Make-An-Agent: A Generalizable Policy Network Generator with
  Behavior-Prompted Diffusion
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.
6695ea8d461de4eea59b32a0
Yongyuan Liang
6695ea8d461de4eea59b32a1
Tingqiang Xu
6695ea8d461de4eea59b32a2
Kaizhe Hu
6695ea8d461de4eea59b32a3
Guangqi Jiang
6695ea8d461de4eea59b32a4
Furong Huang
6695ea8d461de4eea59b32a5
Huazhe Xu
Make-An-Agent: A Generalizable Policy Network Generator with
  Behavior-Prompted Diffusion
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.
Make-An-Agent uses conditional diffusion models to generate policies with a single behavioral demonstration, demonstrating versatility and generalization across tasks and robot platforms.
conditional diffusion models behavior embeddings latent parameter representations policy networks policy checkpoints few-shot demonstrations generalization real-world robots locomotion tasks