D-Cubed: Latent Diffusion Trajectory Optimisation for Dexterous Deformable Manipulation

Understand

Mastering dexterous robotic manipulation of deformable objects is vital for overcoming the limitations of parallel grippers in real-world applications.

• Current trajectory optimisation approaches often struggle to solve such tasks due to the large search space and the limited task information available from a cost function.
• In this work, we propose D-Cubed, a novel trajectory optimisation method using a latent diffusion model (LDM) trained from a task-agnostic play dataset to solve dexterous deformable object manipulation tasks.
• D-Cubed learns a skill-latent space that encodes short-horizon actions in the play dataset using a VAE and trains a LDM to compose the skill latents into a skill trajectory, representing a long-horizon action trajectory in the dataset.