DeepAI AI Chat
Log In Sign Up

Learning Vision-based Reactive Policies for Obstacle Avoidance

by   Elie Aljalbout, et al.

In this paper, we address the problem of vision-based obstacle avoidance for robotic manipulators. This topic poses challenges for both perception and motion generation. While most work in the field aims at improving one of those aspects, we provide a unified framework for approaching this problem. The main goal of this framework is to connect perception and motion by identifying the relationship between the visual input and the corresponding motion representation. To this end, we propose a method for learning reactive obstacle avoidance policies. We evaluate our method on goal-reaching tasks for single and multiple obstacles scenarios. We show the ability of the proposed method to efficiently learn stable obstacle avoidance strategies at a high success rate, while maintaining closed-loop responsiveness required for critical applications like human-robot interaction.


page 7

page 13

page 14

page 15


Informed Circular Fields for Global Reactive Obstacle Avoidance of Robotic Manipulators

In this paper a global reactive motion planning framework for robotic ma...

APF-PF: Probabilistic Depth Perception for 3D Reactive Obstacle Avoidance

This paper proposes a framework for 3D obstacle avoidance in the presenc...

Obstacle avoidance using raycasting and Riemannian Motion Policies at kHz rates for MAVs

In this paper, we present a novel method for using Riemannian Motion Pol...

Online Learning for Obstacle Avoidance

We approach the fundamental problem of obstacle avoidance for robotic sy...

AvoidBench: A high-fidelity vision-based obstacle avoidance benchmarking suite for multi-rotors

Obstacle avoidance is an essential topic in the field of autonomous dron...

Reactive Motion Generation on Learned Riemannian Manifolds

In recent decades, advancements in motion learning have enabled robots t...