This paper reports recent progress on modeling the grounded co-acquisition of syntax and semantics of locative spatial language in developmental robots. Weshow how a learner robot can learn to produce and interpret spatial utterances in guided-learning interactions with a tutor robot (equipped with a system for producing English spatial phrases). The tutor guides the learning process by simplifying the challenges and complexity of utterances, givesfeedback, and gradually increases the complexity of the language to be learnt. Our experiments show promising results towards long-term, incremental acquisition of natural language in a process of co-development of syntax and semantics.

The number of multi-robot systems deployed in field applications has risen dramatically over the years. Nevertheless, supervising and operating multiple robots at once is a difficult task for a single operator to execute. In this paper we propose a novel approach for utilizing advising automated agents when assisting an operator to better manage a team of multiple robots in complex environments. We introduce the Myopic Advice Optimization (MYAO) Problem and exemplify its implementation using an agent for the Search And Rescue (SAR) task. Our intelligent advising agent was evaluated through extensive field trials, with 44 non-expert human operators and 10 low-cost mobile robots, in simulation and physical deployment, and showed a significant improvement in both team performance and the operator’s satisfaction.

Currently, there exists a need for simple, easily-accessible methods with which individuals lacking advanced technical training can expand and customize their robot's knowledge.  This work presents a means to satisfy that need, by abstracting the task of training robots to learn about the world around them as a vision- and dialogue-based game, I Spy .  In our implementation of I Spy , robots gradually learn about objects and the concepts that describe those objects through repeated gameplay.  We show that I Spy is an effective approach for teaching robots how to model new concepts using representations comprised of visual attributes.  The results from 255 test games show that the system was able to correctly determine which object the human had in mind 67% of the time.  Furthermore, a model evaluation showed that the system correctly understood the visual representations of its learned concepts with an average of 65% accuracy.  Human accuracy against the same evaluation standard was just 88% on average.

In this paper, we propose a weakly supervised Restricted Boltzmann Machines (WRBM) approach to deal with the task of semantic segmentation with only image-level labels available. In WRBM, its hidden nodes are divided into multiple blocks, and each block corresponds to a specific label. Accordingly, semantic segmentation can be directly modeled by learning the mapping from visible layer to the hidden layer of WRBM. Specifically, based on the standard RBM, we import another two terms to make full use of image-level labels and alleviate the effect of noisy labels. First, we expect the hidden response of each superpixel is suppressed on the labels outside its parent image-level label set, and a non-image-level label suppression term is formulated to implicitly import the image-level labels as weak supervision. Second, semantic graph propagation is employed to exploit the cooccurrence between visually similar regions and labels. Besides, we deal with the problems of label imbalance and diverse backgrounds by adapting the block size to the label frequency and appending hidden response blocks corresponding to backgrounds respectively. Extensive experiments on two real-world datasets demonstrate the good performance of our approach compared with some state-of-the-art methods.

Current motion planners, such as the ones available in ROS MoveIt, can solve difficult motion planning problems. However, these planners are not practical in unstructured, rapidly-changing environments. First, they assume that the environment is well-known, and static during planning and execution. Second, they do not support temporal constraints, which are often important for synchronization between a robot and other actors. Third, because many popular planners generate completely new trajectories for each planning problem, they do not allow for representing persistent control policy information associated with a trajectory across planning problems. We present Chekhov, a reactive, integrated motion planning and execution system that addresses these problems. Chekhov uses a Tube-based Roadmap in which the edges of the roadmap graph are families of trajectories called flow tubes, rather than the single trajectories commonly used in roadmap systems. Flow tubes contain control policy information about how to move through the tube, and also represent the dynamic limits of the system, which imply temporal constraints. This, combined with an incremental APSP algorithm for quickly finding paths in the roadmap graph, allows Chekhov to operate in rapidly changing environments. Testing in simulation, and with a robot testbed has shown improvement in planning speed and motion predictability over current motion planners.

