In-hand manipulation has attracted attention because of its potential for performing dexterous manipulation tasks. Few successful examples using real robotic fingers have been reported because model-based approaches have been assumed. A gradient descent-based iterative learning control is one of the typical methods for improving the control performance without the need for a precise model. However, the learning performances deteriorate greatly owing to the stochastic uncertainties, and the learning rates have to be determined manually. We propose a novel iterative learning scheme with adaptive learning rate methods for dexterous in-hand manipulation. The proposed scheme not only eliminates the need for a precise model and manual tuning of a learning rate but also is robust to stochastic uncertainties and insensitive to hyperparameters. The validity of the proposed iterative learning scheme is demonstrated through several experiments.

In this study, we propose a novel iterative learning scheme, which generates time-series data comprising the impedance value for human-robot cooperative work, where the human operator moves the end-effector from an initial position to a goal position. The proposed learning scheme iteratively updates time-series data comprising the variable impedance value to minimize a task-oriented cost function. Two types of noise reduction technique are used in the proposed learning scheme, which reduce the noise in the time direction and in the trial direction. The validity of the proposed method was verified based on experiments.

This paper proposes an iterative learning control scheme for whole-arm object manipulation by using a robotic hand system composed of a velocity-controlled finger and a torque-controlled finger. The novel iterative learning control scheme consists of the PD-type iterative learning control scheme for the velocity-controlled finger to follow the desired trajectory and the optimization-based iterative learning control scheme for the torque-controlled finger to maintain equilibrium. The switching between the two controllers is made to achieve compliant manipulation. The validity of the proposed scheme is demonstrated through experiments.

The present paper discusses a planning and control strategy for robotic whole-arm manipulation of a slippery polygonal object by taking into account the estimated bounds of the frictional coefficient. In this study, randomized planning methods are first proposed in order to generate contact state transitions. Second, a novel control strategy that can switch manipulation between quasi-static, dynamic, and caging manipulation modes is proposed. Solving the forward dynamics problem, which is formulated as a complementarity problem, generates the desired trajectory and thus ensures that undesirable contact modes do not occur within the estimated bounds of the frictional coefficient. The validity of the proposed methods is then demonstrated through simulations and experiments.

This paper reports on our initial efforts to achieve robotic nonprehensile catching. First, we show the importance of nonprehensile catching in a robotic catching task. Robotic nonprehensile catching can be achieved by caging or partial caging with gravity. Since an object is restricted within a bounded region via nonprehensile catching, accurate finger positioning is not required because the robotic hand should be moved to an appropriate region where caging is achieved. Second, control strategies for nonprehensile catching are proposed. The robotic arm motion is generated on-line by using visual sensory feedback in response to the object motion. Noting that the object passes from the outside to the inside of the workspace of the robotic arm, the motion phases of which are divided into tracking and catching based on the specified region in a 3-D space. Finally, the validity of the resulting methods is demonstrated by experiments.

The present paper introduces a planning and control strategy for whole-arm manipulation of a slippery polygonal object. Randomized planning methods are first proposed in order to generate contact state transitions, which help not only to reduce the amount of calculation required, but also to handle a hybrid system composed of a continuous system and a discrete system, which has a large search space and complicated constraints. Second, a novel control strategy, which can switch manipulation modes among quasi-static, dynamic, and caging manipulation depending on the situation, is proposed. This strategy not only can cope with changes in the mechanics of the system caused by contact state transitions, but also can increase the manipulation feasibility and reliability. The validity of the proposed methods is verified through simulations and experiments.

The present paper proposes the learning control method for the throwing manipulation which can control not only the position but also the orientation of the polygonal object more accurately and robustly by low-degree-of-freedom robotic arm. We show experimentally the validity of the proposed control method with the one-degree-freedom robotic arm. We also demonstrate the usefulness of the throwing manipulation by applying it to sorting task and assembly task on experiments.

This paper proposes a throwing manipulation strategy for a robot with one revolute joint. The throwing manipulation enables the robot not only to manipulate the object to outside of the movable range of the robot, but also to control the position of the object arbitrarily in the vertical plane even though the robot has only one degree of freedom. In the throwing manipulation, the robot motion is dynamic and quick, and the contact state between the robot and the object changes. These make it difficult to obtain the exact model and solve its inverse problem. In addition, since the throwing manipulation requires more powerful actuators than the static manipulation, we should set the control input by taking consideration of the performance limits of the actuators. The present paper proposes the control strategy based on the iteration optimization learning to overcome the above problems and verifies its effectiveness experimentally.

The present paper proposes the motion planning based on “the dynamics shaping” for a robotic arm to hit the target robustly toward the desired direction, of which the concept is to shape the robot dynamics appropriately in order to accomplish the desired motion. According to the linear system theory, the positional error of the end-point converges onto near the singular vector corresponding to its maximum singular value of the output controllability matrix of the robotic arm. Therefore, if we can control the direction of the singular vector by applying the dynamics shaping, we will be able to control the direction of the positional error of the end-effector caused by the disturbance. We propose a novel motion planning based on the dynamics shaping and verify numerically and experimentally that the robotic arm can robustly hit the target toward the desired direction with a simple open-loop control system even though the disturbance is applied.

