In this work we present a method that allows to learn a cost function for motion planning of human-robot collaborative manipulation tasks where the human and the robot manipulate objects simultaneously in close proximity. Our approach is based on inverse optimal control which enables, considering a set of demonstrations, to find a cost function balancing different features. The cost function that is recovered from the human demonstrations is composed of elementary features, which are designed to encode notions such as safely, legibility and efficiency of the manipulation motions. We demonstrate the approach on data gathered from motion capture of human-human manipulation in close proximity of blocks on a table. To demonstrate the feasibility and efficacy of our approach we provide initial test results consisting of learning a cost function and then planning for the human kinematic model used in the learning phase.

As robot algorithms for manipulation and navigation advance and robot hardware is becoming more robust and readily available, industry demands robots to perform more sophisticated tasks in our homes and factories. For many years, direct teleoperation was the most common and traditional form of control for robots. However, due to the complexity of robot motion, human operators must focus most of their attention on solving low-level motion control which leads to their heightened cognitive load. In this abstract, we propose a goal-directed approach to programming robots by providing a tool to model the world and provide goal states for a given task. Operators will be able to set the initial positions of objects and their affordances along with their goal positions by imposing three dimensional (3D) templates on point clouds. Robots will solve the given task using the combination of task and motion planning algorithms.

Robotic agents often map perceptual input to simplified representations that do not reflect the complexity and richness of the world. This simplification is due in large part to the limitations of planning algorithms, which fail in large stochastic state spaces on account of the well-known "curse of dimensionality." Existing approaches to address this problem fail to prevent autonomous agents from considering many actions which would be obviously irrelevant to a human solving the same problem. We formalize the notion of affordances as knowledge added to an Markov Decision Process (MDP) that prunes actions in a state- and reward- general way. This pruning significantly reduces the number of state-action pairs the agent needs to evaluate in order to act near-optimally. We demonstrate our approach in the Minecraft domain as a model for robotic tasks, showing significant increase in speed and reduction in state-space exploration during planning. Further, we provide a learning framework that enables an agent to learn affordances through experience, opening the door for agents to learn to adapt and plan through new situations. We provide preliminary results indicating that the learning process effectively produces affordances that help solve an MDP faster, suggesting that affordances serve as an effective, transferable piece of knowledge for planning agents in large state spaces.

The goal of this research is to enable robots to learn new things from everyday people. For years, the AI and Robotics community has sought to enable robots to efficiently learn new skills from a knowledgeable human trainer, and prior work has focused on several important technical problems. This vast amount of research in the field of robot Learning by Demonstration has by and large only been evaluated with expert humans, typically the system's designer. Thus, neglecting a key point that this interaction takes place within a social structure that can guide and constrain the learning problem. %Moreover, we We believe that addressing this point will be essential for developing systems that can learn from everyday people that are not experts in Machine Learning or Robotics. Our work focuses on new research questions involved in letting robots learn from everyday human partners (e.g., What kind of input do people want to provide a machine learner? How does their mental model of the learning process affect this input? What interfaces and interaction mechanisms can help people provide better input from a machine learning perspective?) Often our research begins with an investigation into the feasibility of a particular machine learning interaction, which leads to a series of research questions around re-designing both the interaction and the algorithm to better suit learning with end-users. We believe this equal focus on both the Machine Learning and the HRI contributions are key to making progress toward the goal of machines learning from humans. In this abstract we briefly overview four different projects that highlight our HRI approach to the problem of Learning from Demonstration.

In this paper we explain how IETAL agents learn their environment, and how they build their intrinsic, internal representation of it, which they then use to build their expectations when on quest to satisfy its active drives. As environments change (with or without other agents present in them), the agents learn to new and “forget” irrelevant, “old” associations made. We discuss the concept of emotional context of associations, and show a gallery of simulations of behaviors in small multiagent societies.

