Learning Robust Bed Making using Deep Imitation Learning with DART

Abstract

Bed-making is a universal home task that can be challenging for senior citizens due to reaching motions. Automating bed-making has multiple technical challenges such as perception in an unstructured environments, deformable object manipulation, obstacle avoidance and sequential decision making. We explore how DART, an LfD algorithm for learning robust policies, can be applied to automating bed making without fiducial markers with a Toyota Human Support Robot (HSR). By gathering human demonstrations for grasping the sheet and failure detection, we can learn deep neural network policies that leverage pre-trained YOLO features to automate the task. Experiments with a 1/2 scale twin bed and distractors placed on the bed, suggest policies learned on 50 demonstrations with DART achieve 96% sheet coverage, which is over 200% better than a corner detector baseline using contour detection.

References in corpus (3)

• CNN Features off-the-shelf: an Astounding Baseline for Recognition
• DART: Noise Injection for Robust Imitation Learning
• Multi-Level Discovery of Deep Options

Cited by in corpus (4)

• Sim-to-Real Reinforcement Learning for Deformable Object Manipulation
• Learning Arbitrary-Goal Fabric Folding with One Hour of Real Robot Experience
• Seeing All the Angles: Learning Multiview Manipulation Policies for Contact-Rich Tasks from Demonstrations
• Uncertainty-Aware Data Aggregation for Deep Imitation Learning