แนวคิดหลัก
Introducing LAVA for efficient food acquisition using a hierarchical policy framework.
บทคัดย่อ
The study introduces Long-horizon Visual Action (LAVA) for acquiring liquid, semisolid, and deformable foods. The framework employs a hierarchical policy with high-level, mid-level, and low-level policies. LAVA outperforms baselines in real-world trials across various food types with a success rate of 89 ± 4%. The paper details the system architecture, experimental setup, data collection process, baselines comparison, and zero-shot generalization results.
I. Introduction
Robotic Assisted Feeding (RAF) aims to restore independence in feeding for individuals with mobility impairments.
Existing RAF methods focus on solid foods, leaving a gap in manipulation strategies for semi-solid and deformable foods.
LAVA introduces Long-horizon Visual Action for acquiring diverse food types efficiently.
II. Related Work
Prior works have explored robot-assisted feeding focusing on bite acquisition and transfer.
Learning from Demonstration (LfD) is utilized for developing new skills by observing expert demonstrations.
Long-horizon planning frameworks separate high-level strategic decision-making from detailed motion planning.
III. Problem Statement
The challenge addressed is sequential bite acquisition to maximize efficiency in long-horizon food acquisition.
Access to bowl image observations and expert demonstration data is assumed to learn a policy for efficient food acquisition.
IV. Proposed Approach
A hierarchical policy framework is formalized into high-level, mid-level, and low-level sub-policies.
High-level policy selects manipulation primitives based on visual inputs; mid-level refines these primitives; low-level executes actions.
V. Experiments
Experimental setup includes a UR5e robot arm with custom spoon attachment and RealSense camera.
Data collection involves kinesthetic teaching focusing on cereals and tofu.
Baseline models include LAVA-low and Fixed Trajectory Scooping (FTS).
Results show LAVA's superior performance in efficiency, spillage reduction, breakage prevention compared to baselines across various food types.
VI. Conclusion & Future Work
LAVA demonstrates robust performance across varied configurations including soup with tofu chunks through zero-shot generalization.
Limitations exist in handling thin or irregular foods requiring specialized strategies.
Future work will focus on broadening action space for diverse food types and exploring efficient data acquisition methods.
สถิติ
Across 46 bowls, LAVA acquires much more efficiently than baselines with a success rate of 89 ± 4%.
คำพูด
"LAVA adeptly adjusts to real-time changes in food depth."
"Our approach demonstrates robust performance across varied configurations."