Multiview Progress Prediction of Robot Activities

TL;DR

This paper introduces a multi-view architecture for predicting the progress of robot actions, addressing challenges like self-occlusion, to improve robot understanding and interaction with humans.

Contribution

It presents a novel multi-view approach for action progress prediction in robotics, enhancing perception accuracy over single-view methods.

Findings

Effective prediction of robot action progress demonstrated on Mobile ALOHA.

Multi-view architecture outperforms single-view models.

Improves robot safety and collaboration capabilities.

Abstract

For robots to operate effectively and safely alongside humans, they must be able to understand the progress of ongoing actions. This ability, known as action progress prediction, is critical for tasks ranging from timely assistance to autonomous decision-making. However, modeling action progression in robotics has often been overlooked. Moreover, a single camera may be insufficient for understanding robot's ego-actions, as self-occlusion can significantly hinder perception and model performance. In this paper, we propose a multi-view architecture for action progress prediction in robot manipulation tasks. Experiments on Mobile ALOHA demonstrate the effectiveness of the proposed approach.