Being able to both walk and take flight is typical in nature, and now researchers are creating drones with similar capabilities. Scientists have developed a prototype drone that can both fly and drive - a breakthrough that could pave the way for flying cars in the future. The development could lead to machines that can fly into disaster zones and squeeze through tight spaces to transport objects or rescue people. The team developed various'path-planning' algorithms aimed at ensuring that the drones don't collide. To make them capable of driving, the team put two small motors with wheels on the bottom of each drone.

Although it's not a'one size fits all' solution, artificial intelligence can be used to successfully hunt cyberthreats. Giovanni Vigna, CTO and co-founder of Lastline, identifies several of the key areas to address when thinking proactively about AI as a tool in detecting cyberthreats. Artificial intelligence (AI) will not automatically detect and resolve every potential malware or cyberthreat incident, but when it combines both bad and good behavior modeling it becomes a successful and powerful weapon against even the most advanced malware. By their very nature, malware detection tools must constantly evolve to stay up to date with ever-changing crimeware. One of the biggest evolutions in malware detection is the migration from trapping to hunting.

Robots acting in human-scale environments must plan under uncertainty in large state-action spaces and face constantly changing reward functions as requirements and goals change. Planning under uncertainty in large state-action spaces requires hierarchical abstraction for efficient computation. We introduce a new hierarchical planning framework called Abstract Markov Decision Processes (AMDPs) that can plan in a fraction of the time needed for complex decision making in ordinary MDPs. AMDPs provide abstract states, actions, and transition dynamics in multiple layers above a base-level "flat" MDP . AMDPs decompose problems into a series of subtasks with both local reward and local transition functions used to create policies for subtasks. The resulting hierarchical planning method is independently optimal at each level of abstraction, and is recursively optimal when the local reward and transition functions are correct. We present empirical results showing significantly improved planning speed, while maintaining solution quality, in the Taxi domain and in a mobile-manipulation robotics problem. Furthermore, our approach allows specification of a decision-making model for a mobile-manipulation problem on a Turtlebot, spanning from low-level control actions operating on continuous variables all the way up through high-level object manipulation tasks.

In many robot motion planning problems such as manipulation planning for a personal robot in a kitchen or an industrial manipulator in a warehouse, all motion planning queries are in an environment that is largely static. Consequently, one should be able to improve the performance of a planning algorithm by training on this static environment ahead of operation time. In this work, we propose a method to improve the performance of heuristic search-based motion planners in such environments. The first, learning, phase of our proposed method analyzes search performance on multiple planning episodes to infer local minima zones, that is, regions where the existing heuristic(s) are weakly correlated with the true cost-to-go. Then, in the planning phase of the method, the learnt local minima are used to modify the original search graph in a way that improves search performance. We prove that our method preserves guarantees on completeness and bounded suboptimality with respect to the original search graph. Experimentally, we observe significant improvements in success rate and planning time for challenging 11 degree-of-freedom mobile manipulation problems.

Symbolic planning methods have proved to be challenging in robotics due to partial observability and noise as well as unavoidable exceptions to rules that symbol semantics depend on. Often the symbols that a robot considers to support for planning are brittle, making them unsuited for even relatively short term use. Maturing probabilistic methods in robotics, however, are providing a sound basis for symbol grounding that supports using probabilistic distributions over symbolic entities as the basis for planning. In this paper, we describe a belief-space planner that stabilizes the semantics of feedback from the environment by actively interacting with a scene. When distributions over higher-level abstractions stabilize, powerful symbolic planning techniques can provide reliable guidance for problem solving. We present such an approach in a hybrid planning scheme that actively controls uncertainty and yields robust state estimation with bounds on uncertainty that can make effective use of powerful symbolic planning techniques. We illustrate the idea in a hybrid planner for autonomous construction tasks with a real robot system.

