SE(3) Neural Potential Fields for 6-DoF Trajectory Planning Directly from Images Without Explicit 3D Reconstruction

Problem

This work addresses the limitations of conventional 3D reconstruction methods for collision-free trajectory planning in cluttered environments. The authors highlight that existing techniques often struggle with real-time performance and accuracy when navigating complex scenes, necessitating a more efficient approach. This paper is a preprint and has not undergone peer review.

Method

The authors propose an SE(3) neural potential field architecture that directly utilizes posed RGB images as input data. The system is supervised by a navigation function based on the geodesic distance to the target grasp. The training mechanism involves learning from images, allowing the model to generalize effectively to various environments. Key performance metrics include:

• Convergence: The model achieves convergence within 3 cm of the grasp.
• Collision-Free Paths: It generates paths that are collision-free when compared to ground-truth geometry.
• Mean Clearance: The mean clearance from obstacles is reported as 8.6-8.8 cm, significantly improving upon previous methods.
• Arm-Link Contacts: The model reduces arm-link contacts to 2.7-5.5% of executed configurations.
• Execution Time: The planning process takes approximately 2 seconds, demonstrating a substantial speed advantage over traditional methods.

Results

The proposed method shows significant improvements over baseline techniques:

• Mean Clearance: 8.6-8.8 cm compared to under 1 cm when using image supervision alone.
• Arm-Link Contacts: 2.7-5.5% versus 20.6-50.4% of executed configurations in traditional methods.
• Grasp Success Rate: Achieves a grasp success rate of 90.0% in one scene and 40.0% in another, indicating variability based on scene complexity.
• Planning Time: The planning time of 2 seconds is a marked improvement over RRT*, which takes 67-133 seconds when relying on a reconstruction.

Limitations

The authors note that residual failures are primarily due to refusals from the Cartesian executor rather than limitations of the neural potential field itself. This suggests that while the model is effective, there are still challenges in the execution phase that need to be addressed. Additionally, the paper does not discuss the scalability of the approach to more complex environments or the potential impact of varying image quality on performance.

Why it matters

This research has significant implications for robotics and autonomous systems, particularly in environments where rapid and accurate navigation is critical. By eliminating the need for explicit 3D reconstruction, the proposed method can enhance real-time decision-making capabilities in dynamic settings. This approach could pave the way for more robust applications in fields such as autonomous driving, drone navigation, and robotic manipulation, where efficiency and safety are paramount.