Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions (2010.15335v3)

Abstract: Earlier work has shown that reusing experience from prior motion planning problems can improve the efficiency of similar, future motion planning queries. However, for robots with many degrees-of-freedom, these methods exhibit poor generalization across different environments and often require large datasets that are impractical to gather. We present SPARK and FLAME , two experience-based frameworks for sampling-based planning applicable to complex manipulators in 3 D environments. Both combine samplers associated with features from a workspace decomposition into a global biased sampling distribution. SPARK decomposes the environment based on exact geometry while FLAME is more general, and uses an octree-based decomposition obtained from sensor data. We demonstrate the effectiveness of SPARK and FLAME on a Fetch robot tasked with challenging pick-and-place manipulation problems. Our approaches can be trained incrementally and significantly improve performance with only a handful of examples, generalizing better over diverse tasks and environments as compared to prior approaches.

• The paper introduces novel spark and flame frameworks that integrate prior experiences to inform sampling distributions in high-dimensional motion planning.
• It leverages geometric and sensor-derived decompositions to pinpoint challenging regions, significantly reducing planning times and improving solution quality with limited training data.
• Evaluations on an eight-DOF Fetch robot demonstrate robust performance and effective generalization in dynamic, unstructured environments.

Experience-Based Sampling Distributions for Motion Planning

This paper presents novel frameworks, spark and flame, for enhancing sampling-based motion planning through experience-based learning. The frameworks address the need for improved efficiency in high-dimensional robotic control, particularly for complex manipulators operating in three-dimensional environments. The primary contribution lies in integrating prior experiences to inform sampling distributions, strategically targeting "challenging regions" in configuration space, which typically hinder motion planning due to their low probability of being sampled.

Methodology Overview

The paper's central focus is on leveraging workspace information to guide the sampling process. The authors introduce two approaches: spark, which utilizes exact geometric decompositions, and flame, which employs sensor-derived octree decompositions, adaptable to environments where precise models are unavailable. Both methodologies rely on deconstructing the workspace into local primitives—distinct features or combinations of geometric objects—each associated with "challenging regions" where motion planning typically struggles.

• spark Framework: Utilizes pairs of geometric primitives (e.g., boxes) to represent workspace features. This method assumes access to precise geometric information, leveraging a criticality test that computes proximity between robot configurations and workspace obstacles.
• flame Framework: Uses octree-based occupancy information derived from sensors to create local primitives called "octoboxes." This technique facilitates application in less structured and more dynamic environments.

Each framework employs a criticality test to associate key configurations from previously successful planning paths with relevant local primitives. These configurations inform local samplers—biased sampling distributions configured as Gaussian mixtures—that enhance the exploration of key configuration space regions. A spatial database of these local samplers is incrementally built, enabling quick retrieval of relevant samplers for new tasks based on workspace similarities.

Evaluation and Results

The effectiveness of spark and flame is rigorously evaluated across several scenarios using a Fetch robot with eight degrees of freedom. Key metrics in the evaluation include improvement in planning time and solution quality, especially under workspace variations that impose different configurations. The frameworks outperform traditional sampling-based planners and other learning-based methods, such as Thunder and CVAE, particularly in environments with dynamic obstacles and variable geometries.

Notably, the paper underscores the frameworks' ability to generalize effectively with a limited number of examples, demonstrating convergence in performance with relatively small datasets—approximately 300 to 600 training examples. Moreover, the frameworks are shown to facilitate significant performance enhancements, markedly reducing planning times in environments with complex geometry and high-dimensional configuration spaces. Additionally, both frameworks exhibit robust performance when transferring learning across similar but distinct tasks, highlighting their potential for practical deployment in dynamic and unstructured environments.

Implications and Future Directions

The implications of this research are substantial for robotics applications requiring efficient motion planning in high-dimensional, dynamic settings. The paper suggests that experience-based sampling can be a robust alternative to traditional methods reliant on uniform distributions, providing enhanced efficiency and reliability in complex environments. The modular nature of the frameworks suggests potential adaptability to a wider range of robots and tasks beyond those demonstrated.

In future research, further exploration into unified metrics for evaluating and selecting local primitives could enhance the frameworks' general applicability. Additionally, expanding the frameworks' capabilities to handle even more significant workspace variance, possibly through integration with more sophisticated sensory inputs or learning paradigms, represents a promising avenue to address more diverse robotic challenges. Overall, the research represents a meaningful advancement in motion planning, with practical implications for deploying robotic systems in real-world applications.