  Robust learning from noisy demonstrations is a practical but highly
challenging problem in imitation learning. In this paper, we first
theoretically show that robust imitation learning can be achieved by optimizing
a classification risk with a symmetric loss. Based on this theoretical finding,
we then propose a new imitation learning method that optimizes the
classification risk by effectively combining pseudo-labeling with co-training.
Unlike existing methods, our method does not require additional labels or
strict assumptions about noise distributions. Experimental results on
continuous-control benchmarks show that our method is more robust compared to
state-of-the-art methods.
