# Learning Bimanual Scooping Policies for Food Acquisition arXiv: https://arxiv.org/abs/2211.14652 ยท HTML: https://mlsanity.com/paper/2211.14652/ ## Key facts - Cited by 20 papers in the map (data through 14 Oct 2026). - Builds on 14 of its 31 references that are in the map. - First posted on arXiv 26 Nov 2022. ## Builds on (most cited first, 14 of 14) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [FlingBot: The Unreasonable Effectiveness of Dynamic Manipulation for Cloth Unfolding](https://mlsanity.com/paper/2105.03655/) (2021) - [Efficient Bimanual Manipulation Using Learned Task Schemas](https://mlsanity.com/paper/1909.13874/) (2019) - [Grasping with Chopsticks: Combating Covariate Shift in Model-free Imitation Learning for Fine Manipulation](https://mlsanity.com/paper/2011.06719/) (2020) - [Untangling Dense Knots by Learning Task-Relevant Keypoints](https://mlsanity.com/paper/2011.04999/) (2020) - [DextAIRity: Deformable Manipulation Can be a Breeze](https://mlsanity.com/paper/2203.01197/) (2022) - [Towards Robotic Feeding: Role of Haptics in Fork-based Food Manipulation](https://mlsanity.com/paper/1804.08768/) (2018) - [Leveraging Multimodal Haptic Sensory Data for Robust Cutting](https://mlsanity.com/paper/1909.12460/) (2019) - [Adaptive Robot-Assisted Feeding: An Online Learning Framework for Acquiring Previously Unseen Food Items](https://mlsanity.com/paper/1908.07088/) (2019) - [Bi-Manual Manipulation and Attachment via Sim-to-Real Reinforcement Learning](https://mlsanity.com/paper/2203.08277/) (2022) - [Learning to Scaffold the Development of Robotic Manipulation Skills](https://mlsanity.com/paper/1911.00969/) (2019) - [Leveraging Post Hoc Context for Faster Learning in Bandit Settings with Applications in Robot-Assisted Feeding](https://mlsanity.com/paper/2011.02604/) (2020) - [Telemanipulation with Chopsticks: Analyzing Human Factors in User Demonstrations](https://mlsanity.com/paper/2008.00101/) (2020) ## Built on by (most cited first, 20 of 20) - [RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation](https://mlsanity.com/paper/2410.07864/) (2024) - [Stabilize to Act: Learning to Coordinate for Bimanual Manipulation](https://mlsanity.com/paper/2309.01087/) (2023) - [VoxAct-B: Voxel-Based Acting and Stabilizing Policy for Bimanual Manipulation](https://mlsanity.com/paper/2407.04152/) (2024) - [3D FlowMatch Actor: Unified 3D Policy for Single- and Dual-Arm Manipulation](https://mlsanity.com/paper/2508.11002/) (2025) - [ScrewMimic: Bimanual Imitation from Human Videos with Screw Space Projection](https://mlsanity.com/paper/2405.03666/) (2024) - [DAG-Plan: Generating Directed Acyclic Dependency Graphs for Dual-Arm Cooperative Planning](https://mlsanity.com/paper/2406.09953/) (2024) - [Large Language Models for Orchestrating Bimanual Robots](https://mlsanity.com/paper/2404.02018/) (2024) - [FLAIR: Feeding via Long-horizon AcquIsition of Realistic dishes](https://mlsanity.com/paper/2407.07561/) (2024) - [Adaptive Visual Imitation Learning for Robotic Assisted Feeding Across Varied Bowl Configurations and Food Types](https://mlsanity.com/paper/2403.12891/) (2024) - [A Comparison of Imitation Learning Algorithms for Bimanual Manipulation](https://mlsanity.com/paper/2408.06536/) (2024) - [LLM+MAP: Bimanual Robot Task Planning using Large Language Models and Planning Domain Definition Language](https://mlsanity.com/paper/2503.17309/) (2025) - [FEAST: A Flexible Mealtime-Assistance System Towards In-the-Wild Personalization](https://mlsanity.com/paper/2506.14968/) (2025) - [Learning Sequential Acquisition Policies for Robot-Assisted Feeding](https://mlsanity.com/paper/2309.05197/) (2023) - [IMRL: Integrating Visual, Physical, Temporal, and Geometric Representations for Enhanced Food Acquisition](https://mlsanity.com/paper/2409.12092/) (2024) - [D-CODA: Diffusion for Coordinated Dual-Arm Data Augmentation](https://mlsanity.com/paper/2505.04860/) (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) - [AsymDex: Asymmetry and Relative Coordinates for RL-based Bimanual Dexterity](https://mlsanity.com/paper/2411.13020/) (2024) - [GRITS: A Spillage-Aware Guided Diffusion Policy for Robot Food Scooping Tasks](https://mlsanity.com/paper/2510.00573/) (2025) - [VERAGMIL: Virtual Environment for Scooping Granular Foods with Imitation Learning Models](https://mlsanity.com/paper/2608.18258/) (2026) ## Abstract (arXiv) A robotic feeding system must be able to acquire a variety of foods. Prior bite acquisition works consider single-arm spoon scooping or fork skewering, which do not generalize to foods with complex geometries and deformabilities. For example, when acquiring a group of peas, skewering could smoosh the peas while scooping without a barrier could result in chasing the peas on the plate. In order to acquire foods with such diverse properties, we propose stabilizing food items during scooping using a second arm, for example, by pushing peas against the spoon with a flat surface to prevent dispersion. The added stabilizing arm can lead to new challenges. Critically, this arm should stabilize the food scene without interfering with the acquisition motion, which is especially difficult for easily breakable high-risk food items like tofu. These high-risk foods can break between the pusher and spoon during scooping, which can lead to food waste falling out of the spoon. We propose a general bimanual scooping primitive and an adaptive stabilization strategy that enables successful acquisition of a diverse set of food geometries and physical properties. Our approach, CARBS: Coordinated Acquisition with Reactive Bimanual Scooping, learns to stabilize without impeding task progress by identifying high-risk foods and robustly scooping them using closed-loop visual feedback. We find that CARBS is able to generalize across food shape, size, and deformability and is additionally able to manipulate multiple food items simultaneously. CARBS achieves 87.0% success on scooping rigid foods, which is 25.8% more successful than a single-arm baseline, and reduces food breakage by 16.2% compared to an analytical baseline. Videos can be found at https://sites.google.com/view/bimanualscoop-corl22/home .