Name
Title | ||
|---|---|---|
Playing with Food - Learning Food Item Representations Through Interactive Exploration. |
Abstract | ||
|---|---|---|
A key challenge in robotic food manipulation is modeling the material properties of diverse and deformable food items. We propose using a multimodal sensory approach to interact and play with food that facilitates the ability to distinguish these properties across food items. First, we use a robotic arm and an array of sensors, which are synchronized using ROS, to collect a diverse dataset consisting of 21 unique food items with varying slices and properties. Afterwards, we learn visual embedding networks that utilize a combination of proprioceptive, audio, and visual data to encode similarities among food items using a triplet loss formulation. Our evaluations show that embeddings learned through interactions can successfully increase performance in a wide range of material and shape classification tasks. We envision that these learned embeddings can be utilized as a basis for planning and selecting optimal parameters for more material-aware robotic food manipulation skills. Furthermore, we hope to stimulate further innovations in the field of food robotics by sharing this food playing dataset with the research community. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-71151-1_28 | ISER |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amrita Sawhney | 1 | 0 | 0.34 |
| Steven Lee | 2 | 0 | 0.34 |
| Kevin J. Zhang | 3 | 1 | 3.52 |
| Manuela Veloso | 4 | 8563 | 882.50 |
| o kromer | 5 | 472 | 38.99 |