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

arXiv: https://arxiv.org/abs/2302.13935 · HTML: https://mlsanity.com/paper/2302.13935/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2023.

## Built on by (most cited first, 4 of 4)

- [DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation](https://mlsanity.com/paper/2403.07788/) (2024)
- [Generalizable Long-Horizon Manipulations with Large Language Models](https://mlsanity.com/paper/2310.02264/) (2023)
- [TieBot: Learning to Knot a Tie from Visual Demonstration through a Real-to-Sim-to-Real Approach](https://mlsanity.com/paper/2407.03245/) (2024)
- [Tube Diffusion Policy: Reactive Visual-Tactile Policy Learning for Contact-rich Manipulation](https://mlsanity.com/paper/2604.23609/) (2026)

## Abstract (arXiv)

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.