Learning Visuo-Haptic Skewering Strategies for Robot-Assisted Feeding

• URL: http://arxiv.org/abs/2211.14648v2
• Date: Wed, 30 Nov 2022 01:53:26 GMT
• Title: Learning Visuo-Haptic Skewering Strategies for Robot-Assisted Feeding
• Authors: Priya Sundaresan, Suneel Belkhale, Dorsa Sadigh
• Abstract summary: We leverage visual and haptic observations during interaction with an item to plan skewering motions. We learn a generalizable, multimodal representation for a food item from raw sensory inputs. We propose a zero-shot framework to sense visuo-haptic properties of a previously unseen item and reactively skewer it.
• Score: 13.381485293778654
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Acquiring food items with a fork poses an immense challenge to a robot-assisted feeding system, due to the wide range of material properties and visual appearances present across food groups. Deformable foods necessitate different skewering strategies than firm ones, but inferring such characteristics for several previously unseen items on a plate remains nontrivial. Our key insight is to leverage visual and haptic observations during interaction with an item to rapidly and reactively plan skewering motions. We learn a generalizable, multimodal representation for a food item from raw sensory inputs which informs the optimal skewering strategy. Given this representation, we propose a zero-shot framework to sense visuo-haptic properties of a previously unseen item and reactively skewer it, all within a single interaction. Real-robot experiments with foods of varying levels of visual and textural diversity demonstrate that our multimodal policy outperforms baselines which do not exploit both visual and haptic cues or do not reactively plan. Across 6 plates of different food items, our proposed framework achieves 71% success over 69 skewering attempts total. Supplementary material, datasets, code, and videos are available on our website: https://sites.google.com/view/hapticvisualnet-corl22/home

Related papers

• ActionCOMET: A Zero-shot Approach to Learn Image-specific Commonsense Concepts about Actions [66.20773952864802]
  We develop a dataset consisting of 8.5k images and 59.3k inferences about actions grounded in those images. We propose ActionCOMET, a framework to discern knowledge present in language models specific to the provided visual input.
  arXiv  Detail & Related papers  (2024-10-17T15:22:57Z)
• IMRL: Integrating Visual, Physical, Temporal, and Geometric Representations for Enhanced Food Acquisition [16.32678094159896]
  We introduce IMRL (Integrated Multi-Dimensional Representation Learning), which integrates visual, physical, temporal, and geometric representations to enhance robustness and generalizability of IL for food acquisition. Our approach captures food types and physical properties, models temporal dynamics of acquisition actions, and introduces geometric information to determine optimal scooping points. IMRL enables IL to adaptively adjust scooping strategies based on context, improving the robot's capability to handle diverse food acquisition scenarios.
  arXiv  Detail & Related papers  (2024-09-18T16:09:06Z)
• MetaFood3D: Large 3D Food Object Dataset with Nutrition Values [53.24500333363066]
  This dataset consists of 637 meticulously labeled 3D food objects across 108 categories, featuring detailed nutrition information, weight, and food codes linked to a comprehensive nutrition database. Experimental results demonstrate our dataset's significant potential for improving algorithm performance, highlight the challenging gap between video captures and 3D scanned data, and show the strength of the MetaFood3D dataset in high-quality data generation, simulation, and augmentation.
  arXiv  Detail & Related papers  (2024-09-03T15:02:52Z)
• RoDE: Linear Rectified Mixture of Diverse Experts for Food Large Multi-Modal Models [96.43285670458803]
  Uni-Food is a unified food dataset that comprises over 100,000 images with various food labels. Uni-Food is designed to provide a more holistic approach to food data analysis. We introduce a novel Linear Rectification Mixture of Diverse Experts (RoDE) approach to address the inherent challenges of food-related multitasking.
  arXiv  Detail & Related papers  (2024-07-17T16:49:34Z)
• Adaptive Visual Imitation Learning for Robotic Assisted Feeding Across Varied Bowl Configurations and Food Types [17.835835270751176]
  We introduce a novel visual imitation network with a spatial attention module for robotic assisted feeding (RAF) We propose a framework that integrates visual perception with imitation learning to enable the robot to handle diverse scenarios during scooping. Our approach, named AVIL (adaptive visual imitation learning), exhibits adaptability and robustness across different bowl configurations.
  arXiv  Detail & Related papers  (2024-03-19T16:40:57Z)
• Learning Sequential Acquisition Policies for Robot-Assisted Feeding [37.371967116072966]
  We propose Visual Action Planning OveR Sequences (VAPORS) as a framework for long-horizon food acquisition. VAPORS learns a policy for high-level action selection by leveraging learned latent plate dynamics in simulation. We validate our approach on complex real-world acquisition trials involving noodle acquisition and bimanual scooping of jelly beans.
  arXiv  Detail & Related papers  (2023-09-11T02:20:28Z)
• Transferring Knowledge for Food Image Segmentation using Transformers and Convolutions [65.50975507723827]
  Food image segmentation is an important task that has ubiquitous applications, such as estimating the nutritional value of a plate of food. One challenge is that food items can overlap and mix, making them difficult to distinguish. Two models are trained and compared, one based on convolutional neural networks and the other on Bidirectional representation for Image Transformers (BEiT) The BEiT model outperforms the previous state-of-the-art model by achieving a mean intersection over union of 49.4 on FoodSeg103.
  arXiv  Detail & Related papers  (2023-06-15T15:38:10Z)
• Self-Supervised Visual Representation Learning on Food Images [6.602838826255494]
  Existing deep learning-based methods learn the visual representation for downstream tasks based on human annotation of each food image. Most food images in real life are obtained without labels, and data annotation requires plenty of time and human effort. In this paper, we focus on the implementation and analysis of existing representative self-supervised learning methods on food images.
  arXiv  Detail & Related papers  (2023-03-16T02:31:51Z)
• A Large-Scale Benchmark for Food Image Segmentation [62.28029856051079]
  We build a new food image dataset FoodSeg103 (and its extension FoodSeg154) containing 9,490 images. We annotate these images with 154 ingredient classes and each image has an average of 6 ingredient labels and pixel-wise masks. We propose a multi-modality pre-training approach called ReLeM that explicitly equips a segmentation model with rich and semantic food knowledge.
  arXiv  Detail & Related papers  (2021-05-12T03:00:07Z)
• Leveraging Post Hoc Context for Faster Learning in Bandit Settings with Applications in Robot-Assisted Feeding [23.368884607763093]
  Autonomous robot-assisted feeding requires the ability to acquire a wide variety of food items. Previous work showed that the problem can be represented as a linear bandit with visual context. We propose a modified linear contextual bandit framework augmented with post hoc context.
  arXiv  Detail & Related papers  (2020-11-05T01:28:25Z)
• Cross-Modal Food Retrieval: Learning a Joint Embedding of Food Images and Recipes with Semantic Consistency and Attention Mechanism [70.85894675131624]
  We learn an embedding of images and recipes in a common feature space, such that the corresponding image-recipe embeddings lie close to one another. We propose Semantic-Consistent and Attention-based Networks (SCAN), which regularize the embeddings of the two modalities through aligning output semantic probabilities. We show that we can outperform several state-of-the-art cross-modal retrieval strategies for food images and cooking recipes by a significant margin.
  arXiv  Detail & Related papers  (2020-03-09T07:41:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.