Clinical practice guidelines (CPGs) were originally designed to help with evidence-based management of a single disease and such a single disease focus has impacted research on CPG computerization. This computerization is mostly concerned with supporting different representation formats and identifying potential inconsistencies in the definitions of CPGs. However, one of the biggest challenges facing physicians is the personalization of multiple CPGs to comorbid patients. Various research initiatives propose ways of mitigating adverse interactions in concurrently applied CPGs, however, there are no attempts to develop a generalized framework for mitigation that captures generic characteristics of the problem while handling nuances such as precedence relationships. In this paper we present our research towards developing a mitigation framework that relies on a first-order logic-based representation and related theorem proving and model finding techniques. The application of the proposed framework is illustrated with a simple clinical example.

Empirical studies with humans and agents demonstrate that the nature and forms of information required by the human differ depending on the design of the relationship between the participants — a relationship that is sometimes characterised using the concept of levels of autonomy, though the usefulness of that characterisation has recently been questioned. Therefore, understanding how people work with automation and how to design automated systems to better support people, is a field long studied, but of growing importance. Our current work seeks to contribute to the design of representations and algorithms that can be deployed in such contexts.

Socially assistive robotics (SAR) has the potential to combinethe massive replication and standardization of computertechnology with the benefits of learning in a social and tangible(hands-on) context. We are developing HRI methodsfor SAR systems designed to supplement the efforts of humanteachers to personalize education in the classroom. Thisabstract defines and proposes solutions to the computationalchallenges inherent in accomplishing differentiated and personalizededucation utilizing SAR in real-world classrooms. We aim to design robotic systems that are compelling, assistchildren in achieving educational goals, and mitigate developmentalchallenges in a classroom context. To do so, ourapproach must be deeply informed by the needs of our targetusers, children, at all stages of development, and mustadapt to a variety of special needs. In this abstract, we discussmotivation and computational methods for personalizedSAR systems for general, special needs, and mixed multichildeducation contexts. We focus on the personalizationand adaptation of curriculum, feedback, and robot character.

We study the scheduling of human-robot teams where the human and robotic agents share decision-making authority over scheduling decisions. Our goal is to design AI scheduling techniques that account for how people make decisions under different control schema.

The goal of transfer is to use knowledge obtained by solving one task to improvea robot's (or software agent's) performance in future tasks. In general, we do not expect this to work; for transfer to be feasible, there must be something in common between the source task(s) and goal task(s). The question at the core of the transfer learning enterprise is therefore: what makes two tasks related?, or more generally, how do you define a family of related tasks? Given a precise definition of how a particular family of tasks is related, we can formulate clear optimizationmethods for selecting source tasks and determining what knowledge should be imported from the source task(s), and how it should be used in the target task(s). This paper describes one model that has appeared in several different research scenarios where an agent is faced with afamily of tasks that have similar, but not identical, dynamics (or reward functions). For example, a human learning to play baseball may, over the course of their career,be exposed to several different bats, each with slightly different weights and lengths.A human who has learned to play baseball well with one bat would be expected to be able to pick up any similar bat and use it.Similarly, when learning to drive a car, one may learn in more than one car, and then be expected to be able to drive any make and model of car (within reasonablevariations) with little or no relearning. These examples are instances of exactly the kind of flexible, reliable,and sample-efficient behavior that we should be aiming to achieve in robotics applications. One way to model such a family of tasks is to posit that they are generated by asmall set of latent parameters (e.g., the length and weight of the bat, or parametersdescribing the various physical properties of the car's steering system and clutch) thatare fixed for each problem instance (e.g., for each bat, or car), but are not directlyobservable by the agent. Defining a distributionover these latent parameters results in a family of related tasks, and transferis feasible to the extent that the number of latent variables is small, the task dynamics(or reward function) vary smoothly with them, and to the extent to which they can eitherbe ignored or identified using transition data from the task.This model has appeared under several different names in the literature; we refer to it as a hidden-parameterMarkov decision process (or HIP-MDP).