# Probabilistic Learning of Torque Controllers from Kinematic and Force Constraints

arXiv: https://arxiv.org/abs/1712.07249 · HTML: https://mlsanity.com/paper/1712.07249/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2017.

## Built on by (most cited first, 7 of 7)

- [Imitation Learning for Nonprehensile Manipulation through Self-Supervised Learning Considering Motion Speed](https://mlsanity.com/paper/2206.10289/) (2022)
- [Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives](https://mlsanity.com/paper/1903.02114/) (2019)
- [Learning from demonstration with model-based Gaussian process](https://mlsanity.com/paper/1910.05005/) (2019)
- [Gaussian-Process-based Robot Learning from Demonstration](https://mlsanity.com/paper/2002.09979/) (2020)
- [A Linearly Constrained Nonparametric Framework for Imitation Learning](https://mlsanity.com/paper/1909.07374/) (2019)
- [Imitation learning for variable speed motion generation over multiple actions](https://mlsanity.com/paper/2103.06466/) (2021)
- [A Non-parametric Skill Representation with Soft Null Space Projectors for Fast Generalization](https://mlsanity.com/paper/2209.08522/) (2022)

## Abstract (arXiv)

When learning skills from demonstrations, one is often required to think in advance about the appropriate task representation (usually in either operational or configuration space). We here propose a probabilistic approach for simultaneously learning and synthesizing torque control commands which take into account task space, joint space and force constraints. We treat the problem by considering different torque controllers acting on the robot, whose relevance is learned probabilistically from demonstrations. This information is used to combine the controllers by exploiting the properties of Gaussian distributions, generating new torque commands that satisfy the important features of the task. We validate the approach in two experimental scenarios using 7-DoF torquecontrolled manipulators, with tasks that require the consideration of different controllers to be properly executed.