  Can we generate a control policy for an agent using just one demonstration of
desired behaviors as a prompt, as effortlessly as creating an image from a
textual description? In this paper, we present Make-An-Agent, a novel policy
parameter generator that leverages the power of conditional diffusion models
for behavior-to-policy generation. Guided by behavior embeddings that encode
trajectory information, our policy generator synthesizes latent parameter
representations, which can then be decoded into policy networks. Trained on
policy network checkpoints and their corresponding trajectories, our generation
model demonstrates remarkable versatility and scalability on multiple tasks and
has a strong generalization ability on unseen tasks to output well-performed
policies with only few-shot demonstrations as inputs. We showcase its efficacy
and efficiency on various domains and tasks, including varying objectives,
behaviors, and even across different robot manipulators. Beyond simulation, we
directly deploy policies generated by Make-An-Agent onto real-world robots on
locomotion tasks.
