Transformer-Enhanced Motion Planner: Attention-Guided Sampling for State-Specific Decision Making

TL;DR

This paper introduces TEMP, a novel deep learning framework that enhances sampling-based motion planning by integrating semantic environmental encoding and attention-guided decision making, leading to improved efficiency and generalizability.

Contribution

The work presents a new Transformer-based motion planning approach combining semantic environmental encoding with attention mechanisms, advancing the state-of-the-art in sampling efficiency and adaptability.

Findings

TEMP outperforms existing SBMP algorithms in path quality and efficiency.

The model demonstrates strong generalization across diverse planning tasks.

Semantic encoding improves environmental understanding for motion planning.

Abstract

Sampling-based motion planning (SBMP) algorithms are renowned for their robust global search capabilities. However, the inherent randomness in their sampling mechanisms often result in inconsistent path quality and limited search efficiency. In response to these challenges, this work proposes a novel deep learning-based motion planning framework, named Transformer-Enhanced Motion Planner (TEMP), which synergizes an Environmental Information Semantic Encoder (EISE) with a Motion Planning Transformer (MPT). EISE converts environmental data into semantic environmental information (SEI), providing MPT with an enriched environmental comprehension. MPT leverages an attention mechanism to dynamically recalibrate its focus on SEI, task objectives, and historical planning data, refining the sampling node generation. To demonstrate the capabilities of TEMP, we train our model using a dataset comprised of planning results produced by the RRT*. EISE and MPT are collaboratively trained, enabling EISE to autonomously learn and extract patterns from environmental data, thereby forming semantic representations that MPT could more effectively interpret and utilize for motion planning. Subsequently, we conducted a systematic evaluation of TEMP's efficacy across diverse task dimensions, which demonstrates that TEMP achieves exceptional performance metrics and a heightened degree of generalizability compared to state-of-the-art SBMPs.