title: Make-An-Agent: A Generalizable Policy Network Generator with
    Behavior-Prompted Diffusion
summary: 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.
paper_authors._id: 6695ea8d461de4eea59b32a0
paper_authors.name: Yongyuan Liang
paper_authors._id: 6695ea8d461de4eea59b32a1
paper_authors.name: Tingqiang Xu
paper_authors._id: 6695ea8d461de4eea59b32a2
paper_authors.name: Kaizhe Hu
paper_authors._id: 6695ea8d461de4eea59b32a3
paper_authors.name: Guangqi Jiang
paper_authors._id: 6695ea8d461de4eea59b32a4
paper_authors.name: Furong Huang
paper_authors._id: 6695ea8d461de4eea59b32a5
paper_authors.name: Huazhe Xu
paper_title: Make-An-Agent: A Generalizable Policy Network Generator with
    Behavior-Prompted Diffusion
paper_summary: 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.
paper_ai_keywords: conditional diffusion models behavior embeddings trajectory information latent parameter representations policy networks few-shot demonstrations generalization ability real-world robots locomotion tasks