  Robot learning tasks are extremely compute-intensive and hardware-specific.
Thus the avenues of tackling these challenges, using a diverse dataset of
offline demonstrations that can be used to train robot manipulation agents, is
very appealing. The Train-Offline-Test-Online (TOTO) Benchmark provides a
well-curated open-source dataset for offline training comprised mostly of
expert data and also benchmark scores of the common offline-RL and behaviour
cloning agents. In this paper, we introduce DiffClone, an offline algorithm of
enhanced behaviour cloning agent with diffusion-based policy learning, and
measured the efficacy of our method on real online physical robots at test
time. This is also our official submission to the Train-Offline-Test-Online
(TOTO) Benchmark Challenge organized at NeurIPS 2023. We experimented with both
pre-trained visual representation and agent policies. In our experiments, we
find that MOCO finetuned ResNet50 performs the best in comparison to other
finetuned representations. Goal state conditioning and mapping to transitions
resulted in a minute increase in the success rate and mean-reward. As for the
agent policy, we developed DiffClone, a behaviour cloning agent improved using
conditional diffusion.
