See and Switch: Vision-Based Branching for Interactive Robot-Skill Programming

TL;DR

See & Switch introduces a vision-based framework for interactive robot skill programming that enables reliable, high-dimensional perception-grounded branching during task execution, improving scalability and robustness in real-world scenarios.

Contribution

The paper presents a novel vision-based branch selection method using eye-in-hand images, enabling interactive, modality-independent robot skill programming with anomaly detection.

Findings

Achieved 90.7% accuracy in branch selection

Achieved 87.9% accuracy in anomaly detection

Validated on three dexterous manipulation tasks

Abstract

Programming robots by demonstration (PbD) is an intuitive concept, but scaling it to real-world variability remains a challenge for most current teaching frameworks. Conditional task graphs are very expressive and can be defined incrementally, which fits very well with the PbD idea. However, acting using conditional task graphs requires reliable perception-grounded online branch selection. In this paper, we present See & Switch, an interactive teaching-and-execution framework that represents tasks as user-extendable graphs of skill parts connected via decision states (DS), enabling conditional branching during replay. Unlike prior approaches that rely on manual branching or low-dimensional signals (e.g., proprioception), our vision-based Switcher uses eye-in-hand images (high-dimensional) to select among competing successor skill parts and to detect out-of-distribution contexts that require new demonstrations. We integrate kinesthetic teaching, joystick control, and hand gestures via an input-modality-abstraction layer and demonstrate that our proposed method is teaching modality-independent, enabling efficient in-situ recovery demonstrations. The system is validated in experiments on three challenging dexterous manipulation tasks. We evaluate our method under diverse conditions and furthermore conduct user studies with 8 participants. We show that the proposed method reliably performs branch selection and anomaly detection for novice users, achieving 90.7 % and 87.9 % accuracy, respectively, across 576 real-robot rollouts. We provide all code and data required to reproduce our experiments at http://imitrob.ciirc.cvut.cz/publications/seeandswitch.