The teleoperation system has attracted a lot of attention because its advantages in dangerous or unknown environments. It is very difficult to develop an operating that can complete complex tasks completely autonomous. This paper proposes robot arm control strategy based on gestures and visual perception. combines the humans robots obtain convenient flexible interaction model. hand’s data were ...

The idea of probabilistic metric space was introduced by Menger and he showed that probabilistic metric spaces are generalizations of metric spaces. Thus, in this paper, we prove some of the important features and theorems and conclusions that are found in metric spaces. At the beginning of this paper, the distance distribution functions are proposed. These functions are essential in defining p...

Robot manipulators typically rely on complete knowledge of object geometry in order to plan motions and compute grasps. But when an object is not fully in view it can be difficult to form an accurate estimate of the object’s shape and pose, particularly when the object deforms. In this paper we describe a generative model of object geometry based on Mardia and Dryden’s “Probabilistic Procrustea...

How should we aggregate the ex ante preferences of Bayesian agents with heterogeneous beliefs? Suppose state world is described by a random process that unfolds over time. Different have different beliefs about probabilistic laws governing this process. As new information revealed time process, update their and via Bayes rule. Consider Pareto principle applies only to which remain stable in lon...

In this paper we investigate the application of motion planning techniques to the untangling of mathematical knots. Knot untangling can be viewed as a high-dimensional planning problem in reparametrizable configuration spaces. In the past, simulated annealing and other energy minimization methods have been used to find knot untangling paths. We have developed a probabilistic planner that is cap...

This paper formulates a new Minimum Constraint Removal (MCR) motion planning problem in which the objective is to remove the fewest geometric constraints necessary to connect a start and goal state with a free path. I present a probabilistic roadmap motion planner for MCR in continuous configuration spaces. The planner operates by constructing increasingly refined roadmaps, and efficiently solv...

Probabilistic planning is an inherently multi-objective problem where plans must trade-off probability of goal satisfaction with expected plan cost. To date, probabilistic plan synthesis algorithms have focussed on single objective formulations that bound one of the objectives by making some unnatural assumptions. We show that a multi-objective formulation is not only needed, but also enables u...

We consider the problem of planning optimally in potentially concurrent probabilistic domains: actions have probabilistic effects and may execute in parallel under certain conditions; we seek a contingency plan that maximises the probability of reaching the goal. The Graphplan framework has proven to be highly successful at solving classical planning problems, but has not previously been applie...

Probabilistic planning captures the uncertainty of plan execution by probabilistically modeling the effects of actions in the environment, and therefore the probability of reaching different states from a given state and action. In order to compute a solution for a probabilistic planning problem, planners need to manage the uncertainty associated with the different paths from the initial state ...

For most real-world problems the agent operates in only partially-known environments. Probabilistic planners can reason over the missing information and produce plans that take into account the uncertainty about the environment. Unfortunately though, they can rarely scale up to the problems that are of interest in real-world. In this paper, however, we show that for a certain subset of problems...