# Imitation Learning for Active Neck Motion Enabling Robot Manipulation beyond the Field of View

arXiv: https://arxiv.org/abs/2506.17624 · HTML: https://mlsanity.com/paper/2506.17624/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 8 of its 14 references that are in the map.
- First posted on arXiv 21 Jun 2025.

## Placed in

- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation](https://mlsanity.com/paper/1710.04615/) (2017)
- [Open-TeleVision: Teleoperation with Immersive Active Visual Feedback](https://mlsanity.com/paper/2407.01512/) (2024)
- [Deep Imitation Learning for Humanoid Loco-manipulation through Human Teleoperation](https://mlsanity.com/paper/2309.01952/) (2023)
- [Goal-conditioned dual-action imitation learning for dexterous dual-arm robot manipulation](https://mlsanity.com/paper/2203.09749/) (2022)

## Abstract (arXiv)

Most prior research in deep imitation learning has predominantly utilized fixed cameras for image input, which constrains task performance to the predefined field of view. However, enabling a robot to actively maneuver its neck can significantly expand the scope of imitation learning to encompass a wider variety of tasks and expressive actions such as neck gestures. To facilitate imitation learning in robots capable of neck movement while simultaneously performing object manipulation, we propose a teaching system that systematically collects datasets incorporating neck movements while minimizing discomfort caused by dynamic viewpoints during teleoperation. In addition, we present a novel network model for learning manipulation tasks including active neck motion. Experimental results showed that our model can achieve a high success rate of around 90%, regardless of the distraction from the viewpoint variations by active neck motion. Moreover, the proposed model proved particularly effective in challenging scenarios, such as when objects were situated at the periphery or beyond the standard field of view, where traditional models struggled. The proposed approach contributes to the efficiency of dataset collection and extends the applicability of imitation learning to more complex and dynamic scenarios.