Presents a globally convergent range-sensor based navigation algorithm in three-dimensions, called 3D Bug. The 3D Bug algorithm navigates a point robot in a three-dimensional unknown environment using position and range sensors. The algorithm strives to process the sensory data in the most reactive way possible, without sacrificing the global convergence guarantee. Moreover, unlike previous reactive-like algorithms, 3D Bug uses three-dimensional range data and plans three-dimensional motion throughout the navigation process. The algorithm alternates between two modes of motion. During motion towards the target, which is the first motion mode of the algorithm, the robot follows the locally shortest path in a purely reactive fashion. During traversal of an obstacle surface, which is the second mode of motion, the robot incrementally constructs a reduced data structure of an obstacle, while performing local shortcuts based on range data. We resent preliminary simulation results of the algorithm, which show that 3D Bug generates paths that resemble the globally shortest path in simple scenarios. Moreover, the algorithm generates reasonably short paths even in concave, room-like environments.

We present a general scheme for learning sensorimotor tasks, which allows rapid online learning and generalization of the learned knowledge to unfamiliar objects. The scheme consists of two modules, the first generating candidate actions and the second estimating their quality. Both modules work in an alternating fashion until an action which is expected to provide satisfactory performance is generated, at which point the system executes the action. We developed a method for off-line selection of heuristic strategies and quality predicting features, based on statistical analysis. The usefulness of the scheme was demonstrated in the context of learning visually guided grasping. We consider a system that coordinates a parallel-jaw gripper and a fixed camera. The system learns to estimate grasp quality by learning a function from relevant visual features to the quality. An experimental setup using an AdeptOne manipulator was developed to test the scheme.

We present DistBug, a new navigation algorithm for mobile robots which exploits range data. The algorithm belongs to the Bug family, which combines local planning with global information that guarantees convergence. Most Bug-type algorithms use contact sensors and consist of two reactive modes of motion: moving toward the target between obstacles and following obstacle boundaries, DistBug uses range data in a new "leaving condition" which allows the robot to abandon obstacle boundaries as soon as global convergence is guaranteed, based on the free range in the direction of the target. The leaving condition is tested directly on the sensor readings, thus making the algorithm simple to implement. To further improve performance, local information is utilized for choosing the boundary following direction, and a search manager is introduced for bounding the search area. The simulation results indicate a significant advantage of DistBug relative to the classical Bug2 algorithm. The algorithm was implemented and tested on a real robot, demonstrating the usefulness and applicability of our approach.

A scheme for learning to grasp objects using visual information is presented. The learning problem is divided into two separate subproblems: choosing grasping points and predicting the quality of a given grasp. For each grasp we store location parameters that code the locations of the grasping points, quality parameters that are relevant features for the assessment of grasp quality, and the associated grade. The location parameters, using a special coding which is not object specific, are used to locate grasping points on new target objects. A function from the quality parameters to the grade is learned from examples. Grasp quality for novel situations can be generalized and estimated using the learned function. An experimental setup using an AdeptOne manipulator was developed to test this scheme. The system had demonstrated an ability to grasp a relatively wide variety of objects, and its performance had significantly improved with practice following a small number of trials. The knowledge learned for a set of objects was successfully generalized to new objects.

We present TangentBug, a new range-sensor based navigation algorithm for two degrees-of-freedom mobile robots. The algorithm combines local reactive planning with globally convergent behaviour. For the local planning, TangentBug uses the range data to compute a locally shortest path based on a novel structure, termed the local tangent graph (LTG). The robot uses the LTG for choosing the locally optimal direction while moving towards the target. The robot also uses the LTG in its other motion mode, where it follows an obstacle boundary. In this mode the robot uses the LTG for making local short-cuts and testing a leaving condition which allows the robot to resume its motion towards the target. We analyze the convergence and performance properties of TangentBug. We also present simulation results, showing that TangentBug consistently performs better than the classical VisBug algorithm. Moreover, TangentBug produces paths that in simple environments approach the globally optimal path as the sensor's maximal detection range increases.

A sensory based algorithm DistBug, that is guaranteed to reach the target in an unknown environment or report that the target is unreachable, is presented. The algorithm is reactive in the sense that it relies on range data to make local decisions, and does not create a world model. The algorithm consists of two behaviors (modes of motion): straight motion between obstacles and obstacle boundary following. Simulation results as well as experiments with a real robot are presented. The condition for leaving obstacle boundary is based on the free range in the direction to the target. This condition allows the robot to leave the obstacle as soon as the local conditions guarantee global convergence. Range data is utilized for choosing the turning direction when the robot approaches an obstacle. A criterion for reversing the boundary following direction when it seems to be the wrong direction is also introduced. As a direct result of these local decisions, a significant improvement in the performance was achieved.