IMRL: Integrating Visual, Physical, Temporal, and Geometric Representations for Enhanced Food Acquisition

Rui Liu, Zahiruddin Mahammad, Amisha Bhaskar, Pratap Tokekar
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Abstract

Robotic assistive feeding holds significant promise for improving the quality of life for individuals with eating disabilities. However, acquiring diverse food items under varying conditions and generalizing to unseen food presents unique challenges. Existing methods that rely on surface-level geometric information (e.g., bounding box and pose) derived from visual cues (e.g., color, shape, and texture) often lacks adaptability and robustness, especially when foods share similar physical properties but differ in visual appearance. We employ imitation learning (IL) to learn a policy for food acquisition. Existing methods employ IL or Reinforcement Learning (RL) to learn a policy based on off-the-shelf image encoders such as ResNet-50. However, such representations are not robust and struggle to generalize across diverse acquisition scenarios. To address these limitations, we propose a novel approach, IMRL (Integrated Multi-Dimensional Representation Learning), which integrates visual, physical, temporal, and geometric representations to enhance the robustness and generalizability of IL for food acquisition. Our approach captures food types and physical properties (e.g., solid, semi-solid, granular, liquid, and mixture), models temporal dynamics of acquisition actions, and introduces geometric information to determine optimal scooping points and assess bowl fullness. IMRL enables IL to adaptively adjust scooping strategies based on context, improving the robot's capability to handle diverse food acquisition scenarios. Experiments on a real robot demonstrate our approach's robustness and adaptability across various foods and bowl configurations, including zero-shot generalization to unseen settings. Our approach achieves improvement up to $35\%$ in success rate compared with the best-performing baseline.
IMRL:整合视觉、物理、时间和几何表征,增强食物获取能力
机器人辅助喂食为改善进食残疾人士的生活质量带来了巨大希望。然而,在不同条件下获取不同的食物并将其推广到未见过的食物上,这带来了独特的挑战。现有的方法依赖于从视觉线索(如颜色、形状和纹理)中提取的表面级几何信息(如边界框和姿势),这些方法往往缺乏适应性和鲁棒性,尤其是当食物具有相似的物理特性但视觉外观不同时。然而,这种方法并不稳健,很难在不同的获取场景中通用。为了解决这些局限性,我们提出了一种新方法--IMRL(综合多维表征学习),它整合了视觉、物理、时间和几何表征,以增强用于食物获取的 IL 的鲁棒性和泛化能力。我们的方法捕捉食物类型和物理特性(如固体、半固体、颗粒状、液体和混合物),建立获取动作的时间动态模型,并引入几何信息以确定最佳舀食点和评估碗的饱满度。IMRL使IL能够根据上下文自适应地调整舀取策略,从而提高机器人处理各种食物获取场景的能力。在真实机器人上进行的实验证明了我们的方法在各种食物和碗配置中的稳健性和适应性,包括对未知环境的零点泛化。与表现最好的基准相比,我们的方法在成功率上提高了 35%。
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