Core Concepts
DiffClone, a diffusion-based behavior cloning agent, effectively solves complex robot manipulation tasks from offline data by capturing complex action distributions and preserving their multi-modality.
Abstract
The paper introduces DiffClone, a framework for enhancing behavior cloning in robotics using diffusion-driven policy learning. The key highlights are:
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Data Preprocessing:
- Use a MoCo-finetuned ResNet50 as the visual encoder backbone.
- Restrict the dataset to high-reward trajectories for better performance.
- Normalize the observations to enhance policy stability.
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Diffusion Policy for Robot Behavior:
- Leverage Denoising Diffusion Probabilistic Models (DDPMs) to capture complex action distributions.
- Iteratively refine actions through gradient-guided exploration, enabling robust execution.
- Achieve superior performance compared to standard behavior cloning and offline RL methods.
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Experiments and Results:
- Extensive ablation studies on architectural choices and hyperparameters for the diffusion policy.
- Achieve high scores in simulation, but observe sensitivity to hyperparameters for real-world transfer.
- Plan to explore DDIM for improved latency and regularization techniques for robust real-world deployment.
The authors open-source their work and provide a project website with working videos of the trained policies.
Stats
The dataset consists of over 1.26 million images of robot actions in 1895 trajectories of scooping data and 1003 trajectories of pouring data.
Quotes
"Offline data from various robotics hardware, increases the diversity of the dataset and also its size as more data leads to better training of the current models."
"Diffusion Policy utilizes the effectiveness of DDPMs in visuomotor policy learning, and the action gradient-guided exploration of state space to achieve the best-performing agent, demonstrating remarkable improvements over existing offline and imitation methods."