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

2024-09-18

arXiv.org

AI Summary

Robust spooning of diverse foods—especially visually similar yet physically distinct items—under complex, real-world conditions remains challenging for robotic feeding, particularly regarding cross-scene generalization and zero-shot adaptation to unseen food types and container configurations. Method: We propose the first context-adaptive spooning framework integrating four-dimensional representations: visual, physical, temporal, and geometric. Our approach unifies imitation learning, multimodal feature encoding, physical property modeling, temporal action dynamics modeling, and geometric spatial reasoning—including optimal scooping point localization and bowl fullness estimation. Contribution/Results: The framework enables zero-shot transfer to novel foods and containers without retraining. Evaluated on a real robotic platform, it achieves a 35% absolute improvement in spooning success rate over the strongest baseline, demonstrating significantly enhanced robustness and generalization across food categories and container geometries.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

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.

Problem

• Enhances robotic food acquisition using multi-dimensional representations.
• Improves adaptability and robustness in diverse food scenarios.
• Generalizes to unseen food types and bowl configurations.

Innovation

• Integrates visual, physical, temporal, geometric representations
• Uses imitation learning for adaptive food acquisition strategies
• Improves success rate by 35% in diverse scenarios