Robot navigation through non-uniform environments requires reliable motion plan generation. The choice of planning model fidelity can significantly impact performance. Prior research has shown that reducing model fidelity saves planning time, but sacrifices execution reliability. While current adaptive hierarchical motion planning techniques are promising, we present a framework that leverages a richer set of robot motion models at plan-time. The framework chooses when to switch models and what model is most applicable within a single trajectory. For instance, more complex environment locales require higher fidelity models, while lower fidelity models are sufficient for simpler parts of the planning space, thus saving plan time. Our algorithm continuously aims to pick the model that best handles the current local environment. This effectively generates a single, mixed-fidelity plan. We present results for a simulated mobile robot with attached trailer in a hospital domain. We compare using a single motion planning model to switching with our framework of multiple models. Our results demonstrate that multi-fidelity model switching increases plan-time efficiency without sacrificing execution reliability.

Collaboration between humans and robots to accomplish different kinds of tasks has been recently studied as a planning problem and several techniques have been developed to define and generate shared plans where humans and robots collaborate to achieve a common goal. However, current methods require the knowledge of the human about the plan under execution and an agreement between users and robots about their roles before the execution of the plan. In this paper, we propose an extension to the Hierarchical Agent-based Task Planner (HA TP) that enables humans and robots to negotiate some aspects of the collaboration online during the execution of the plan. The proposed method is based on the automatic generation of a conditional plan in which missing information is acquired at execution time by means of sensing actions. The proposed method has been fully implemented and tested on a real robot performing collaborative tasks in an office-like environment.

The deployment of robots in public environments is gaining more and more attention and interest both for the research opportunities and for the possibility of developing commercial applications over it. In these scenarios, proper definitions and implementations of human-robot interactions are crucial and the specific characteristics of the environment (in particular, the presence of untrained users) makes the task of defining and implementing effective interactions particularly challenging. In this paper, we describe a method and a fully implemented robotic system using conditional planning for generating and executing short-term interactions by a robot deployed in a public environment. To this end, the proposed method integrates and extends two components already successfully used for planning in robotics: ROSPlan and Petri Net Plans. The contributions of this paper are the problem definition of generating short-term interactions as a conditional planning problem and the description of a solution fully implemented on a real robot. The proposed method is based on the integration between a contingent planner in ROSPlan and the Petri Net Plans execution framework, and it has been tested in different scenarios where the robot interacted with hundreds of untrained users.

We consider the motion-planning problem of planning a collision-free path of a robot in the presence of risk zones. The robot is allowed to travel in these zones but is penalized in a super-linear fashion for consecutive accumulative time spent there. We suggest a natural cost function that balances path length and risk-exposure time. Specifically, we consider the discrete setting where we are given a graph, or a roadmap, and we wish to compute the minimal-cost path under this cost function. Interestingly, paths defined using our cost function do not have an optimal substructure. Namely, subpaths of an optimal path are not necessarily optimal. Thus, the Bellman condition is not satisfied and standard graph-search algorithms such as Dijkstra cannot be used. We present a path-finding algorithm, which can be seen as a natural generalization of Dijkstra’s algorithm. Our algorithm runs in O ((n B · n) log(n B · n) + n B · m) time, where n and m are the number of vertices and edges of the graph, respectively, and n B is the number of intersections between edges and the boundary of the risk zone. We present simulations on robotic platforms demonstrating both the natural paths produced by our cost function and the computational efficiency of our algorithm.

Planning in an online robotics context has the specific requirement of a short planning duration. A property of typical contemporary scenarios is that (mobile) robots perform similar or even repeating tasks during operation. With these robot domains in mind, we propose database-driven macro planning for STRIPS ( DBMP/S) that learns macros - action sequences that frequently appear in plans - from experience for PDDL-based planners. Planning duration is improved over time by off-line processing of seed plans using a scalable database. The approach is indifferent about the specific planner by representing the resulting macros again as actions with preconditions and effects determined based on the actions contained in the macro. For some domains we have used separate planners for learning and execution exploiting their respective strengths. Initial results based on some IPC domains and a logistic robot scenario show significantly improved (over non-macro planners) or slightly better and comparable (to existing macro planners) performance.