A shared understanding of language will assist natural interactions between humans and artificial agents or robots undertaking collaborative tasks. An important domain for collaborative armed robots is interacting with humans and objects on a table, for example, picking, placing, or handing over a variety of objects. Such tasks combine object representation and movement planning in the geometric domain with abstract reasoning about symbolic spatial representations. This paper presents an initial study in which a human partner teaches the robot words for spatial relationships by providing exemplars and indicating where words may be used over the surface. This study demonstrates how robots can be taught the words required for these tasks in a quick and simple manner that allows the concepts to be generalizable over different surfaces, objects, and object placements.

It is possible to build highly accurate and mobile robots able to perceive their environment. Such robots can be used to assist humans in everyday tasks to reduce their workload. As a consequence, communication and interaction between humans and robots is becoming more important. In this paper we present a system for human-robot collaboration for on-table tasks. Due to its extensible design our system serves us as a base for further investigations in human-robot collaboration. We used the system to implement five action-selection strategies for the robot:proactive, autonomous, reactive, human-requested and human-commands. We conducted a pilot study to compare the interaction modes during a task in which the human and the robot build a bridge using blocks. The results of the pilot study indicate that for the simple bridge-building task, people prefer to interact with a robot using the proactive action-selection strategy. The completion of the pilot study indicates that the system is useful for human-robot collaboration studies. Several limitations have been identified that will be addressed in future developments.

In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot without environment-specific training. Motivated by their ongoing success in various visual recognition tasks, we build our system upon a state-of-the-art convolutional network. We overcome its closed-set limitations by complementing the network with a series of one-vs-all classifiers that can learn to recognize new semantic classes online. Prior domain knowledge is incorporated by embedding the classification system into a Bayesian filter framework that also ensures temporal coherence. We evaluate the classification accuracy of the system on a robot that maps a variety of places on our campus in real-time. We show how semantic information can boost robotic object detection performance and how the semantic map can be used to modulate the robot's behaviour during navigation tasks. The system is made available to the community as a ROS module.

This paper shows that by using only symbolic language phrases, a mobile robot can purposefully navigate to specified rooms in previously unexplored environments. The robot intelligently organises a symbolic language description of the unseen environment and “imagines” a representative map, called the abstract map. The abstract map is an internal representation of the topological structure and spatial layout of symbolically defined locations. To perform goal-directed exploration, the abstract map creates a high-level semantic plan to reason about spaces beyond the robot's known world. While completing the plan, the robot uses the metric guidance provided by a spatial layout, and grounded observations of door labels, to efficiently guide its navigation. The system is shown to complete exploration in unexplored spaces by travelling only 13.3% further than the optimal path.

In this paper we present for the first time a complete symbolic navigation system that performs goal-directed exploration to unfamiliar environments on a physical robot. We introduce a novel construct called the abstract map to link provided symbolic spatial information with observed symbolic information and actual places in the real world. Symbolic information is observed using a text recognition system that has been developed specifically for the application of reading door labels. In the study described in this paper, the robot was provided with a floor plan and a destination. The destination was specified by a room number, used both in the floor plan and on the door to the room. The robot autonomously navigated to the destination using its text recognition, abstract map, mapping, and path planning systems. The robot used the symbolic navigation system to determine an efficient path to the destination, and reached the goal in two different real-world environments. Simulation results show that the system reduces the time required to navigate to a goal when compared to random exploration.

Previous studies have shown how Lingodroids, language learning mobile robots, learn terms for space and time, connecting their personal maps of the world to a publically shared language. One caveat of previous studies was that the robots shared the same cognitive architecture, identical in all respects from sensors to mapping systems. In this paper we investigate the question of how terms for space can be developed between robots that have fundamentally different sensors and spatial representations. In the real world, communication needs to occur between agents that have different embodiment and cognitive capabilities, including different sensors, different representations of the world, and different species (including humans). The novel aspects of these studies is that one robot uses a forward facing camera to estimate appearance and uses a biologically inspired continuous attractor network to generate a topological map; the other robot uses a laser scanner to estimate range and uses a probabilistic filter approach to generate an occupancy grid. The robots hold conversations in different locations to establish a shared language. Despite their different ways of sensing and mapping the world, the robots are able to create coherent lexicons for the space around them.

For humans and robots to communicate using natural language it is necessary for the robots to develop concepts and associated terms that correspond to the human use of words. Time and space are foundational concepts in human language, and to develop a set of words that correspond to human notions of time and space, it is necessary to take into account the way that they are used in natural human conversations, where terms and phrases such as `soon', `in a while', or `near' are often used. We present language learning robots called Lingodroids that can learn and use simple terms for time and space. In previous work, the Lingodroids were able to learn terms for space. In this work we extend their abilities by adding temporal variables which allow them to learn terms for time. The robots build their own maps of the world and interact socially to form a shared lexicon for location and duration terms. The robots successfully use the shared lexicons to communicate places and times to meet again.

The Lingodroids are a pair of mobile robots that evolve a language for places and relationships between places (based on distance and direction). Each robot in these studies has its own understanding of the layout of the world, based on its unique experiences and exploration of the environment. Despite having different internal representations of the world, the robots are able to develop a common lexicon for places, and then use simple sentences to explain and understand relationships between places even places that they could not physically experience, such as areas behind closed doors. By learning the language, the robots are able to develop representations for places that are inaccessible to them, and later, when the doors are opened, use those representations to perform goal-directed behavior.