  Training novice users to operate an excavator for learning different skills
requires the presence of expert teachers. Considering the complexity of the
problem, it is comparatively expensive to find skilled experts as the process
is time-consuming and requires precise focus. Moreover, since humans tend to be
biased, the evaluation process is noisy and will lead to high variance in the
final score of different operators with similar skills. In this work, we
address these issues and propose a novel strategy for the automatic evaluation
of excavator operators. We take into account the internal dynamics of the
excavator and the safety criterion at every time step to evaluate the
performance. To further validate our approach, we use this score prediction
model as a source of reward for a reinforcement learning agent to learn the
task of maneuvering an excavator in a simulated environment that closely
replicates the real-world dynamics. For a policy learned using these external
reward prediction models, our results demonstrate safer solutions following the
required dynamic constraints when compared to policy trained with task-based
reward functions only, making it one step closer to real-life adoption. For
future research, we release our codebase at
https://github.com/pranavAL/InvRL_Auto-Evaluate and video results
https://drive.google.com/file/d/1jR1otOAu8zrY8mkhUOUZW9jkBOAKK71Z/view?usp=share_link .
