  Large-scale endeavors like RT-1 and widespread community efforts such as
Open-X-Embodiment have contributed to growing the scale of robot demonstration
data. However, there is still an opportunity to improve the quality, quantity,
and diversity of robot demonstration data. Although vision-language models have
been shown to automatically generate demonstration data, their utility has been
limited to environments with privileged state information, they require
hand-designed skills, and are limited to interactions with few object
instances. We propose Manipulate-Anything, a scalable automated generation
method for real-world robotic manipulation. Unlike prior work, our method can
operate in real-world environments without any privileged state information,
hand-designed skills, and can manipulate any static object. We evaluate our
method using two setups. First, Manipulate-Anything successfully generates
trajectories for all 5 real-world and 12 simulation tasks, significantly
outperforming existing methods like VoxPoser. Second, Manipulate-Anything's
demonstrations can train more robust behavior cloning policies than training
with human demonstrations, or from data generated by VoxPoser and
Code-As-Policies. We believe Manipulate-Anything can be the scalable method for
both generating data for robotics and solving novel tasks in a zero-shot
setting.
