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Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation (2306.17817v2)

Published 30 Jun 2023 in cs.RO, cs.AI, and cs.LG

Abstract: 3D perceptual representations are well suited for robot manipulation as they easily encode occlusions and simplify spatial reasoning. Many manipulation tasks require high spatial precision in end-effector pose prediction, which typically demands high-resolution 3D feature grids that are computationally expensive to process. As a result, most manipulation policies operate directly in 2D, foregoing 3D inductive biases. In this paper, we introduce Act3D, a manipulation policy transformer that represents the robot's workspace using a 3D feature field with adaptive resolutions dependent on the task at hand. The model lifts 2D pre-trained features to 3D using sensed depth, and attends to them to compute features for sampled 3D points. It samples 3D point grids in a coarse to fine manner, featurizes them using relative-position attention, and selects where to focus the next round of point sampling. In this way, it efficiently computes 3D action maps of high spatial resolution. Act3D sets a new state-of-the-art in RL-Bench, an established manipulation benchmark, where it achieves 10% absolute improvement over the previous SOTA 2D multi-view policy on 74 RLBench tasks and 22% absolute improvement with 3x less compute over the previous SOTA 3D policy. We quantify the importance of relative spatial attention, large-scale vision-language pre-trained 2D backbones, and weight tying across coarse-to-fine attentions in ablative experiments. Code and videos are available on our project website: https://act3d.github.io/.

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Authors (4)
  1. Theophile Gervet (13 papers)
  2. Zhou Xian (17 papers)
  3. Nikolaos Gkanatsios (9 papers)
  4. Katerina Fragkiadaki (61 papers)
Citations (48)
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Summary

  • The paper presents Act3D, a novel framework using 3D feature field transformers to achieve robust, high-precision robotic manipulation with state-of-the-art performance.
  • It introduces a coarse-to-fine sampling strategy that efficiently focuses computational resources on key 3D regions, enhancing spatial precision.
  • The spatially adaptive attention mechanism enables flexible inference and improved task success while significantly lowering computational costs.

Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation

The paper introduces Act3D, a novel framework for robotic manipulation that leverages the strengths of 3D feature field transformers to address the challenges posed by high-resolution 3D action map prediction. The development of robotic manipulation policies capable of reliably and efficiently handling a wide variety of tasks remains an active area of research. Traditional methods have often relied on 2D image processing pipelines, which, while effective in terms of computational resources, fall short when high spatial precision is required. The Act3D framework addresses this shortcoming by adopting a 3D representation of the robot's workspace, enhanced by coarse-to-fine sampling methodologies, and a spatially adaptive attention mechanism.

Key Contributions

  • 3D Perceptual Representation: Act3D leverages the inherent advantages of 3D representations, such as better handling of occlusions and enabling more sophisticated spatial reasoning capabilities. This shift is driven by the necessity for high spatial precision in predicting the robot's end-effector pose following 6-DoF manipulation tasks.
  • Coarse-to-Fine Sampling: The model employs a novel coarse-to-fine strategy to sample 3D points, which mitigates the computational overhead typically associated with high-resolution 3D feature grids. This method allows Act3D to efficiently focus computational resources on areas of interest, hence improving the spatial precision of the generated 3D action maps.
  • Spatially Adaptive Attention: Through the use of spatially adaptive attention operations, the model learns to predict continuous resolution 3D action maps. This enhances the model's capability to handle various manipulation tasks with greater accuracy by dynamically adjusting the resolution of the 3D feature field based on task-specific needs.

Empirical Validation

Act3D establishes a new state-of-the-art on the RLBench benchmark, demonstrating significant improvements over previous 2D and 3D methodologies. Specifically, Act3D achieves a 10% absolute improvement over the best-performing 2D multi-view policy and a 22% improvement over the leading 3D policy in terms of task success rates, while also utilizing three times less computational resources. These results underline the efficacy of Act3D in handling complex manipulation scenarios.

Technical Insights

  • Integration with Pre-Trained Models: Act3D effectively utilizes pre-trained 2D features by lifting them into a 3D feature space using sensed depth. This integration not only augments the model's performance but also streamlines the training process by leveraging existing robust visual feature extractors.
  • Equivariant Transformations: An important aspect of Act3D is its design principle of maintaining spatial equivariance. This design ensures that the model is robust to changes in the spatial arrangement of inputs, a critical feature for policies that need to generalize across different camera views and environmental configurations.
  • Flexible Inference: The Act3D framework allows for a flexible trade-off between computational resources and spatial precision during inference. This is achieved by altering the number of sampled 3D points, thus providing practical adaptability in computationally constrained environments.

Implications and Future Directions

The introduction of Act3D signifies a pivotal step forward in the deployment of transformers within robotic manipulation contexts, particularly for tasks requiring nuanced spatial reasoning and precision. The demonstrated improvements in task success rates and computational efficiency open up potential applications across various sectors, such as automation and manufacturing, where complex manipulations are routine.

Looking forward, future work could explore the integration of hierarchical task decomposition into the Act3D framework. By structuring tasks into subtasks, the model could potentially achieve greater efficiency and adaptability in task execution. Furthermore, extending the adaptability of Act3D to real-world hardware configurations, beyond the robust simulation demonstrations, will be a critical step toward widespread applicability in automated systems. The exploration of reinforcement learning paradigms and their integration with Act3D could also offer enhanced learning capabilities and further reduce the sample complexity of training in diverse scenarios.

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