# Towards Robotic Feeding: Role of Haptics in Fork-based Food Manipulation

arXiv: https://arxiv.org/abs/1804.08768 · HTML: https://mlsanity.com/paper/1804.08768/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2018.

## Built on by (most cited first, 16 of 16)

- [Learning Bimanual Scooping Policies for Food Acquisition](https://mlsanity.com/paper/2211.14652/) (2022)
- [Vision-driven Compliant Manipulation for Reliable, High-Precision Assembly Tasks](https://mlsanity.com/paper/2106.14070/) (2021)
- [Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration](https://mlsanity.com/paper/2108.06038/) (2021)
- [Learning Visuo-Haptic Skewering Strategies for Robot-Assisted Feeding](https://mlsanity.com/paper/2211.14648/) (2022)
- [Point Cloud Models Improve Visual Robustness in Robotic Learners](https://mlsanity.com/paper/2404.18926/) (2024)
- [Adaptive Visual Imitation Learning for Robotic Assisted Feeding Across Varied Bowl Configurations and Food Types](https://mlsanity.com/paper/2403.12891/) (2024)
- [User profile-driven large-scale multi-agent learning from demonstration in federated human-robot collaborative environments](https://mlsanity.com/paper/2103.16434/) (2021)
- [Human-Robot Commensality: Bite Timing Prediction for Robot-Assisted Feeding in Groups](https://mlsanity.com/paper/2207.03348/) (2022)
- [Few-Shot Learning of Force-Based Motions From Demonstration Through Pre-training of Haptic Representation](https://mlsanity.com/paper/2309.04640/) (2023)
- [Extended Tactile Perception: Vibration Sensing through Tools and Grasped Objects](https://mlsanity.com/paper/2106.00489/) (2021)
- [REPeat: A Real2Sim2Real Approach for Pre-acquisition of Soft Food Items in Robot-assisted Feeding](https://mlsanity.com/paper/2410.10017/) (2024)
- [Towards Sample-efficient Apprenticeship Learning from Suboptimal Demonstration](https://mlsanity.com/paper/2110.04347/) (2021)
- [Learning Multimodal Attention for Manipulating Deformable Objects with Changing States](https://mlsanity.com/paper/2309.14837/) (2023)
- ["Do it my way!": Impact of Customizations on Trust perceptions in Human-Robot Collaboration](https://mlsanity.com/paper/2310.18791/) (2023)
- [Robot-Assisted Social Dining as a White Glove Service](https://mlsanity.com/paper/2602.15767/) (2026)
- [TacSushi: Tactile-Grounded World-Action Modeling for Dexterous Sushi Manipulation](https://mlsanity.com/paper/2609.19613/) (2026)

## Abstract (arXiv)

Autonomous feeding is challenging because it requires manipulation of food items with various compliance, sizes, and shapes. To understand how humans manipulate food items during feeding and to explore ways to adapt their strategies to robots, we collected a rich dataset of human trajectories by asking them to pick up food and feed it to a mannequin. From the analysis of the collected haptic and motion signals, we demonstrate that humans adapt their control policies to accommodate to the compliance and shape of the food item being acquired. We propose a taxonomy of manipulation strategies for feeding to highlight such policies. As a first step to generate compliance-dependent policies, we propose a set of classifiers for compliance-based food categorization from haptic and motion signals. We compare these human manipulation strategies with fixed position-control policies via a robot. Our analysis of success and failure cases of human and robot policies further highlights the importance of adapting the policy to the compliance of a food item.