# Learning body models: from humans to humanoids

arXiv: https://arxiv.org/abs/2211.03049 · HTML: https://mlsanity.com/paper/2211.03049/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 96 references that are in the map.
- First posted on arXiv 6 Nov 2022.

## Builds on (most cited first, 8 of 8)

- [Active Learning of Inverse Models with Intrinsically Motivated Goal Exploration in Robots](https://mlsanity.com/paper/1301.4862/) (2013)
- [Sensorimotor representation learning for an "active self" in robots: A model survey](https://mlsanity.com/paper/2011.12860/) (2020)
- [Safe physical HRI: Toward a unified treatment of speed and separation monitoring together with power and force limiting](https://mlsanity.com/paper/1908.03046/) (2019)
- [Robots as Powerful Allies for the Study of Embodied Cognition from the Bottom Up](https://mlsanity.com/paper/1801.04819/) (2018)
- [Body models in humans, animals, and robots: mechanisms and plasticity](https://mlsanity.com/paper/2010.09325/) (2020)
- [The role of self-touch experience in the formation of the self](https://mlsanity.com/paper/1712.07843/) (2017)
- [3D Collision-Force-Map for Safe Human-Robot Collaboration](https://mlsanity.com/paper/2009.01036/) (2020)
- [Automatic self-contained calibration of an industrial dual-arm robot with cameras using self-contact, planar constraints, and self-observation](https://mlsanity.com/paper/2012.07548/) (2020)

## Abstract (arXiv)

Humans and animals excel in combining information from multiple sensory modalities, controlling their complex bodies, adapting to growth, failures, or using tools. These capabilities are also highly desirable in robots. They are displayed by machines to some extent. Yet, the artificial creatures are lagging behind. The key foundation is an internal representation of the body that the agent - human, animal, or robot - has developed. The mechanisms of operation of body models in the brain are largely unknown and even less is known about how they are constructed from experience after birth. In collaboration with developmental psychologists, we conducted targeted experiments to understand how infants acquire first "sensorimotor body knowledge". These experiments inform our work in which we construct embodied computational models on humanoid robots that address the mechanisms behind learning, adaptation, and operation of multimodal body representations. At the same time, we assess which of the features of the "body in the brain" should be transferred to robots to give rise to more adaptive and resilient, self-calibrating machines. We extend traditional robot kinematic calibration focusing on self-contained approaches where no external metrology is needed: self-contact and self-observation. Problem formulation allowing to combine several ways of closing the kinematic chain simultaneously is presented, along with a calibration toolbox and experimental validation on several robot platforms. Finally, next to models of the body itself, we study peripersonal space - the space immediately surrounding the body. Again, embodied computational models are developed and subsequently, the possibility of turning these biologically inspired representations into safe human-robot collaboration is studied.