REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics

This paper describes an architecture for robots that combines the complementary strengths of probabilistic graphical models and declarative programming to represent and reason with logic-based and probabilistic descriptions of uncertainty and domain knowledge. An action language is extended to support non-boolean fluents and non-deterministic causal laws. This action language is used to describe tightly-coupled transition diagrams at two levels of granularity, with a fine-resolution transition diagram defined as a refinement of a coarse-resolution transition diagram of the domain. The coarse-resolution system description, and a history that includes (prioritized) defaults, are translated into an Answer Set Prolog (ASP) program. For any given goal, inference in the ASP program provides a plan of abstract actions. To implement each such abstract action, the robot automatically zooms to the part of the fine-resolution transition diagram relevant to this action. A probabilistic representation of the uncertainty in sensing and actuation is then included in this zoomed fine-resolution system description, and used to construct a partially observable Markov decision process (POMDP). The policy obtained by solving the POMDP is invoked repeatedly to implement the abstract action as a sequence of concrete actions, with the corresponding observations being recorded in the coarse-resolution history and used for subsequent reasoning. The architecture is evaluated in simulation and on a mobile robot moving objects in an indoor domain, to show that it supports reasoning with violation of defaults, noisy observations and unreliable actions, in complex domains.Comment: 72 pages, 14 figure

Bayesian robot Programming

We propose a new method to program robots based on Bayesian inference and learning. The capacities of this programming method are demonstrated through a succession of increasingly complex experiments. Starting from the learning of simple reactive behaviors, we present instances of behavior combinations, sensor fusion, hierarchical behavior composition, situation recognition and temporal sequencing. This series of experiments comprises the steps in the incremental development of a complex robot program. The advantages and drawbacks of this approach are discussed along with these different experiments and summed up as a conclusion. These different robotics programs may be seen as an illustration of probabilistic programming applicable whenever one must deal with problems based on uncertain or incomplete knowledge. The scope of possible applications is obviously much broader than robotics

Bayesian Optimization Using Domain Knowledge on the ATRIAS Biped

Controllers in robotics often consist of expert-designed heuristics, which can be hard to tune in higher dimensions. It is typical to use simulation to learn these parameters, but controllers learned in simulation often don't transfer to hardware. This necessitates optimization directly on hardware. However, collecting data on hardware can be expensive. This has led to a recent interest in adapting data-efficient learning techniques to robotics. One popular method is Bayesian Optimization (BO), a sample-efficient black-box optimization scheme, but its performance typically degrades in higher dimensions. We aim to overcome this problem by incorporating domain knowledge to reduce dimensionality in a meaningful way, with a focus on bipedal locomotion. In previous work, we proposed a transformation based on knowledge of human walking that projected a 16-dimensional controller to a 1-dimensional space. In simulation, this showed enhanced sample efficiency when optimizing human-inspired neuromuscular walking controllers on a humanoid model. In this paper, we present a generalized feature transform applicable to non-humanoid robot morphologies and evaluate it on the ATRIAS bipedal robot -- in simulation and on hardware. We present three different walking controllers; two are evaluated on the real robot. Our results show that this feature transform captures important aspects of walking and accelerates learning on hardware and simulation, as compared to traditional BO.Comment: 8 pages, submitted to IEEE International Conference on Robotics and Automation 201

Control Systems and Robotics Outreach to Middle-school Girls: Approach, Results, and Suggestions

We conducted a three-day outreach camp focused on control systems and robotics for 8th grade girls from economically disadvantaged families. The overall objective of the camp was motivating the young girls to consider pursuing a career in engineering and sciences. The main focus of the camp were hands-on labs using LEGO Mindstorms EV3 kit. Students learned about programming, sensors, motors and put their skills to test by creating a mobile robot that took part in three contests: car racing, line following, and parallel parking. A pre- and post-camp survey indicated that although program did not predominantly change the girls’ excitement towards careers in engineering and sciences, it increased the girls’ knowledge and excitement towards robotics and control systems. Our results indicate that short camps help kindle the interests of young girls, but are not able to sway them to take on engineering/science careers. In the latter case, we hypothesize that long-term STEM-based programs (e.g., a quarter or year-long robotics course) might be more effective.Cockrell School of Engineerin

