# Learning Generalisable Coupling Terms for Obstacle Avoidance via Low-dimensional Geometric Descriptors

## TL;DR

This paper introduces a hierarchical framework that uses low-dimensional geometric descriptors and learning techniques to enable robots to perform robust, generalisable obstacle avoidance in dynamic, real-world environments.

## Contribution

It presents a novel hierarchical approach combining learning and geometric descriptors for reactive obstacle avoidance, improving robustness and generalisation.

## Key findings

- Effective in synthetic environments and real-world robot experiments.
- Robustness and generalisation outperform existing methods.
- Suitable for real-time obstacle avoidance in unpredictable scenarios.

## Abstract

Unforeseen events are frequent in the real-world environments where robots are expected to assist, raising the need for fast replanning of the policy in execution to guarantee the system and environment safety. Inspired by human behavioural studies of obstacle avoidance and route selection, this paper presents a hierarchical framework which generates reactive yet bounded obstacle avoidance behaviours through a multi-layered analysis. The framework leverages the strengths of learning techniques and the versatility of dynamic movement primitives to efficiently unify perception, decision, and action levels via low-dimensional geometric descriptors of the environment. Experimental evaluation on synthetic environments and a real anthropomorphic manipulator proves that the robustness and generalisation capabilities of the proposed approach regardless of the obstacle avoidance scenario makes it suitable for robotic systems in real-world environments.