MaestROB: A Robotics Framework for Integrated Orchestration of Low-Level Control and High-Level Reasoning

This paper describes a framework called MaestROB. It is designed to make the robots perform complex tasks with high precision by simple high-level instructions given by natural language or demonstration. To realize this, it handles a hierarchical structure by using the knowledge stored in the forms of ontology and rules for bridging among different levels of instructions. Accordingly, the framework has multiple layers of processing components; perception and actuation control at the low level, symbolic planner and Watson APIs for cognitive capabilities and semantic understanding, and orchestration of these components by a new open source robot middleware called Project Intu at its core. We show how this framework can be used in a complex scenario where multiple actors (human, a communication robot, and an industrial robot) collaborate to perform a common industrial task. Human teaches an assembly task to Pepper (a humanoid robot from SoftBank Robotics) using natural language conversation and demonstration. Our framework helps Pepper perceive the human demonstration and generate a sequence of actions for UR5 (collaborative robot arm from Universal Robots), which ultimately performs the assembly (e.g. insertion) task.Comment: IEEE International Conference on Robotics and Automation (ICRA) 2018. Video: https://www.youtube.com/watch?v=19JsdZi0TW

Making tools and making sense: complex, intentional behaviour in human evolution

Stone tool-making is an ancient and prototypically human skill characterized by multiple levels of intentional organization. In a formal sense, it displays surprising similarities to the multi-level organization of human language. Recent functional brain imaging studies of stone tool-making similarly demonstrate overlap with neural circuits involved in language processing. These observations consistent with the hypothesis that language and tool-making share key requirements for the construction of hierarchically structured action sequences and evolved together in a mutually reinforcing way

What Can I Do Around Here? Deep Functional Scene Understanding for Cognitive Robots

For robots that have the capability to interact with the physical environment through their end effectors, understanding the surrounding scenes is not merely a task of image classification or object recognition. To perform actual tasks, it is critical for the robot to have a functional understanding of the visual scene. Here, we address the problem of localizing and recognition of functional areas from an arbitrary indoor scene, formulated as a two-stage deep learning based detection pipeline. A new scene functionality testing-bed, which is complied from two publicly available indoor scene datasets, is used for evaluation. Our method is evaluated quantitatively on the new dataset, demonstrating the ability to perform efficient recognition of functional areas from arbitrary indoor scenes. We also demonstrate that our detection model can be generalized onto novel indoor scenes by cross validating it with the images from two different datasets

Ostension and Demonstrative Reference

Abstract. The strong similarity between the use of ostension and that of a simple demonstrative to predicate something of an object seems to conflict with equally strong intuitions according to which, while “this” does usually refer to an object, the gesture of holding an object in your hand and showing it to an audience does not refer to the demonstrated object. This paper argues that the problem is authentic and provides a solution to it. In doing so, a more general thought is given support by the approach used. Namely, the thought that our abilities to directly refer to things require some basic referential abilities exhibited in ostension and the use of demonstratives which, in their turn, rest upon our abilities to cooperate in performing non-communicative actions on our environment. Several concepts introduced in order to solve the initial problem can be used to articulate this thought in more detail

Can there ever be a theory of utterance interpretation?

In this paper, I tackle what appears to be a rather simple question: can there ever be a theory of utterance interpretation? It will be contended that a theory of utterance interpretation is not beyond the intellectual grasp of present-day pragmatists so much as it is a construct which lacks sense and is unintelligible. Although many of our most successful theories exhibit desiderata such as simplicity, completeness and explanatory power, it will be argued that these same desiderata are problematic when it is utterance interpretation that is the focus of theoretical efforts. The case in support of this claim sets out from a detailed analysis of the rational, intentional, holistic character of utterance interpretation and draws on the insights of the American philosopher Hilary Putnam. To the extent that a theory of utterance interpretation is not a difficult empirical possibility to realize so much as it is an endeavour which leads to an unintelligible outcome, we consider where this situation leaves pragmatists who have a substantial appetite for theory construction