  Imitation learning is a promising approach to help robots acquire dexterous
manipulation capabilities without the need for a carefully-designed reward or a
significant computational effort. However, existing imitation learning
approaches require sophisticated data collection infrastructure and struggle to
generalize beyond the training distribution. One way to address this limitation
is to gather additional data that better represents the full operating
conditions. In this work, we investigate characteristics of such additional
demonstrations and their impact on performance. Specifically, we study the
effects of corrective and randomly-sampled additional demonstrations on
learning a policy that guides a five-fingered robot hand through a
pick-and-place task. Our results suggest that corrective demonstrations
considerably outperform randomly-sampled demonstrations, when the proportion of
additional demonstrations sampled from the full task distribution is larger
than the number of original demonstrations sampled from a restrictive training
distribution. Conversely, when the number of original demonstrations are higher
than that of additional demonstrations, we find no significant differences
between corrective and randomly-sampled additional demonstrations. These
results provide insights into the inherent trade-off between the effort
required to collect corrective demonstrations and their relative benefits over
randomly-sampled demonstrations. Additionally, we show that inexpensive
vision-based sensors, such as LeapMotion, can be used to dramatically reduce
the cost of providing demonstrations for dexterous manipulation tasks. Our code
is available at
https://github.com/GT-STAR-Lab/corrective-demos-dexterous-manipulation.
