Inductive learning of answer set programs for autonomous surgical task planning

The quality of robot-assisted surgery can be improved and the use of hospital resources can be optimized by enhancing autonomy and reliability in the robot’s operation. Logic programming is a good choice for task planning in robot-assisted surgery because it supports reliable reasoning with domain knowledge and increases transparency in the decision making. However, prior knowledge of the task and the domain is typically incomplete, and it often needs to be refined from executions of the surgical task(s) under consideration to avoid sub-optimal performance. In this paper, we investigate the applicability of inductive logic programming for learning previously unknown axioms governing domain dynamics. We do so under answer set semantics for a benchmark surgical training task, the ring transfer. We extend our previous work on learning the immediate preconditions of actions and constraints, to also learn axioms encoding arbitrary temporal delays between atoms that are effects of actions under the event calculus formalism. We propose a systematic approach for learning the specifications of a generic robotic task under the answer set semantics, allowing easy knowledge refinement with iterative learning. In the context of 1000 simulated scenarios, we demonstrate the significant improvement in performance obtained with the learned axioms compared with the hand-written ones; specifically, the learned axioms address some critical issues related to the plan computation time, which is promising for reliable real-time performance during surgery

Dagstuhl News January - December 2006

"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic

Spatial representation for planning and executing robot behaviors in complex environments

Robots are already improving our well-being and productivity in different applications such as industry, health-care and indoor service applications. However, we are still far from developing (and releasing) a fully functional robotic agent that can autonomously survive in tasks that require human-level cognitive capabilities. Robotic systems on the market, in fact, are designed to address specific applications, and can only run pre-defined behaviors to robustly repeat few tasks (e.g., assembling objects parts, vacuum cleaning). They internal representation of the world is usually constrained to the task they are performing, and does not allows for generalization to other scenarios. Unfortunately, such a paradigm only apply to a very limited set of domains, where the environment can be assumed to be static, and its dynamics can be handled before deployment. Additionally, robots configured in this way will eventually fail if their "handcrafted'' representation of the environment does not match the external world. Hence, to enable more sophisticated cognitive skills, we investigate how to design robots to properly represent the environment and behave accordingly. To this end, we formalize a representation of the environment that enhances the robot spatial knowledge to explicitly include a representation of its own actions. Spatial knowledge constitutes the core of the robot understanding of the environment, however it is not sufficient to represent what the robot is capable to do in it. To overcome such a limitation, we formalize SK4R, a spatial knowledge representation for robots which enhances spatial knowledge with a novel and "functional" point of view that explicitly models robot actions. To this end, we exploit the concept of affordances, introduced to express opportunities (actions) that objects offer to an agent. To encode affordances within SK4R, we define the "affordance semantics" of actions that is used to annotate an environment, and to represent to which extent robot actions support goal-oriented behaviors. We demonstrate the benefits of a functional representation of the environment in multiple robotic scenarios that traverse and contribute different research topics relating to: robot knowledge representations, social robotics, multi-robot systems and robot learning and planning. We show how a domain-specific representation, that explicitly encodes affordance semantics, provides the robot with a more concrete understanding of the environment and of the effects that its actions have on it. The goal of our work is to design an agent that will no longer execute an action, because of mere pre-defined routine, rather, it will execute an actions because it "knows'' that the resulting state leads one step closer to success in its task

Imitation and social learning for synthetic characters

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2004.Includes bibliographical references (p. 137-149).We want to build animated characters and robots capable of rich social interactions with humans and each other, and who are able to learn by observing those around them. An increasing amount of evidence suggests that, in human infants, the ability to learn by watching others, and in particular, the ability to imitate, could be crucial precursors to the development of appropriate social behavior, and ultimately the ability to reason about the thoughts, intents, beliefs, and desires of others. We have created a number of imitative characters and robots, the latest of which is Max T. Mouse, an anthropomorphic animated mouse character who is able to observe the actions he sees his friend Morris Mouse performing, and compare them to the actions he knows how to perform himself. This matching process allows Max to accurately imitate Morris's gestures and actions, even when provided with limited synthetic visual input. Furthermore, by using his own perception, motor, and action systems as models for the behavioral and perceptual capabilities of others (a process known as Simulation Theory in the cognitive literature), Max can begin to identify simple goals and motivations for Morris's behavior, an important step towards developing characters with a full theory of mind. Finally, Max can learn about unfamiliar objects in his environment, such as food and toys, by observing and correctly interpreting Morris's interactions with these objects, demonstrating his ability to take advantage of socially acquired information.by Daphna Buchsbaum.S.M

Path planning algorithms for autonomous navigation of a non-holonomic robot in unstructured environments

openPath planning is a crucial aspect of autonomous robot navigation, enabling robots to efficiently and safely navigate through complex environments. This thesis focuses on autonomous navigation for robots in dynamic and uncertain environments. In particular, the project aims to analyze the localization and path planning problems. A fundamental review of the existing literature on path planning algorithms has been carried on. Various factors affecting path planning, such as sensor data fusion, map representation, and motion constraints, are also analyzed. Thanks to the collaboration with E80 Group S.p.A., the project has been developed using ROS (Robot Operating System) on a Clearpath Dingo-O, an indoor mobile robot. To address the challenges posed by unstructured and dynamic environments, ROS follows a combined approach of using a global planner and a local planner. The global planner generates a high-level path, considering the overall environment, while the local planner handles real-time adjustments to avoid moving obstacles and optimize the trajectory. This thesis describes the role of the global planner in a ROS-framework. Performance benchmarking of traditional algorithms like Dijkstra and A*, as well as other techniques, is fundamental in order to understand the limits of these methods. In the end, the Hybrid A* algorithm is introduced as a promising approach for addressing the issues of unstructured environments for autonomous navigation of a non-holonomic robot. The core concepts and implementation details of the algorithm are discussed, emphasizing its ability to efficiently explore continuous state spaces and generate drivable paths.The effectiveness of the proposed path planning algorithms is evaluated through extensive simulations and real-world experiments using the mobile platform. Performance metrics such as path length, execution time, and collision avoidance are analyzed to assess the efficiency and reliability of the algorithms.Path planning is a crucial aspect of autonomous robot navigation, enabling robots to efficiently and safely navigate through complex environments. This thesis focuses on autonomous navigation for robots in dynamic and uncertain environments. In particular, the project aims to analyze the localization and path planning problems. A fundamental review of the existing literature on path planning algorithms has been carried on. Various factors affecting path planning, such as sensor data fusion, map representation, and motion constraints, are also analyzed. Thanks to the collaboration with E80 Group S.p.A., the project has been developed using ROS (Robot Operating System) on a Clearpath Dingo-O, an indoor mobile robot. To address the challenges posed by unstructured and dynamic environments, ROS follows a combined approach of using a global planner and a local planner. The global planner generates a high-level path, considering the overall environment, while the local planner handles real-time adjustments to avoid moving obstacles and optimize the trajectory. This thesis describes the role of the global planner in a ROS-framework. Performance benchmarking of traditional algorithms like Dijkstra and A*, as well as other techniques, is fundamental in order to understand the limits of these methods. In the end, the Hybrid A* algorithm is introduced as a promising approach for addressing the issues of unstructured environments for autonomous navigation of a non-holonomic robot. The core concepts and implementation details of the algorithm are discussed, emphasizing its ability to efficiently explore continuous state spaces and generate drivable paths.The effectiveness of the proposed path planning algorithms is evaluated through extensive simulations and real-world experiments using the mobile platform. Performance metrics such as path length, execution time, and collision avoidance are analyzed to assess the efficiency and reliability of the algorithms