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RiEMann: Near Real-Time SE(3)-Equivariant Robot Manipulation without Point Cloud Segmentation (2403.19460v2)

Published 28 Mar 2024 in cs.RO and cs.AI

Abstract: We present RiEMann, an end-to-end near Real-time SE(3)-Equivariant Robot Manipulation imitation learning framework from scene point cloud input. Compared to previous methods that rely on descriptor field matching, RiEMann directly predicts the target poses of objects for manipulation without any object segmentation. RiEMann learns a manipulation task from scratch with 5 to 10 demonstrations, generalizes to unseen SE(3) transformations and instances of target objects, resists visual interference of distracting objects, and follows the near real-time pose change of the target object. The scalable action space of RiEMann facilitates the addition of custom equivariant actions such as the direction of turning the faucet, which makes articulated object manipulation possible for RiEMann. In simulation and real-world 6-DOF robot manipulation experiments, we test RiEMann on 5 categories of manipulation tasks with a total of 25 variants and show that RiEMann outperforms baselines in both task success rates and SE(3) geodesic distance errors on predicted poses (reduced by 68.6%), and achieves a 5.4 frames per second (FPS) network inference speed. Code and video results are available at https://riemann-web.github.io/.

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Citations (3)
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Summary

  • The paper presents a novel SE(3)-equivariant framework that directly predicts target poses without relying on point cloud segmentation.
  • It introduces an innovative action space design using type-0 and type-1 vectors, achieving a 68.6% reduction in SE(3) geodesic errors.
  • Extensive evaluations demonstrate that RiEMann generalizes robustly to cluttered environments with near real-time performance at 5.4 fps.

RiEMann: Advancing SE(3)\mathrm{SE(3)}-Equivariant Robot Manipulation with Imitation Learning

Introduction to RiEMann

In the domain of robotics, mastering manipulation tasks demands both precision and adaptability. Traditional approaches have encountered obstacles in terms of data efficiency and generalization, particularly in dynamic or cluttered environments. Leveraging the symmetries inherent in physical interactions can significantly enhance the learning process. RiEMann, a novel framework designed for SE(3)\mathrm{SE(3)}-equivariant robot manipulation, addresses these challenges by eschewing point cloud segmentation and directly predicting the target poses of objects for manipulation. By incorporating local SE(3)\mathrm{SE(3)}-equivariant models and a clever action space design, RiEMann demonstrates remarkable efficiency and adaptability, capable of learning from a minimal number of demonstrations and generalizing to unseen transformations and object instances while maintaining near real-time responsiveness.

Key Contributions and Methodology

RiEMann presents several pivotal advancements in the field of robot manipulation:

  • Equivariant Action Space Design: RiEMann introduces an SE(3)\mathrm{SE(3)}-equivariant design for its action space that facilitates direct action predictions, including both translation and rotation, without resorting to descriptor field matching. This design uses type-$0$ vectors for target position prediction and type-$1$ vectors for orientation, streamlining the learning process.
  • Efficient and Scalable Learning Framework: Addressing computational constraints, RiEMann employs an SE(3)\mathrm{SE(3)}-invariant module to reduce the input's complexity by focusing on regions of interest. This module significantly optimizes computational resources, making the framework scalable and efficient.
  • Robustness to Variability: Through extensive testing, the RiEMann framework has proven itself robust against distractions from unrelated objects and capable of generalizing across different instances of target objects and their SE(3)\mathrm{SE(3)} transformations.

Evaluation and Results

RiEMann was rigorously evaluated in both simulated and real-world settings, demonstrating superior performance in a variety of tasks including "Mug on Rack", "Plane on Shelf", and "Turn Faucet". Notably, RiEMann achieved these results with as few as 5 to 10 demonstrations per task, outperforming baseline models in task success rates and significantly reducing SE(3)\mathrm{SE(3)} geodesic distance errors by 68.6%. Furthermore, it operates at an impressive 5.4 frames per second for network inference, highlighting its potential for real-time applications.

Implications and Future Directions

RiEMann's success suggests a promising direction for future research in robot manipulation. Its ability to efficiently learn and generalize from minimal demonstrations, while maintaining resistances to visual distractions, positions it as a valuable tool for a wide range of applications. Future work could explore extending RiEMann's capabilities to more complex manipulation tasks, including those involving articulated objects or multiple stages. Additionally, integrating RiEMann's approach with reinforcement learning could uncover new possibilities for adaptive and intelligent robotic systems.

RiEMann represents a significant step forward in the quest for efficient, generalizable, and real-time capable robot manipulation. By elegantly leveraging SE(3)\mathrm{SE(3)} equivariance and focusing computational resources where they are most needed, it sets a new benchmark for what is achievable in this challenging field.

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