D-Cubed: Latent Diffusion Trajectory Optimisation for Dexterous Deformable Manipulation
(2403.12861)Abstract
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. To optimise a trajectory for a target task, we introduce a novel gradient-free guided sampling method that employs the Cross-Entropy method within the reverse diffusion process. In particular, D-Cubed samples a small number of noisy skill trajectories using the LDM for exploration and evaluates the trajectories in simulation. Then, D-Cubed selects the trajectory with the lowest cost for the subsequent reverse process. This effectively explores promising solution areas and optimises the sampled trajectories towards a target task throughout the reverse diffusion process. Through empirical evaluation on a public benchmark of dexterous deformable object manipulation tasks, we demonstrate that D-Cubed outperforms traditional trajectory optimisation and competitive baseline approaches by a significant margin. We further demonstrate that trajectories found by D-Cubed readily transfer to a real-world LEAP hand on a folding task.
Overview
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D-Cubed introduces a novel trajectory optimisation strategy using a latent diffusion model (LDM) aimed at improving dexterous manipulation of deformable objects.
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The methodology leverages skill latents encoded from short-horizon actions into long-horizon skill trajectories, optimized through a unique gradient-free guided sampling method.
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Empirical evaluations show D-Cubed outperforming existing trajectory optimisation and reinforcement learning methods in six dexterous deformable object manipulation tasks.
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The approach presents a significant advancement in robotics, with potential for broader applications and future research in both improved computational methods and real-world applications.
D-Cubed: Enhanced Trajectory Optimisation for Dexterous Manipulation via Latent Diffusion Models
Introduction
In recent years, dexterous manipulation of deformable objects has emerged as a critical challenge in robotics, requiring advancements beyond the capabilities of traditional parallel grippers. Existing trajectory optimisation methods often fall short in addressing the complexity of such tasks, limited by the vast search space and the sparse task information from cost functions. In our exploration, we introduce D-Cubed, a novel trajectory optimisation methodology that employs a latent diffusion model (LDM) trained on a task-agnostic play dataset to address dexterous deformable object manipulation. This approach is distinguished by its ability to leverage skill latents encoded from short-horizon actions, which are then composed into skill trajectories to represent long-horizon actions. A unique aspect of D-Cubed is its innovative gradient-free guided sampling method, utilizing the Cross-Entropy method within the reverse diffusion process for trajectory optimisation.
Methodology
D-Cubed is built upon a variational autoencoder (VAE) that encodes short-horizon action sequences into a skill-latent space, using data from a play dataset containing various hand motions. These skill latents are composed by an LDM into skill trajectories, which are subsequently optimised for specific tasks. The gradient-free guided sampling strategy crucially enables the exploration of promising solution areas by sampling and evaluating trajectories in simulation, refining towards the target task throughout the reverse diffusion process. This method stands out for its capacity to generate diverse, meaningful trajectories of robot hand motions, facilitating efficient exploration in complex manipulation tasks.
Empirical Evaluation
Our empirical evaluation on a public benchmark suite featuring six dexterous deformable object manipulation tasks demonstrates D-Cubed's superior performance over existing trajectory optimisation and reinforcement learning approaches. The method significantly outperforms traditional optimisation techniques and RL models, showcasing its effective exploration and exploitation capabilities in complex manipulation scenarios. Additionally, ablation studies confirm the importance of the skill-latent space in performance improvement and detail the effectiveness of various components of D-Cubed.
Implications and Future Directions
The introduction of D-Cubed represents a significant advancement in trajectory optimisation for dexterous manipulation tasks. By effectively leveraging latent skill representations and adopting a novel gradient-free guided sampling method, D-Cubed opens new avenues for research and application in robotic manipulation. The method's ability to explore and exploit large search spaces presents opportunities for tackling a wide range of complex manipulation tasks beyond deformable object handling. Future work may focus on extending D-Cubed's capabilities to more generalized manipulation tasks, improving computational efficiency, and exploring real-world applications. Additionally, further research into improving the transferability of trajectories from simulation to real-world environments could enhance the practical applicability of D-Cubed in robotics.
Conclusion
D-Cubed introduces a groundbreaking approach to trajectory optimisation for dexterous deformable object manipulation, leveraging latent diffusion models and a novel sampling method. Its success in efficiently exploring and optimizing trajectories in complex manipulation tasks marks a significant step forward in robotics research, with potential impacts across a broad spectrum of applications. As we continue to push the boundaries of what is achievable in robotic manipulation, D-Cubed offers a promising pathway for future advancements.
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