We are developing a system for human-robot communication that enables people to communicate with robots in a natural way and is focused on solving problems in a shared space. Our strategy for developing this system is fundamentally data-driven: we use data from multiple input sources and train key components with various machine learning techniques. We developed a web application that is collecting data on how two humans communicate to accomplish a task, as well as a mobile laboratory that is instrumented to collect data on how two humans communicate to accomplish a task in a physically shared space. The data from these systems will be used to train and fine-tune the second stage of our system, in which the robot will be simulated through software. A physical robot will be used in the final stage of our project. We describe these instruments, a test-suite and performance metrics designed to evaluate and automate the data gathering process as well as evaluate an initial data set.
 Abstract Recently, the concept of human-robot collaboration has raised many research interests. Instead of robots replacing human workers in workplaces, human-robot collaboration allows human workers and robots working together in a shared manufacturing environment. Human-robot collaboration can release human workers from heavy tasks with assistive robots if effective communication channels between humans and robots are established. Although the communication channels between human workers and robots are still limited, gesture recognition has been effectively applied as the interface between humans and computers for long time. Covering some of the most important technologies and algorithms of gesture recognition, this paper is intended to provide an overview of the gesture recognition research and explore the possibility to apply gesture recognition in human-robot collaborative manufacturing. In this paper, an overall model of gesture recognition for human-robot collaboration is also proposed. There are four essential technical components in the model of gesture recognition for human-robot collaboration: sensor technologies, gesture identification, gesture tracking and gesture classification. Reviewed approaches are classified according to the four essential technical components. Statistical analysis is also presented after technical analysis. Towards the end of this paper, future research trends are outlined. We propose to directly map raw visual observations and text input to actions for instruction execution. While existing approaches assume access to structured environment representations or use a pipeline of separately trained models, we learn a single model to jointly reason about linguistic and visual input. We use reinforcement learning in a contextual bandit setting to train a neural network agent. To guide the agent's exploration, we use reward shaping with different forms of supervision. Our approach does not require intermediate representations, planning procedures, or training different models. We evaluate in a simulated environment, and show significant improvements over supervised learning and common reinforcement learning variants. Fetching items is an important problem for a social robot. It requires a robot to interpret a person's language and gesture and use these noisy observations to infer what item to deliver. If the robot could ask questions, it would help the robot be faster and more accurate in its task. Existing approaches either do not ask questions, or rely on fixed question-asking policies. To address this problem, we propose a model that makes assumptions about cooperation between agents to perform richer signal extraction from observations. This work defines a mathematical framework for an item-fetching domain that allows a robot to increase the speed and accuracy of its ability to interpret a person's requests by reasoning about its own uncertainty as well as processing implicit information (implicatures). We formalize the item-delivery domain as a Partially Observable Markov Decision Process (POMDP), and approximately solve this POMDP in real time. Our model improves speed and accuracy of fetching tasks by asking relevant clarifying questions only when necessary. To measure our model's improvements, we conducted a real world user study with 16 participants. Our method achieved greater accuracy and a faster interaction time compared to state-of-the-art baselines. Our model is 2.17 seconds faster (25 faster) than a state-of-the-art baseline, while being 2.1 more accurate. As robots become more ubiquitous, it is increasingly important for untrained users to be able to interact with them intuitively. In this work, we investigate how people refer to objects in the world during relatively unstructured communication with robots. We collect a corpus of deictic interactions from users describing objects, which we use to train language and gesture models that allow our robot to determine what objects are being indicated. We introduce a temporal extension to state-of-the-art hierarchical matching pursuit features to support gesture understanding, and demonstrate that combining multiple communication modalities more effectively capture user intent than relying on a single type of input. Finally, we present initial interactions with a robot that uses the learned models to follow commands. Recent studies in human–robot interaction (HRI) have investigated ways to harness the power of the crowd for the purpose of creating robot interaction logic through games and teleoperation interfaces. Sensor networks capable of observing human–human interactions in the real world provide a potentially valuable and scalable source of interaction data that can be used for designing robot behavior. To that end, we present here a fully automated method for reproducing observed real-world social interactions with a robot. The proposed method includes techniques for characterizing the speech and locomotion observed in training interactions, using clustering to identify typical behavior elements and identifying spatial formations using established HRI proxemics models. Behavior logic is learned based on discretized actions captured from the sensor data stream, using a naive Bayesian classifier. Finally, we propose techniques for reproducing robot speech and locomotion behaviors in a robust way, despite the natural variation of human behaviors and the large amount of sensor noise present in speech recognition. We show our technique in use, training a robot to play the role of a shop clerk in a simple camera shop scenario, and we demonstrate through a comparison experiment that our techniques successfully enabled the generation of socially appropriate speech and locomotion behavior. Notably, the performance of our technique in terms of correct behavior selection was found to be higher than the success rate of speech recognition, indicating its robustness to sensor noise.   It is natural for humans to work with abstract plans which are often an intuitive and concise way to represent a task. However, high level task descriptions contain symbols and concepts which need to be grounded within the environment if the plan is to be executed by an autonomous robot. The problem of learning the mapping between abstract plan symbols and their physical instances in the environment is known as the problem of physical symbol grounding. In this paper, we propose a framework for Grounding and Learning Instances through Demonstration and Eye tracking (GLIDE). We associate traces of task demonstration to a sequence of fixations which we call fixation programs and exploit their properties in order to perform physical symbol grounding. We formulate the problem as a probabilistic generative model and present an algorithm for computationally feasible inference over the proposed model. A key aspect of our work is that we estimate fixation locations within the environment which enables the appearance of symbol instances to be learnt. Instance learning is a crucial ability when the robot does not have any knowledge about the model or the appearance of the symbols referred to in the plan instructions. We have conducted human experiments and demonstrate that GLIDE successfully grounds plan symbols and learns the appearance of their instances, thus enabling robots to autonomously execute tasks in initially unknown environments. Robots require a broad range of interaction skills in order to work effectively alongside humans. They must have the ability to detect and recognize the actions and intentions of a person, produce functionally valid and situationally appropriate actions, and engage in social interactions through physical cues and dialog. However, social interactions with one of today’s robots will quickly become one-sided and repetitive, even after just a few minutes due to its shallow depth of knowledge and experience. This problem exposes weaknesses in the underlying traditional approaches that aim to pre-code responses to a limited number of inputs. We propose the use of crowdsourcing as a tool for the development of social robots that allow for rich, diverse and natural human-robot interaction. To enable crowdsourcing at a massive scale, we describe a newly implemented system that uses online virtual agents to collect data and then leverages the resulting corpus to train our robot behavior system for use on a real world task. As humans and robots collaborate together on spatial tasks, they must communicate clearly about the objects they are referencing. Communication is clearer when language is unambiguous which implies the use of spatial references and explicit perspectives. In this work, we contribute two studies to understand how people instruct a partner to identify and pick up objects on a table. We investigate spatial features and perspectives in human spatial references and compare word usage when instructing robots vs. instructing other humans. We then focus our analysis on the clarity of instructions with respect to perspective taking and spatial references. We find that only about 42 of instructions contain perspective-independent spatial references. There is a strong correlation between participants' accuracy in executing instructions and the perspectives that the instructions are given in, as well between accuracy and the number of spatial relations that were required for the instruction. We conclude that sentence complexity (in terms of spatial relations and perspective taking) impacts understanding, and we provide suggestions for automatic generation of spatial references. 