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Render and Diffuse: Aligning Image and Action Spaces for Diffusion-based Behaviour Cloning (2405.18196v1)

Published 28 May 2024 in cs.RO, cs.AI, cs.CV, and cs.LG

Abstract: In the field of Robot Learning, the complex mapping between high-dimensional observations such as RGB images and low-level robotic actions, two inherently very different spaces, constitutes a complex learning problem, especially with limited amounts of data. In this work, we introduce Render and Diffuse (R&D) a method that unifies low-level robot actions and RGB observations within the image space using virtual renders of the 3D model of the robot. Using this joint observation-action representation it computes low-level robot actions using a learnt diffusion process that iteratively updates the virtual renders of the robot. This space unification simplifies the learning problem and introduces inductive biases that are crucial for sample efficiency and spatial generalisation. We thoroughly evaluate several variants of R&D in simulation and showcase their applicability on six everyday tasks in the real world. Our results show that R&D exhibits strong spatial generalisation capabilities and is more sample efficient than more common image-to-action methods.

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Authors (4)
  1. Vitalis Vosylius (9 papers)
  2. Younggyo Seo (25 papers)
  3. Jafar Uruç (2 papers)
  4. Stephen James (42 papers)
Citations (9)
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Summary

  • The paper introduces Render and Diffuse, a method that unifies high-dimensional image observations with low-level robotic actions to enhance sample efficiency.
  • It details three variants—R&D-A, R&D-I, and R&D-AI—that operate in action, image, and hybrid spaces to improve spatial generalization and control precision.
  • Empirical results show that R&D-AI achieves up to 52.1% success with 10 demonstrations, outperforming state-of-the-art behavior cloning techniques.

An Essay on "Render and Diffuse: Aligning Image and Action Spaces for Diffusion-based Behaviour Cloning"

The paper "Render and Diffuse: Aligning Image and Action Spaces for Diffusion-based Behaviour Cloning" introduces an innovative approach in robot learning that addresses the challenges of mapping high-dimensional RGB observations to low-level robotic actions, particularly in data-scarce environments. This method, termed Render and Diffuse (R&D), aims to enhance sample efficiency and spatial generalization by representing both observations and actions within a unified image space.

Methodological Innovation

The authors identify significant challenges in learning effective robotic policies directly from RGB images due to the disparity between their representational space and low-level action spaces. Traditional approaches often rely heavily on depth information and make hierarchical predictions, requiring accurate depth perception or a multi-step action determination process. Render and Diffuse bypasses these limitations by virtually rendering the robot's potential configurations based on prospective actions and embedding these renderings back into the observation space. This effectively simplifies the learning problem and introduces beneficial inductive biases.

Render and Diffuse Variants

Three core variants of the Render and Diffuse family are discussed:

  1. R&D-A (Action-space): This variant predicts the noise added to the ground truth actions directly in the action space, using the rendered action representations as input.
  2. R&D-I (Image-space): This variant predicts the denoising direction in the image space. It uses a per-pixel 3D flow expressed in the camera frame to update the rendered action representations iteratively.
  3. R&D-AI (Hybrid): This variant combines predictions in both image and action spaces, leveraging the strengths of each. It uses image-space predictions for the initial coarse denoising and action-space predictions for fine adjustments, enhancing both spatial alignment and action precision.

Empirical Evaluation

The paper provides a comprehensive performance comparison between these R&D variants and state-of-the-art behavior cloning methods, namely ACT and Diffusion Policy. The methods were tested on 11 tasks from the RLBench suite, with evaluations conducted in both standard and data-limited conditions. Additional experiments focused on spatial generalization within the convex hull of demonstrations, multi-task learning, and real-world task execution.

Numerical Results and Insights

Key findings highlight that:

  • Render and Diffuse variants consistently outperformed baselines on tasks requiring significant spatial reasoning and generalization. For example, in low-data regimes, R&D-AI achieved an average success rate of 52.1% with 10 demonstrations, compared to 35% and 32.1% for ACT and Diffusion Policy, respectively.
  • The combination of image and action space alignment (R&D-AI) provided the best results, particularly for tasks demanding precise low-level control or spatial understanding with visual distractors.
  • Simulated evaluations demonstrated that R&D variants could interpolate well within the demonstrated workspace, a critical capability for efficient robotic learning from limited data.

In real-world settings, R&D also demonstrated robust performance on tasks like opening boxes, placing objects, and manipulating drawers, showcasing its practical applicability in physical environments.

Implications and Future Directions

The significant implications of Render and Diffuse extend to both theoretical and practical domains. Theoretically, the unification of observation and action spaces within an image-centric framework introduces a paradigm where spatial reasoning is inherently simplified, augmenting both learning speed and policy accuracy. Practically, this enhances the feasibility of deploying robotic learning systems in real-world tasks where data collection is expensive and environments are visually complex.

Moving forward, future research directions include optimizing the computational efficiency of the Render and Diffuse process by exploring alternative network architectures and more advanced denoising schedules. Further extensions could involve integrating gripper actions more robustly within the rendered representations and augmenting the model's complexity to handle full-configuration predictions. Additionally, exploiting pre-trained foundational models for image understanding could further reduce the data requirements for training effective robotic policies.

Conclusion

"Render and Diffuse: Aligning Image and Action Spaces for Diffusion-based Behaviour Cloning" presents a compelling method for sample-efficient and generalizable robotic policy learning. By virtually rendering robotic actions and aligning them within the observation space, this approach effectively simplifies the learning problem and enhances the model's understanding of spatial action consequences. Empirical results validate its superiority over traditional methods, signalling a notable advancement in robot learning paradigms, especially for applications constrained by data availability.

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