VP-GO: a "light" action-conditioned visual prediction model

TL;DR

VP-GO is a lightweight, stochastic, action-conditioned visual prediction model designed for robotic grasping, offering improved qualitative predictions of complex grasps while maintaining computational efficiency.

Contribution

The paper introduces VP-GO, a novel lightweight stochastic visual prediction model with hierarchical action decomposition and releases a new dataset for robotic grasp prediction.

Findings

Performs comparably to complex models on signal prediction metrics.

Outperforms in qualitative prediction of complex robotic grasps.

Compatible with existing datasets like RoboNet and PandaGrasp.

Abstract

Visual prediction models are a promising solution for visual-based robotic grasping of cluttered, unknown soft objects. Previous models from the literature are computationally greedy, which limits reproducibility; although some consider stochasticity in the prediction model, it is often too weak to catch the reality of robotics experiments involving grasping such objects. Furthermore, previous work focused on elementary movements that are not efficient to reason in terms of more complex semantic actions. To address these limitations, we propose VP-GO, a ``light'' stochastic action-conditioned visual prediction model. We propose a hierarchical decomposition of semantic grasping and manipulation actions into elementary end-effector movements, to ensure compatibility with existing models and datasets for visual prediction of robotic actions such as RoboNet. We also record and release a new open dataset for visual prediction of object grasping, called PandaGrasp. Our model can be pre-trained on RoboNet and fine-tuned on PandaGrasp, and performs similarly to more complex models in terms of signal prediction metrics. Qualitatively, it outperforms when predicting the outcome of complex grasps performed by our robot.