Let's Push Things Forward: A Survey on Robot Pushing

Abstract

As robot make their way out of factories into human environments, outer space, and beyond, they require the skill to manipulate their environment in multifarious, unforeseeable circumstances. With this regard, pushing is an essential motion primitive that dramatically extends a robot's manipulation repertoire. In this work, we review the robotic pushing literature. While focusing on work concerned with predicting the motion of pushed objects, we also cover relevant applications of pushing for planning and control. Beginning with analytical approaches, under which we also subsume physics engines, we then proceed to discuss work on learning models from data. In doing so, we dedicate a separate section to deep learning approaches which have seen a recent upsurge in the literature. Concluding remarks and further research perspectives are given at the end of the paper.

References in corpus (1)

• Feature-Based Transfer Learning for Robotic Push Manipulation

Cited by in corpus (5)

• Object and Relation Centric Representations for Push Effect Prediction
• Force Push: Robust Single-Point Pushing with Force Feedback
• One-shot Learning for Autonomous Aerial Manipulation
• Learning Transferable Push Manipulation Skills in Novel Contexts
• Exploring How Non-Prehensile Manipulation Expands Capability in Robots Experiencing Multi-Joint Failure