DynaGuide: Steering Diffusion Polices with Active Dynamic Guidance
Deploying large, complex policies in the real world requires the ability to
steer them to fit the needs of a situation. Most common steering approaches,
like goal-conditioning, require training the robot policy with a distribution
of test-time objectives in mind. To overcome this limitation, we present
DynaGuide, a steering method for diffusion policies using guidance from an
external dynamics model during the diffusion denoising process. DynaGuide
separates the dynamics model from the base policy, which gives it multiple
advantages, including the ability to steer towards multiple objectives, enhance
underrepresented base policy behaviors, and maintain robustness on low-quality
objectives. The separate guidance signal also allows DynaGuide to work with
off-the-shelf pretrained diffusion policies. We demonstrate the performance and
features of DynaGuide against other steering approaches in a series of
simulated and real experiments, showing an average steering success of 70% on a
set of articulated CALVIN tasks and outperforming goal-conditioning by 5.4x
when steered with low-quality objectives. We also successfully steer an
off-the-shelf real robot policy to express preference for particular objects
and even create novel behavior. Videos and more can be found on the project
website: https://dynaguide.github.io
6852c1cf8dd3b4a7bdb8d9a9
Maximilian Du
extracted_confirmed
2025-06-18T15:41:45.280Z
64e153cf75fae2212e8a140c
https://cdn-avatars.huggingface.co/v1/production/uploads/64e153cf75fae2212e8a140c/qvhUNND1ENTUImH-WpiHC.jpeg
Maximilian Du
user
MaxDu
6852c1cf8dd3b4a7bdb8d9aa
Shuran Song
DynaGuide: Steering Diffusion Polices with Active Dynamic Guidance
Deploying large, complex policies in the real world requires the ability to
steer them to fit the needs of a situation. Most common steering approaches,
like goal-conditioning, require training the robot policy with a distribution
of test-time objectives in mind. To overcome this limitation, we present
DynaGuide, a steering method for diffusion policies using guidance from an
external dynamics model during the diffusion denoising process. DynaGuide
separates the dynamics model from the base policy, which gives it multiple
advantages, including the ability to steer towards multiple objectives, enhance
underrepresented base policy behaviors, and maintain robustness on low-quality
objectives. The separate guidance signal also allows DynaGuide to work with
off-the-shelf pretrained diffusion policies. We demonstrate the performance and
features of DynaGuide against other steering approaches in a series of
simulated and real experiments, showing an average steering success of 70% on a
set of articulated CALVIN tasks and outperforming goal-conditioning by 5.4x
when steered with low-quality objectives. We also successfully steer an
off-the-shelf real robot policy to express preference for particular objects
and even create novel behavior. Videos and more can be found on the project
website: https://dynaguide.github.io
DynaGuide, a steering method using an external dynamics model, enhances diffusion policies by allowing them to adapt to multiple objectives and maintain robustness, outperforming goal-conditioning especially with low-quality objectives.
diffusion policies guidance external dynamics model diffusion denoising process articulated CALVIN tasks goal-conditioning off-the-shelf real robot policy