# An Open Tele-Impedance Framework to Generate Data for Contact-Rich Tasks in Robotic Manipulation

arXiv: https://arxiv.org/abs/2209.10486 · HTML: https://mlsanity.com/paper/2209.10486/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2022.

## Built on by (most cited first, 1 of 1)

- [Learning Variable Compliance Control From a Few Demonstrations for Bimanual Robot with Haptic Feedback Teleoperation System](https://mlsanity.com/paper/2406.14990/) (2024)

## Abstract (arXiv)

Using large datasets in machine learning has led to outstanding results, in some cases outperforming humans in tasks that were believed impossible for machines. However, achieving human-level performance when dealing with physically interactive tasks, e.g., in contact-rich robotic manipulation, is still a big challenge. It is well known that regulating the Cartesian impedance for such operations is of utmost importance for their successful execution. Approaches like reinforcement Learning (RL) can be a promising paradigm for solving such problems. More precisely, approaches that use task-agnostic expert demonstrations to bootstrap learning when solving new tasks have a huge potential since they can exploit large datasets. However, existing data collection systems are expensive, complex, or do not allow for impedance regulation. This work represents a first step towards a data collection framework suitable for collecting large datasets of impedance-based expert demonstrations compatible with the RL problem formulation, where a novel action space is used. The framework is designed according to requirements acquired after an extensive analysis of available data collection frameworks for robotics manipulation. The result is a low-cost and open-access tele-impedance framework which makes human experts capable of demonstrating contact-rich tasks.