  Building effective imitation learning methods that enable robots to learn
from limited data and still generalize across diverse real-world environments
is a long-standing problem in robot learning. We propose EquiBot, a robust,
data-efficient, and generalizable approach for robot manipulation task
learning. Our approach combines SIM(3)-equivariant neural network architectures
with diffusion models. This ensures that our learned policies are invariant to
changes in scale, rotation, and translation, enhancing their applicability to
unseen environments while retaining the benefits of diffusion-based policy
learning such as multi-modality and robustness. We show in a suite of 6
simulation tasks that our proposed method reduces the data requirements and
improves generalization to novel scenarios. In the real world, we show with in
total 10 variations of 6 mobile manipulation tasks that our method can easily
generalize to novel objects and scenes after learning from just 5 minutes of
human demonstrations in each task.
