# ILoSA: Interactive Learning of Stiffness and Attractors

arXiv: https://arxiv.org/abs/2103.03099 · HTML: https://mlsanity.com/paper/2103.03099/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 36 references that are in the map.
- First posted on arXiv 4 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [HG-DAgger: Interactive Imitation Learning with Human Experts](https://mlsanity.com/paper/1810.02890/) (2018)
- [DART: Noise Injection for Robust Imitation Learning](https://mlsanity.com/paper/1703.09327/) (2017)
- [LESS is More: Rethinking Probabilistic Models of Human Behavior](https://mlsanity.com/paper/2001.04465/) (2020)
- [Bayesian Gaussian mixture model for robotic policy imitation](https://mlsanity.com/paper/1904.10716/) (2019)

## Built on by (most cited first, 12 of 12)

- [Interactive Imitation Learning of Bimanual Movement Primitives](https://mlsanity.com/paper/2210.16220/) (2022)
- [Learning to Pick at Non-Zero-Velocity from Interactive Demonstrations](https://mlsanity.com/paper/2110.04534/) (2021)
- [Interactive incremental learning of generalizable skills with local trajectory modulation](https://mlsanity.com/paper/2409.05655/) (2024)
- [Impedance Primitive-augmented Hierarchical Reinforcement Learning for Sequential Tasks](https://mlsanity.com/paper/2508.19607/) (2025)
- [A Unifying Variational Framework for Gaussian Process Motion Planning](https://mlsanity.com/paper/2309.00854/) (2023)
- [ILeSiA: Interactive Learning of Robot Situational Awareness from Camera Input](https://mlsanity.com/paper/2409.20173/) (2024)
- [Robotic Fabric Flattening with Wrinkle Direction Detection](https://mlsanity.com/paper/2303.04909/) (2023)
- [Robotic Grasping of Harvested Tomato Trusses Using Vision and Online Learning](https://mlsanity.com/paper/2309.17170/) (2023)
- [A context-adaptive policy framework for robust and reactive robotic manipulation via uncertainty-aware imitation learning](https://mlsanity.com/paper/2410.24035/) (2024)
- [Deep-Learning Control of Lower-Limb Exoskeletons via simplified Therapist Input](https://mlsanity.com/paper/2412.07959/) (2024)
- [Addressing Ambiguity in Imitation Learning through Product of Experts based Negative Feedback](https://mlsanity.com/paper/2603.26467/) (2026)
- [User-Driven Learning from Demonstration: A Trajectory and Impedance Learning Method](https://mlsanity.com/paper/2607.16998/) (2026)

## Abstract (arXiv)

Teaching robots how to apply forces according to our preferences is still an open challenge that has to be tackled from multiple engineering perspectives. This paper studies how to learn variable impedance policies where both the Cartesian stiffness and the attractor can be learned from human demonstrations and corrections with a user-friendly interface. The presented framework, named ILoSA, uses Gaussian Processes for policy learning, identifying regions of uncertainty and allowing interactive corrections, stiffness modulation and active disturbance rejection. The experimental evaluation of the framework is carried out on a Franka-Emika Panda in four separate cases with unique force interaction properties: 1) pulling a plug wherein a sudden force discontinuity occurs upon successful removal of the plug, 2) pushing a box where a sustained force is required to keep the robot in motion, 3) wiping a whiteboard in which the force is applied perpendicular to the direction of movement, and 4) inserting a plug to verify the usability for precision-critical tasks in an experimental validation performed with non-expert users.