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3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations (2403.03954v7)

Published 6 Mar 2024 in cs.RO, cs.CV, and cs.LG

Abstract: Imitation learning provides an efficient way to teach robots dexterous skills; however, learning complex skills robustly and generalizablely usually consumes large amounts of human demonstrations. To tackle this challenging problem, we present 3D Diffusion Policy (DP3), a novel visual imitation learning approach that incorporates the power of 3D visual representations into diffusion policies, a class of conditional action generative models. The core design of DP3 is the utilization of a compact 3D visual representation, extracted from sparse point clouds with an efficient point encoder. In our experiments involving 72 simulation tasks, DP3 successfully handles most tasks with just 10 demonstrations and surpasses baselines with a 24.2% relative improvement. In 4 real robot tasks, DP3 demonstrates precise control with a high success rate of 85%, given only 40 demonstrations of each task, and shows excellent generalization abilities in diverse aspects, including space, viewpoint, appearance, and instance. Interestingly, in real robot experiments, DP3 rarely violates safety requirements, in contrast to baseline methods which frequently do, necessitating human intervention. Our extensive evaluation highlights the critical importance of 3D representations in real-world robot learning. Videos, code, and data are available on https://3d-diffusion-policy.github.io .

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Authors (6)
  1. Yanjie Ze (20 papers)
  2. Gu Zhang (33 papers)
  3. Kangning Zhang (7 papers)
  4. Chenyuan Hu (1 paper)
  5. Muhan Wang (6 papers)
  6. Huazhe Xu (93 papers)
Citations (47)
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Summary

Overview of "3D Diffusion Policy"

The paper "3D Diffusion Policy" presents a novel approach to visual imitation learning, leveraging the integration of 3D visual representations with diffusion policies. This research addresses the challenge of learning complex robotic skills with limited demonstrations, focusing on enhancing generalizability and efficiency.

Key Contributions

The authors introduce the 3D Diffusion Policy (DP3), an imitation learning framework that uses compact 3D visual representations derived from sparse point clouds. The point clouds are encoded using a simple, yet effective MLP-based encoder, which efficiently processes the 3D data into a form suitable for the diffusion policy backbone.

Key features of this method include:

  • Efficiency: DP3 demonstrates significant improvements over existing 2D-based methods, achieving a 55.3% relative enhancement in task success rates.
  • Generalizability: The framework showcases strong generalization across various scenarios, including variations in spatial configuration, viewpoint, appearance, and object instances.
  • Safety: Remarkably, DP3 maintains adherence to safety requirements in real-world robotic tasks, minimizing the need for human intervention.

Experimental Evaluation

The paper comprehensively evaluates DP3 across 72 simulated tasks and 4 real-world tasks, focusing on diverse applications from dexterous manipulation to mobile and humanoid robotics. The simulation tasks span multiple domains and include both high-dimensional and low-dimensional control challenges.

Numerical Results

In simulation, DP3 achieved superior results, handling most tasks with minimal demonstrations. Notably, it required only 10 demonstrations to outperform baselines with a significant margin. Moreover, in real-world experiments, DP3 attained an 85% success rate across tasks like manipulation of deformable objects, utilizing just 40 demonstrations per task.

Theoretical and Practical Implications

The integration of 3D representations with diffusion policies emphasizes the importance of spatial understanding in robotics. The success of DP3 highlights the inadequacy of traditional 2D approaches, particularly in tasks requiring complex spatial reasoning.

This research potentially shifts the paradigm towards 3D-based learning frameworks in robotics, encouraging further exploration of compact and efficient 3D representation methods.

Future Directions

Future work could delve into optimizing 3D representation techniques and extending DP3's capabilities to address even longer-horizon tasks. Moreover, investigating the applicability of DP3 across other domains in robotics and its integration with emerging technologies could catalyze advancements in visual imitation learning.

In conclusion, the "3D Diffusion Policy" paper showcases a significant step forward in the field of imitation learning, offering a well-grounded framework that advances both theoretical understanding and practical implementations in robotic learning systems.

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  1. 3D Diffusion Policy
  2. GitHub - YanjieZe/3D-Diffusion-Policy: [arXiv 2024] 3D Diffusion Policy (253 stars)
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