Interactive Perception Based on Gaussian Process Classification for House-Hold Objects Recognition and Sorting

We present an interactive perception model for object sorting based on Gaussian Process (GP) classification that is capable of recognizing objects categories from point cloud data. In our approach, FPFH features are extracted from point clouds to describe the local 3D shape of objects and a Bag-of-Words coding method is used to obtain an object-level vocabulary representation. Multi-class Gaussian Process classification is employed to provide and probable estimation of the identity of the object and serves a key role in the interactive perception cycle – modelling perception confidence. We show results from simulated input data on both SVM and GP based multi-class classifiers to validate the recognition accuracy of our proposed perception model. Our results demonstrate that by using a GP-based classifier, we obtain true positive classification rates of up to 80%. Our semi-autonomous object sorting experiments show that the proposed GP based interactive sorting approach outperforms random sorting by up to 30% when applied to scenes comprising configurations of household objects

ChatGPT Empowered Long-Step Robot Control in Various Environments: A Case Application

This paper demonstrates how OpenAI's ChatGPT can be used in a few-shot setting to convert natural language instructions into an executable robot action sequence. The paper proposes easy-to-customize input prompts for ChatGPT that meet common requirements in practical applications, such as easy integration with robot execution systems and applicability to various environments while minimizing the impact of ChatGPT's token limit. The prompts encourage ChatGPT to output a sequence of predefined robot actions, represent the operating environment in a formalized style, and infer the updated state of the operating environment. Experiments confirmed that the proposed prompts enable ChatGPT to act according to requirements in various environments, and users can adjust ChatGPT's output with natural language feedback for safe and robust operation. The proposed prompts and source code are open-source and publicly available at https://github.com/microsoft/ChatGPT-Robot-Manipulation-PromptsComment: 17 figures. Last updated April 11th, 202

Object affordance as a guide for grasp-type recognition

Recognizing human grasping strategies is an important factor in robot teaching as these strategies contain the implicit knowledge necessary to perform a series of manipulations smoothly. This study analyzed the effects of object affordance-a prior distribution of grasp types for each object-on convolutional neural network (CNN)-based grasp-type recognition. To this end, we created datasets of first-person grasping-hand images labeled with grasp types and object names, and tested a recognition pipeline leveraging object affordance. We evaluated scenarios with real and illusory objects to be grasped, to consider a teaching condition in mixed reality where the lack of visual object information can make the CNN recognition challenging. The results show that object affordance guided the CNN in both scenarios, increasing the accuracy by 1) excluding unlikely grasp types from the candidates and 2) enhancing likely grasp types. In addition, the "enhancing effect" was more pronounced with high degrees of grasp-type heterogeneity. These results indicate the effectiveness of object affordance for guiding grasp-type recognition in robot teaching applications.Comment: 12 pages, 11 figures. Last updated February 27th, 202