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We introduce an information theoretic model predictive control (MPC) algorithm capable of handling complex cost criteria and general nonlinear dynamics. The generality of the approach makes it possible to use multi-layer neural networks as dynamics models, which we incorporate into our MPC algorithm in order to solve model-based reinforcement learning tasks. We test the algorithm in simulation on...
In this paper we are continuing in our research to show mutual intersection of two different areas of research: complex network and evolutionary computation. This research parer is focused on possibility to convert run of evolution algorithm to a complex network inspired by ants. Such network can then be analyzed to get useful information about algorithm dynamics. In this paper we focused on one global...
An important task for mobile robots is autonomous navigation, where the robot travels between a starting point and a target point without the need for human intervention. This task can be described as a planning path problem, whose purpose is to locate sequential segments of state transitions (Cells) from an initial to a final goal. This paper investigates a family of trajectory generation algorithms...
In our previous work [1] we introduced the Anticipative Kinodynamic Planning (AKP): a robot navigation algorithm in dynamic urban environments that seeks to minimize its disruption to nearby pedestrians. In the present paper, we maintain all the advantages of the AKP, and we overcome the previous limitations by presenting novel contributions to our approach. Firstly, we present a multi-objective cost...
This paper presents an extension of our previous work on hybrid metric/topological maps to enable uncertainty reduction planning through the map, taking into account both map uncertainty and distance. An enhancement of the edge structure which enables the simulation of bidirectional edge propagation through an extended Kalman filter is proposed in our heuristic search planning algorithm to plan for...
Autonomous learning of object manipulation skills can enable robots to acquire rich behavioral repertoires that scale to the variety of objects found in the real world. However, current motion skill learning methods typically restrict the behavior to a compact, low-dimensional representation, limiting its expressiveness and generality. In this paper, we extend a recently developed policy search method...
This paper describes a path planning algorithm for fixed-wing UAV flights that follow the local terrain. The proposed algorithm utilizes a spline-RRT∗ planner in which the tree structure is extended using a spline method to generate smooth paths without any post-processing. In addition, a cost function ensures paths are sufficiently far from several hazardous positions and close to the surface of...
This paper describes an algorithm for robotic motion planning that is capable of optimising several cost functions simultaneously to provide optimised, feasible and collision-free paths. The algorithm is based on the best-first graph search algorithm using a Pareto frontier to evaluate costs at each node. Additionally, we include a calculation of the distribution of robot trajectories when the path...
Learning motion control as a unified process of designing the reference trajectory and the controller is one of the most challenging problems in robotics. The complexity of the problem prevents most of the existing optimization algorithms from giving satisfactory results. While model-based algorithms like iterative linear-quadratic-Gaussian (iLQG) can be used to design a suitable controller for the...
This paper investigates motion-planning using formal language specifications for dynamical systems with differential constraints. In particular, we focus on process algebra as a language to specify complex task specifications motivated by autonomous electric vehicles operating in a mobility-on-demand scenario. We use ideas from sampling-based motion-planning algorithms to incrementally construct a...
In recent years, robotic systems have been playing an increasingly important role in physiotherapy. The aim of these platforms is to aid the recovery process from strokes or muscular damage by assisting patients to perform a number of controlled tasks, thus effectively complementing the role of the physiotherapist. In this paper, we present a novel learning from demonstration framework for cooperative...
We present Kinodynamic RRT*, an incremental sampling-based approach for asymptotically optimal motion planning for robots with linear dynamics. Our approach extends RRT*, which was introduced for holonomic robots [10], by using a fixed-final-state-free-final-time controller that optimally connects any pair of states, where the cost function is expressed as a trade-off between the duration of a trajectory...
Path planning in dynamic environments with moving obstacles is computationally complex since it requires modeling time as an additional dimension. While in other domains there are state dominance relationships that can significantly reduce the complexity of the search, in dynamic environments such relationships do not exist. This paper presents a novel state dominance relationship tailored specifically...
Indoor aerial robots are useful in many applications due to their size, agility and ability to hover. However, tweaking a state-feedback controller to fly stably takes either intensive human supervision, or extensive modeling and identification, hence has never been trivial. In this paper, we give a successful flight controller design that can learn from a single demonstration performed by human and...
Reinforcement learning (RL) has been applied to a wide range of motion control problems in robotics. In particular, policy gradient method (PGM) emerges as a powerful subset of RL that can learn effectively from one's experience. However, when the dynamics is stochastic and is short of samples for learning, the performance of PGM becomes inconsistent and heavily relies on the tweaking of the learning...
Consider slide parking, given a desired demonstration, how to repeat it accurately? Many robotics tasks, such as slide parking, can be formulated in trajectory following, but not many dynamics of which can be easily modeled to facilitate a solving by the optimal control. Although an emerging stream in robotics is to learn the dynamics and policy from demonstrations, multiple, if not numerous, demonstrations...
We present an efficient dynamic programming algorithm to solve the problem of optimal multi-location robot rendezvous. The rendezvous problem considered can be structured as a tree, with each node representing a meeting of robots, and the algorithm computes optimal meeting locations and connecting robot trajectories. The tree structure is exploited by using dynamic programming to compute solutions...
In this paper, we present an optimum approach to design a MIMO controller for a manipulator using discrete tabu search (TS) algorithm. In the first step, the TS algorithm is reviewed and then we employ the proposed method in order to assign efficiently the optimal PID controller parameters. The design goal is to minimize the integral absolute error and reduce transient response by minimizing overshoot,...
We present new techniques for establishing lower bounds in robot motion planning problems. Our scheme is based on path encoding and uses homotopy equivalence classes of paths to encode state. We first apply the method to the shortest path problem in 3 dimensions. The problem is to find the shortest path under an Lp metric (e.g. a euclidean metric) between two points amid polyhedral obstacles. Although...
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