge of robotic adaptation to damage in industrial and social applications, representing an incremental improvement by combining diagnosis and policy learning.
The paper tackles the problem of enabling robots to adapt to various damages during mission-critical tasks by proposing a damage-aware control architecture that diagnoses damage in a single trial and uses a single policy for adaptation, achieving robust performance with minimal computational complexity.
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.