EquiBot: SIM(3)-Equivariant Diffusion Policy for Generalizable and Data Efficient Learning
(2407.01479)Abstract
Building effective imitation learning methods that enable robots to learn from limited data and still generalize across diverse real-world environments is a long-standing problem in robot learning. We propose EquiBot, a robust, data-efficient, and generalizable approach for robot manipulation task learning. Our approach combines SIM(3)-equivariant neural network architectures with diffusion models. This ensures that our learned policies are invariant to changes in scale, rotation, and translation, enhancing their applicability to unseen environments while retaining the benefits of diffusion-based policy learning such as multi-modality and robustness. We show in a suite of 6 simulation tasks that our proposed method reduces the data requirements and improves generalization to novel scenarios. In the real world, we show with in total 10 variations of 6 mobile manipulation tasks that our method can easily generalize to novel objects and scenes after learning from just 5 minutes of human demonstrations in each task.
Overview
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The paper presents EquiBot, a novel approach combining SIM(3)-equivariant neural network architectures with diffusion models to improve data efficiency and generalizability in robot manipulation tasks.
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The method leverages SIM(3)-equivariant PointNet++ for feature extraction and a SIM(3)-equivariant U-net for denoising in policy learning, resulting in robust performance across both simulated and real-world tasks.
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Empirical evaluations show that EquiBot outperforms baseline methods in terms of generalization and data efficiency, successfully handling diverse and unseen task conditions in both simulated and real-world environments.
Overview of SIM(3)-Equivariant Diffusion Policy for Generalizable and Data Efficient Learning
The paper titled "EquiBot: SIM(3)-Equivariant Diffusion Policy for Generalizable and Data Efficient Learning" introduces a significant advancement in the field of imitation learning for robot manipulation. This work proposes a novel approach combining SIM(3)-equivariant neural network architectures with diffusion models to achieve robust, data-efficient, and generalizable policy learning for various robot manipulation tasks.
Key Contributions
- SIM(3)-Equivariant Network Architecture: The authors present a specialized network architecture that integrates SIM(3) equivariance into the policy learning process. SIM(3) includes transformations such as scaling, rotation, and translation. The designed network guarantees that outputs scale, translate, and rotate with the inputs, enabling generalizable policies.
- Diffusion Models in Policy Learning: The integration of diffusion models enhances the robustness and multi-modality of the learned policies. These models aid in predicting actions under various transformations, maintaining the stability and diversity of the generative process.
- Empirical Evaluation on Diverse Tasks: The method is tested on both simulated and real-world tasks. In simulation, tasks include box closing, cloth folding, object covering, and push T tasks. Real-world evaluations encompass daily tasks such as pushing a chair, luggage packing and closing, laundry door closing, and bimanual folding and making of beds.
Methodology
The proposed method leverages a combination of SIM(3)-equivariant PointNet++ for encoding point cloud observations and a SIM(3)-equivariant U-net for denoising diffusion steps. The PointNet++ encoder extracts features that remain consistent under translation, rotation, and scaling. The policy is then trained using a diffusion process, where the noise prediction network is adjusted to ensure each step in the denoising process adheres to SIM(3)-equivariant properties.
Numerical Results
- Simulation Results: In a suite of four simulated tasks, EquiBot demonstrated superior generalization performance beyond the training data distributions, outperforming three baselines (vanilla Diffusion Policy (DP), DP with augmentations, and EquivAct). Notably, the method maintained high performance under significant perturbations of task conditions.
- Data Efficiency: On benchmark tasks (Robomimic Can and Square), EquiBot showed better data efficiency, retaining performance even when trained on a reduced dataset size.
- Real-World Tasks: In real-world evaluations, EquiBot successfully generalized to new objects and task setups, achieving markedly higher success rates than the baseline methods. For example, the method successfully handled novel objects in the Luggage Packing task and unseen poses and rotations in difficult laundry door closing and bimanual manipulation tasks.
Implications and Future Work
The theoretical implications of this research signify a substantial step towards making robot manipulation more practical and adaptive to dynamic environments. The practical utility of EquiBot lies in its ability to generalize from minimal amounts of human demonstration data, thus significantly reducing the cost and time required for data collection in real-world settings.
Future research could explore the integration of nonlinear transformations and dynamics variations into the training regime, addressing limitations related to handling objects with new shapes or different physical properties not observed during training.
Conclusion
The presented method, EquiBot, underpins substantial progress in imitation learning for robot manipulation, offering improvements in both generalization to unseen environments and data efficiency. These advancements pave the way toward more versatile and adaptive robotic systems capable of performing a wide range of tasks with minimal demonstration effort.
This overview encapsulates the primary contributions, methodology, results, and implications of the paper, providing a clear and concise insight into the advances made by the authors in the domain of robot learning.