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

A POMDP approach to Affective Dialogue Modeling

We propose a novel approach to developing a dialogue model that is able to take into account some aspects of the user's affective state and to act appropriately. Our dialogue model uses a Partially Observable Markov Decision Process approach with observations composed of the observed user's affective state and action. A simple example of route navigation is explained to clarify our approach. The preliminary results showed that: (1) the expected return of the optimal dialogue strategy depends on the correlation between the user's affective state & the user's action and (2) the POMDP dialogue strategy outperforms five other dialogue strategies (the random, three handcrafted and greedy action selection strategies)

Robots that Say ‘No’. Affective Symbol Grounding and the Case of Intent Interpretations

© 2017 IEEE. This article has been accepted for publication in a forthcoming issue of IEEE Transactions on Cognitive and Developmental Systems. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Modern theories on early child language acquisition tend to focus on referential words, mostly nouns, labeling concrete objects, or physical properties. In this experimental proof-of-concept study, we show how nonreferential negation words, typically belonging to a child's first ten words, may be acquired. A child-like humanoid robot is deployed in speech-wise unconstrained interaction with naïve human participants. In agreement with psycholinguistic observations, we corroborate the hypothesis that affect plays a pivotal role in the socially distributed acquisition process where the adept conversation partner provides linguistic interpretations of the affective displays of the less adept speaker. Negation words are prosodically salient within intent interpretations that are triggered by the learner's display of affect. From there they can be picked up and used by the budding language learner which may involve the grounding of these words in the very affective states that triggered them in the first place. The pragmatic analysis of the robot's linguistic performance indicates that the correct timing of negative utterances is essential for the listener to infer the meaning of otherwise ambiguous negative utterances. In order to assess the robot's performance thoroughly comparative data from psycholinguistic studies of parent-child dyads is needed highlighting the need for further interdisciplinary work.Peer reviewe

Potential climatic transitions with profound impact on Europe

We discuss potential transitions of six climatic subsystems with large-scale impact on Europe, sometimes denoted as tipping elements. These are the ice sheets on Greenland and West Antarctica, the Atlantic thermohaline circulation, Arctic sea ice, Alpine glaciers and northern hemisphere stratospheric ozone. Each system is represented by co-authors actively publishing in the corresponding field. For each subsystem we summarize the mechanism of a potential transition in a warmer climate along with its impact on Europe and assess the likelihood for such a transition based on published scientific literature. As a summary, the ‘tipping’ potential for each system is provided as a function of global mean temperature increase which required some subjective interpretation of scientific facts by the authors and should be considered as a snapshot of our current understanding. <br/

ASSESSING THE RISKS OF A FUTURE RAPID LARGE SEA LEVEL RISE: A REVIEW

Our aim is to make an appropriate characterization and interpretation of the risk problem of rapid large sea level rise that reflects the very large uncertainty in present day knowledge concerning this possibility, and that will be useful in informing discussion about risk management approaches. We consider mainly the potential collapse of the West Antarctic ice sheet as the source of such a sea level rise. Our review, characterization and interpretation of the risk makes us conclude that the risk of a rapid large sea level rise is characterized by potentially catastrophic consequences and high epistemic uncertainty; effective risk management must involve highly adaptive management regimes, vulnerability reduction, and prompt development of capabilities for precautionary reduction of climate change forcings.sea level rise, West Antarctic ice sheet, climate change, adaptive management, epistemic uncertainty, risk management arenas, vulnerability

A tractable DDN-POMDP Approach to Affective Dialogue Modeling for General Probabilistic Frame-based Dialogue Systems

We propose a new approach to developing a tractable affective dialogue model for general probabilistic frame-based dialogue systems. The dialogue model, based on the Partially Observable Markov Decision Process (POMDP) and the Dynamic Decision Network (DDN) techniques, is composed of two main parts, the slot level dialogue manager and the global dialogue manager. Our implemented dialogue manager prototype can handle hundreds of slots; each slot might have many values. A first evaluation of the slot level dialogue manager (1-slot case) showed that with a 95% confidence level the DDN-POMDP dialogue strategy outperforms three simple handcrafted dialogue strategies when the user's action error is induced by stress

Welcoming Robots into the Moral Circle: A Defence of Ethical Behaviourism

Can robots have significant moral status? This is an emerging topic of debate among roboticists and ethicists. This paper makes three contributions to this debate. First, it presents a theory – ‘ethical behaviourism’ – which holds that robots can have significant moral status if they are roughly performatively equivalent to other entities that have significant moral status. This theory is then defended from seven objections. Second, taking this theoretical position onboard, it is argued that the performative threshold that robots need to cross in order to be afforded significant moral status may not be that high and that they may soon cross it (if they haven’t done so already). Finally, the implications of this for our procreative duties to robots are considered, and it is argued that we may need to take seriously a duty of ‘procreative beneficence’ towards robots

Progress in AI Planning Research and Applications

Planning has made significant progress since its inception in the 1970s, in terms both of the efficiency and sophistication of its algorithms and representations and its potential for application to real problems. In this paper we sketch the foundations of planning as a sub-field of Artificial Intelligence and the history of its development over the past three decades. Then some of the recent achievements within the field are discussed and provided some experimental data demonstrating the progress that has been made in the application of general planners to realistic and complex problems. The paper concludes by identifying some of the open issues that remain as important challenges for future research in planning