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

RiEMann: Advancing $\mathrm{SE(3)}$-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 $\mathrm{SE(3)}$-equivariant robot manipulation, addresses these challenges by eschewing point cloud segmentation and directly predicting the target poses of objects for manipulation. By incorporating local $\mathrm{SE(3)}$-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 $\mathrm{SE(3)}$-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 $\mathrm{SE(3)}$-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 $\mathrm{SE(3)}$ 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 $\mathrm{SE(3)}$ 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 $\mathrm{SE(3)}$ equivariance and focusing computational resources where they are most needed, it sets a new benchmark for what is achievable in this challenging field.