The Operation of Autonomous Mobile Robot Assistants in the Environment of Care Facilities Adopting a User-Centered Development Design

The successful development of autonomous mobile robot assistants depends significantly on the well-balanced reconcilements of the technically possible and the socially desirable. Based on empirical research 2 substantiated conclusions can be established for the suitability of "scenario-based design" (Rosson/Carroll 2003) for the successful development of mobile robot assistants and automated guided vehicles to be applied for service functions in stationary care facilities for seniors.User-Centered Technology Development, Knowledge-Transfer, Participative Assessment Methods, Robotics

Geometric reasoning

Cognitive robot systems are ones in which sensing and representation occur, from which task plans and tactics are determined. Such a robot system accomplishes a task after being told what to do, but determines for itself how to do it. Cognition is required when the work environment is uncontrolled, when contingencies are prevalent, or when task complexity is large; it is useful in any robotic mission. A number of distinguishing features can be associated with cognitive robotics, and one emphasized here is the role of artificial intelligence in knowledge representation and in planning. While space telerobotics may elude some of the problems driving cognitive robotics, it shares many of the same demands, and it can be assumed that capabilities developed for cognitive robotics can be employed advantageously for telerobotics in general. The top level problem is task planning, and it is appropriate to introduce a hierarchical view of control. Presented with certain mission objectives, the system must generate plans (typically) at the strategic, tactical, and reflexive levels. The structure by which knowledge is used to construct and update these plans endows the system with its cognitive attributes, and with the ability to deal with contingencies, changes, unknowns, and so on. Issues of representation and reasoning which are absolutely fundamental to robot manipulation, decisions based upon geometry, are discussed here, not AI task planning per se

Narrative based Postdictive Reasoning for Cognitive Robotics

Making sense of incomplete and conflicting narrative knowledge in the presence of abnormalities, unobservable processes, and other real world considerations is a challenge and crucial requirement for cognitive robotics systems. An added challenge, even when suitably specialised action languages and reasoning systems exist, is practical integration and application within large-scale robot control frameworks. In the backdrop of an autonomous wheelchair robot control task, we report on application-driven work to realise postdiction triggered abnormality detection and re-planning for real-time robot control: (a) Narrative-based knowledge about the environment is obtained via a larger smart environment framework; and (b) abnormalities are postdicted from stable-models of an answer-set program corresponding to the robot's epistemic model. The overall reasoning is performed in the context of an approximate epistemic action theory based planner implemented via a translation to answer-set programming.Comment: Commonsense Reasoning Symposium, Ayia Napa, Cyprus, 201

Special Issue “Cognitive Robotics”

Within the realm of new robotics, researchers have placed a great amount of effort into learning, understanding, and representing knowledge for task execution by robots. The goal is to develop robots that can help humans with daily tasks. Cognitive robots need to explore and understand their environment, choose a safe and human-aware course of action, and learn—not only from experience, but also through interaction. This Special Issue collects nine research papers in various fields related to Cognitive robotics. The relevance of the knowledge representation and its use by decision makers is present in the proposal by Martín et al. [1]. Specifically, the necessity of integrating behaviors and symbolic knowledge was solved by adding a graph-based working memory to a cognitive robotics architecture. The proposed framework has been successfully tested in robotics competitions such as the RoboCup and the European Robotics League. The aim of combining deliberative and reactive behaviors in a flexible way is also present in the work by González-Santamarta et al. [2]. In the MERLIN cognitive architecture, the process of integrating deliberative and behavioral-based mechanisms in robotics is normalized. The solution is empirically tested using a variation of the challenge defined in the SciRoc @ home competition. The relevance that cognitive robots can provide for improving task effectiveness and productivity in the industrial domain is highlighted in the work by Chacón et al. [3]. 8. (...)This work has been partially funded by the RTI2018-099522-B-C41 project, funded by the Spanish Ministerio de Ciencia, Innovación y Universidades and FEDER funds