# Learning Sequential Acquisition Policies for Robot-Assisted Feeding

arXiv: https://arxiv.org/abs/2309.05197 · HTML: https://mlsanity.com/paper/2309.05197/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 52 references that are in the map.
- First posted on arXiv 11 Sep 2023.

## Builds on (most cited first, 30 of 35)

- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [Learning Latent Dynamics for Planning from Pixels](https://mlsanity.com/paper/1811.04551/) (2018)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [CLIPort: What and Where Pathways for Robotic Manipulation](https://mlsanity.com/paper/2109.12098/) (2021)
- [RMA: Rapid Motor Adaptation for Legged Robots](https://mlsanity.com/paper/2107.04034/) (2021)
- [Transporter Networks: Rearranging the Visual World for Robotic Manipulation](https://mlsanity.com/paper/2010.14406/) (2020)
- [Integrated Task and Motion Planning](https://mlsanity.com/paper/2010.01083/) (2020)
- [Deep Visual Foresight for Planning Robot Motion](https://mlsanity.com/paper/1610.00696/) (2016)
- [Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control](https://mlsanity.com/paper/1812.00568/) (2018)
- [Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation](https://mlsanity.com/paper/1806.08756/) (2018)
- [FlingBot: The Unreasonable Effectiveness of Dynamic Manipulation for Cloth Unfolding](https://mlsanity.com/paper/2105.03655/) (2021)
- [Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation Tasks](https://mlsanity.com/paper/2110.03655/) (2021)
- [Causal Discovery in Physical Systems from Videos](https://mlsanity.com/paper/2007.00631/) (2020)
- [RoboCraft: Learning to See, Simulate, and Shape Elasto-Plastic Objects with Graph Networks](https://mlsanity.com/paper/2205.02909/) (2022)
- [Keypoints into the Future: Self-Supervised Correspondence in Model-Based Reinforcement Learning](https://mlsanity.com/paper/2009.05085/) (2020)
- [Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives](https://mlsanity.com/paper/2110.15360/) (2021)
- [Skill-based Model-based Reinforcement Learning](https://mlsanity.com/paper/2207.07560/) (2022)
- [Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor](https://mlsanity.com/paper/1910.04854/) (2019)
- [DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools](https://mlsanity.com/paper/2203.17275/) (2022)
- [kPAM 2.0: Feedback Control for Category-Level Robotic Manipulation](https://mlsanity.com/paper/2102.06279/) (2021)
- [The Surprising Effectiveness of Linear Models for Visual Foresight in Object Pile Manipulation](https://mlsanity.com/paper/2002.09093/) (2020)
- [Reinforcement Learning based Control of Imitative Policies for Near-Accident Driving](https://mlsanity.com/paper/2007.00178/) (2020)
- [Untangling Dense Knots by Learning Task-Relevant Keypoints](https://mlsanity.com/paper/2011.04999/) (2020)
- [Learning Bimanual Scooping Policies for Food Acquisition](https://mlsanity.com/paper/2211.14652/) (2022)
- [Hierarchical Neural Dynamic Policies](https://mlsanity.com/paper/2107.05627/) (2021)
- [Leveraging Multimodal Haptic Sensory Data for Robust Cutting](https://mlsanity.com/paper/1909.12460/) (2019)
- [Learning Visuo-Haptic Skewering Strategies for Robot-Assisted Feeding](https://mlsanity.com/paper/2211.14648/) (2022)
- [Adaptive Robot-Assisted Feeding: An Online Learning Framework for Acquiring Previously Unseen Food Items](https://mlsanity.com/paper/1908.07088/) (2019)
- [Leveraging Post Hoc Context for Faster Learning in Bandit Settings with Applications in Robot-Assisted Feeding](https://mlsanity.com/paper/2011.02604/) (2020)
- [Untangling Dense Non-Planar Knots by Learning Manipulation Features and Recovery Policies](https://mlsanity.com/paper/2107.08942/) (2021)

## Built on by (most cited first, 6 of 6)

- [FeelAnyForce: Estimating Contact Force Feedback from Tactile Sensation for Vision-Based Tactile Sensors](https://mlsanity.com/paper/2410.02048/) (2024)
- [FLAIR: Feeding via Long-horizon AcquIsition of Realistic dishes](https://mlsanity.com/paper/2407.07561/) (2024)
- [FEAST: A Flexible Mealtime-Assistance System Towards In-the-Wild Personalization](https://mlsanity.com/paper/2506.14968/) (2025)
- [REPeat: A Real2Sim2Real Approach for Pre-acquisition of Soft Food Items in Robot-assisted Feeding](https://mlsanity.com/paper/2410.10017/) (2024)
- [SCOOP'D: Learning Mixed-Liquid-Solid Scooping via Sim2Real Generative Policy](https://mlsanity.com/paper/2510.11566/) (2025)
- [GRITS: A Spillage-Aware Guided Diffusion Policy for Robot Food Scooping Tasks](https://mlsanity.com/paper/2510.00573/) (2025)

## Abstract (arXiv)

A robot providing mealtime assistance must perform specialized maneuvers with various utensils in order to pick up and feed a range of food items. Beyond these dexterous low-level skills, an assistive robot must also plan these strategies in sequence over a long horizon to clear a plate and complete a meal. Previous methods in robot-assisted feeding introduce highly specialized primitives for food handling without a means to compose them together. Meanwhile, existing approaches to long-horizon manipulation lack the flexibility to embed highly specialized primitives into their frameworks. We propose Visual Action Planning OveR Sequences (VAPORS), a framework for long-horizon food acquisition. VAPORS learns a policy for high-level action selection by leveraging learned latent plate dynamics in simulation. To carry out sequential plans in the real world, VAPORS delegates action execution to visually parameterized primitives. We validate our approach on complex real-world acquisition trials involving noodle acquisition and bimanual scooping of jelly beans. Across 38 plates, VAPORS acquires much more efficiently than baselines, generalizes across realistic plate variations such as toppings and sauces, and qualitatively appeals to user feeding preferences in a survey conducted across 49 individuals. Code, datasets, videos, and supplementary materials can be found on our website: https://sites.google.com/view/vaporsbot.