# RangedIK: An Optimization-based Robot Motion Generation Method for Ranged-Goal Tasks

## TL;DR

RangedIK introduces a real-time optimization-based method for robot motion generation that effectively handles tasks with specific goals, ranges, or preferences, enabling smooth and feasible motions in complex scenarios.

## Contribution

It presents a unified framework that incorporates range-based tasks using barrier methods within a weighted-sum optimization, improving flexibility and task accommodation.

## Key findings

- Outperforms state-of-the-art methods in simulations
- Enables smooth, feasible motions on a physical robot
- Effectively handles multiple task categories simultaneously

## Abstract

Generating feasible robot motions in real-time requires achieving multiple tasks (i.e., kinematic requirements) simultaneously. These tasks can have a specific goal, a range of equally valid goals, or a range of acceptable goals with a preference toward a specific goal. To satisfy multiple and potentially competing tasks simultaneously, it is important to exploit the flexibility afforded by tasks with a range of goals. In this paper, we propose a real-time motion generation method that accommodates all three categories of tasks within a single, unified framework and leverages the flexibility of tasks with a range of goals to accommodate other tasks. Our method incorporates tasks in a weighted-sum multiple-objective optimization structure and uses barrier methods with novel loss functions to encode the valid range of a task. We demonstrate the effectiveness of our method through a simulation experiment that compares it to state-of-the-art alternative approaches, and by demonstrating it on a physical camera-in-hand robot that shows that our method enables the robot to achieve smooth and feasible camera motions.