Intention Inference and Decision Making with Hierarchical Gaussian Process Dynamics Models

Anticipation is crucial for fluent human-robot interaction, which allows a robot to independently coordinate its actions with human beings in joint activities. An anticipatory robot relies on a predictive model of its human partners, and selects its own action according to the model's predictions. Intention inference and decision making are key elements towards such anticipatory robots. In this thesis, we present a machine-learning approach to intention inference and decision making, based on Hierarchical Gaussian Process Dynamics Models (H-GPDMs). We first introduce the H-GPDM, a class of generic latent-variable dynamics models. The H-GPDM represents the generative process of complex human movements that are directed by exogenous driving factors. Incorporating the exogenous variables in the dynamics model, the H-GPDM achieves improved interpretation, analysis, and prediction of human movements. While exact inference of the exogenous variables and the latent states is intractable, we introduce an approximate method using variational Bayesian inference, and demonstrate the merits of the H-GPDM in three different applications of human movement analysis. The H-GPDM lays a foundation for the following studies on intention inference and decision making. Intention inference is an essential step towards anticipatory robots. For this purpose, we consider a special case of the H-GPDM, the Intention-Driven Dynamics Model (IDDM), which considers the human partners' intention as exogenous driving factors. The IDDM is applicable to intention inference from observed movements using Bayes' theorem, where the latent state variables are marginalized out. As most robotics applications are subject to real-time constraints, we introduce an efficient online algorithm that allows for real-time intention inference. We show that the IDDM achieved state-of-the-art performance in intention inference using two human-robot interaction scenarios, i.e., target prediction for robot table tennis and action recognition for interactive robots. Decision making based on a time series of predictions allows a robot to be proactive in its action selection, which involves a trade-off between the accuracy and confidence of the prediction and the time for executing a selected action. To address the problem of action selection and optimal timing for initiating the movement, we formulate the anticipatory action selection using Partially Observable Markov Decision Process, where the H-GPDM is adopted to update belief state and to estimate transition model. We present two approaches to policy learning and decision making, and show their effectiveness using human-robot table tennis. In addition, we consider decision making solely based on the preference of the human partners, where observations are not sufficient for reliable intention inference. We formulate it as a repeated game and present a learning approach to safe strategies that exploit the humans' preferences. The learned strategy enables action selection when reliable intention inference is not available due to insufficient observation, e.g., for a robot to return served balls from a human table tennis player. In this thesis, we use human-robot table tennis as a running example, where a key bottleneck is the limited amount of time for executing a hitting movement. Movement initiation usually requires an early decision on the type of action, such as a forehand or backhand hitting movement, at least 80ms before the opponent has hit the ball. The robot, therefore, needs to be anticipatory and proactive of the opponent's intended target. Using the proposed methods, the robot can predict the intended target of the opponent and initiate an appropriate hitting movement according to the prediction. Experimental results show that the proposed intention inference and decision making methods can substantially enhance the capability of the robot table tennis player, using both a physically realistic simulation and a real Barrett WAM robot arm with seven degrees of freedom

Early Recognition of Human Activities from First-Person Videos Using Onset Representations

In this paper, we propose a methodology for early recognition of human activities from videos taken with a first-person viewpoint. Early recognition, which is also known as activity prediction, is an ability to infer an ongoing activity at its early stage. We present an algorithm to perform recognition of activities targeted at the camera from streaming videos, making the system to predict intended activities of the interacting person and avoid harmful events before they actually happen. We introduce the novel concept of 'onset' that efficiently summarizes pre-activity observations, and design an approach to consider event history in addition to ongoing video observation for early first-person recognition of activities. We propose to represent onset using cascade histograms of time series gradients, and we describe a novel algorithmic setup to take advantage of onset for early recognition of activities. The experimental results clearly illustrate that the proposed concept of onset enables better/earlier recognition of human activities from first-person videos

Just Because You\u27re Offended Doesn\u27t Mean You\u27re In The Right: A Perspective on Language, Comedy, and Ethics

Some humor is offensive, but does this convey a moral constraint on what comedians can include in their jokes? Using stand up bits and reflections on comedy from George Carlin, Louis C.K., and Doug Stanhope, various philosophies of humor, and the linguistic philosophy of H.P. Grice, I explore the given question and attempt to settle the disputes about when it is prudent to be offended, in what ways comedians should be allowed to offend, and whether or not words can hurt just as much as sticks and stones

Making GATT Dolphin-Safe: Trade and the Environment

The Swedish municipalities have traditionally, as suppliers of gas, electricity and district heating to end-use consumers, been one of the key actors of national energy politics. Yet, although Swedish district heating systems typically are owned by the public, neither electricity nor heat production is always under public control. This has historically imposed limitations on municipal plans for own CHP (Combined Heat and Power) plants and district heating systems. Previous research in the field have established that an organisational division between companies active in either heat or electricity supply was one of the most serious challenges for municipal engagement in energy affairs after WW2. Yet, in many cases municipal energy departments have engaged in joint cooperation projects with process industries and power companies, in order to utilize technical expertise and industrial waste heat for the build-up of the local energy system. Processes of initiating, developing and maintaining such cooperation projects - and the erection of municipal CHP plats - are focused upon here. The purpose of this research project is to describe and analyse the roles played by two municipalities (Helsingborg and Gävle) in the years 1945-1983. The specific research questions were as follows: How were different socio-technical systems for electricity and heat supply introduced and how were different alternatives received within the frames of the municipal organisations? What was the impact of competition between different alternatives on the development of the local energy systems? It shows that strategies for municipal-industrial cooperation are vital for the possibilities of municipal energy companies to come to terms with the organisational gulf between power and heat companies. It is argued that the forming of strategical alliances can illustrate how municipal organisations not always act in autonomous positions. It can rather be the other way round - it is strategies for cooperation and competition that make the municipality a key actor in the local energy system

Unratified Treaties, Domestic Politics, and the U.S. Constitution

Under contemporary treaty practice, a nation\u27s signature of a treaty typically does not make the nation a party to the treaty. Rather, nations become parties to treaties through an act of ratification or accession, which sometimes occurs long after signature. Nevertheless, Article 18 of the Vienna Convention on the Law of Treaties, which many commentators regard as reflecting customary international law, provides that when a nation signs a treaty it is obligated to refrain from actions that would defeat the object and purpose of the treaty until such time as it makes clear its intent not to become a party to the treaty. Some commentators further claim that this object and purpose obligation means that a nation that has signed a treaty is prohibited either from violating the treaty altogether or from violating the treaty\u27s core or important provisions. Attaching legal obligations to the signing of a treaty, however, poses a constitutional issue for the United States because the U.S. Constitution divides the treaty power between the President and Senate, whereas only the President and his agents are involved in the signing of treaties. This constitutional issue has broad significance because, for a variety of political and other reasons, the United States often signs but fails to ratify treaties. The constitutional issue is not eliminated by the president\u27s authority to conclude sole executive agreements, since both constitutional structure and historical practice suggest that this authority is significantly narrower than the power of the President and Senate to jointly conclude treaties. The drafting history of Article 18, however, offers a partial solution to this difficulty, since it indicates that the object and purpose obligation was intended to prohibit only actions that would substantially undermine the parties\u27 ability to comply with or benefit from a treaty after ratification, an obligation that has little relevance to the treaties for which signing obligations would be most constitutionally problematic