Inverse optimal control (IOC) is a powerful approach for learning robotic controllers from demonstration that estimates a cost function which rationalizes demonstrated control trajectories. Unfortunately, its applicability is difficult in settings where optimal control can only be solved approximately. While local IOC approaches have been shown to successfully learn cost functions in such settings, they rely on the availability of good reference trajectories, which might not be available at test time. We address the problem of using IOC in these computationally challenging control tasks by using a graph-based discretization of the trajectory space. Our approach projects continuous demonstrations onto this discrete graph, where a cost function can be tractably learned via IOC. Discrete control trajectories from the graph are then projected back to the original space and locally optimized using the learned cost function. We demonstrate the effectiveness of the approach with experiments conducted on two 7-degree of freedom robotic arms.

Multi-robot inverse reinforcement learning (mIRL) is broadly useful for learning, from observations, the behaviors of multiple robots executing fixed trajectories and interacting with each other. In this paper, we relax a crucial assumption in IRL to make it better suited for wider robotic applications: we allow the transition functions of other robots to be stochastic and do not assume that the transition error probabilities are known to the learner. Challenged by occlusion where large portions of others' state spaces are fully hidden, we present a new approach that maps stochastic transitions to distributions over features. Then, the underconstrained problem is solved using nonlinear optimization that maximizes entropy to learn the transition function of each robot from occluded observations. Our methods represent significant and first steps toward making mIRL pragmatic.

Knowledge bases (KBs) are often greatly incomplete, necessitating a demand for KB completion. A promising approach is to embed KBs into latent spaces and make inferences by learning and operating on latent representations. Such embedding models, however, do not make use of any rules during inference and hence have limited accuracy. This paper proposes a novel approach which incorporates rules seamlessly into embedding models for KB completion. It formulates inference as an integer linear programming (ILP) problem, with the objective function generated from embedding models and the constraints translated from rules. Solving the ILP problem results in a number of facts which 1) are the most preferred by the embedding models, and 2) comply with all the rules. By incorporating rules, our approach can greatly reduce the solution space and significantly improve the inference accuracy of embedding models. We further provide a slacking technique to handle noise in KBs, by explicitly modeling the noise with slack variables. Experimental results on two publicly available data sets show that our approach significantly and consistently outperforms state-of-the-art embedding models in KB completion. Moreover, the slacking technique is effective in identifying erroneous facts and ambiguous entities, with a precision higher than 90%.

Existing techniques for inference in probabilistic logic programs are sequential: they first compute the relevant propositional formula for the query of interest, then compile it into a tractable target representation and finally, perform weighted model counting on the resulting representation. We propose Tp-compilation, a new inference technique based on forward reasoning. Tp-compilation proceeds incrementally in that it interleaves the knowledge compilation step for weighted model counting with forward reasoning on the logic program. This leads to a novel anytime algorithm that provides hard bounds on the inferred probabilities. Furthermore, an empirical evaluation shows that Tp-compilation effectively handles larger instances of complex real-world problems than current sequential approaches, both for exact and for anytime approximate inference.

We present Saul, a new probabilistic programming language designed to address some of the shortcomings of programming languages that aim at advancing and simplifying the development of AI systems. Such languages need to interact with messy, naturally occurring data, to allow a programmer to specify what needs to be done at an appropriate level of abstraction rather than at the data level, to be developed on a solid theory that supports moving to and reasoning at this level of abstraction and, finally, to support flexible integration of these learning and inference models within an application program. Saul is an object-functional programming language written in Scala that facilitates these by (1) allowing a programmer to learn, name and manipulate named abstractions over relational data; (2) supporting seamless incorporation of trainable (probabilistic or discriminative) components into the program, and (3) providing a level of inference over trainable models to support composition and make decisions that respect domain and application constraints. Saul is developed over a declaratively defined relational data model, can use piecewise learned factor graphs with declaratively specified learning and inference objectives, and it supports inference over probabilistic models augmented with declarative knowledge-based constraints.We describe the key constructs of Saul and exemplify its use in developing applications that require relational feature engineering and structured output prediction.