# Sensorimotor representation learning for an "active self" in robots: A model survey

arXiv: https://arxiv.org/abs/2011.12860 · HTML: https://mlsanity.com/paper/2011.12860/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2020.

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

- [A Survey of Embodied AI: From Simulators to Research Tasks](https://mlsanity.com/paper/2103.04918/) (2021)
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- [VLNVerse: A Benchmark for Vision-Language Navigation with Versatile, Embodied, Realistic Simulation and Evaluation](https://mlsanity.com/paper/2512.19021/) (2025)
- [Learning to Autonomously Reach Objects with NICO and Grow-When-Required Networks](https://mlsanity.com/paper/2210.07851/) (2022)
- [Analyzing Multimodal Integration in the Variational Autoencoder from an Information-Theoretic Perspective](https://mlsanity.com/paper/2411.00522/) (2024)
- [Learning body models: from humans to humanoids](https://mlsanity.com/paper/2211.03049/) (2022)
- [Robot Skill Learning Via Classical Robotics-Based Generated Datasets: Advantages, Disadvantages, and Future Improvement](https://mlsanity.com/paper/2301.08794/) (2023)

## Abstract (arXiv)

Safe human-robot interactions require robots to be able to learn how to behave appropriately in southumans' world revspaces populated by people and thus to cope with the challenges posed by our dynamic and unstructured environment, rather than being provided a rigid set of rules for operations. In humans, these capabilities are thought to be related to our ability to perceive our body in space, sensing the location of our limbs during movement, being aware of other objects and agents, and controlling our body parts to interact with them intentionally. Toward the next generation of robots with bio-inspired capacities, in this paper, we first review the developmental processes of underlying mechanisms of these abilities: The sensory representations of body schema, peripersonal space, and the active self in humans. Second, we provide a survey of robotics models of these sensory representations and robotics models of the self; and we compare these models with the human counterparts. Finally, we analyse what is missing from these robotics models and propose a theoretical computational framework, which aims to allow the emergence of the sense of self in artificial agents by developing sensory representations through self-exploration.