The present paper introduces an evaluation of the manipulation performance of a robotic arm with respect to control accuracy and mechanical efficiency, taking into consideration the effects of gravity and the dynamic process between inputs and outputs in the robotic system. A measure based on the output controllability ellipsoid is proposed, which shows the relationship between the end-effector motion and the joint driving force. Computer simulations demonstrate the effects of gravity on the manipulation performance and the benefits of the proposed measure for the trajectory planning.

This paper presents randomized manipulation planning for re-orientation of an object in order to achieve dexterous manipulation by a robotic multi-fingered hand. A key issue is to develop an efficient algorithm to search for feasible trajectories of a manipulation system between two arbitrary grasps. The reasons for this are that aspects inherent in manipulation planning have high dimensional search spaces and complicated contact constraints. The present manipulation planning technique is based on the generation of subgoals for object orientations and the connection between subgoals. The generation of a random orientation, which should take into account the physical features of a rigid body orientation in a three-dimensional space, is also discussed. Furthermore, simulation examples for manipulation planning are presented and discussed.

This paper presents a randomized manipulation planner for a multi-fingered hand by switching contact modes. Manipulation planning for such a system should consider changing kinematics and dynamics of the system according to the contact modes. We derive the conditions that would be satisfied in manipulation planning, based on the kinematical properties of a manipulation system. The conditions allow the planner to assign more contact modes than those corresponding to the relative motion of contact points restricted by constraint forces. Consequently, this enables dextrous manipulation. In addition, the conditions give the restrictions of feasible contact modes and the number of contact points. Inspired by randomized motion planning techniques, we propose a new algorithm for manipulation planning in order to explore object configuration space, SE(3), rapidly and uniformly. The basis for this approach is for the construction of exploring random trees. Simulation examples for 3-D manipulation by switching contact modes are presented to verify the planner's effectiveness.

We propose a novel complementarity model for a general three-dimensional manipulation system with rolling and sliding contacts to solve the forward dynamics problem. The key idea is to derive complementary constraints for a rolling contact by decomposing the tangential properties. We also discuss the existence and uniqueness of a solution to the model, and algorithms for solving the problem. We show examples of computer simulation for dynamics of multifingered hand manipulation and discuss how the transition of contact modes varies for applied joint driving torques.

This paper presents a randomized manipulation planner for a multi-fingered hand by switching contact modes. Manipulation planning for such a system should consider changing kinematics and dynamics of the system according to the contact modes. We derive the conditions that would be satisfied in manipulation planning, based on the properties of a manipulation system. The conditions give the restrictions of feasible contact modes and the number of contact points. Inspired by randomized motion planning techniques, we propose a new algorithm for manipulation planning in order to explore object configuration space rapidly and uniformly. Locally optimized contact modes are determined using a cost function. The basis for this approach is for the construction of exploring random trees. Simulation examples for 3-D manipulation by switching contact modes are presented to verify the planner's effectiveness.

We present motion planning for dexterous manipulation by a whole arm grasp system based on switching contact modes. Motion planning for such a system should consider changing dynamics and kinematics according to the contact modes. We systematize the properties of the manipulation system by taking account of contact dynamics and kinematics, and derive the conditions to be satisfied in motion planning. The conditions give the restrictions of the feasible contact mode and the number of contact points. We propose an algorithm of global motion planning based on a hybrid control perspective. We aim at finding a feasible joint torque trajectory and a sequence of contact modes to move the object to an object final state for given switching times and object state trajectory. The proposed planning algorithm mainly consists of planning the object trajectory by a randomized approach using an object nominal trajectory, and constructing subgoals which satisfy the proposed conditions by solving an inverse problem. We show the validity of the planner by simulating manipulation by whole arm grasp system.

In this paper we consider the dynamics and control of whole arm grasping systems. We develop a control scheme that employs a minimal set of inputs to control the trajectory of the system while using the surplus inputs to control the interaction forces in order to maintain the unilateral constraints at both rolling and sliding contacts. Since the number of surplus inputs is less than the number of output force variables, we propose a controller that controls the critical contact force components. We emphasize the dynamic models and algorithms for computing contact forces, which are crucial to the development of the control algorithms. Finally, we show how compliant contact models and an integrated simulation approach are used to overcome the difficulties with uniqueness and existence of solutions. A planar whole arm manipulation system is used as an example to illustrate the basic ideas.

This paper presents mathematical conditions and algorithm for a whole arm manipulation planning with switching contact modes. The motion planning for such a system should consider changing the dynamics and kinematics according to contact modes. We show the conditions about kinematic and dynamics aspects to be satisfied for the motion planning. The conditions give the restriction of feasible contact modes and the number of contact points. The planning problem is simplified using a randomized technique. We illustrate the approach by simulating the whole arm manipulation.

We propose a novel approach to dynamic simulation of grasps that overcomes difficulties due to inconsistencies in the forward dynamics problem. The key idea in the paper is a minimalist model of the contact compliance and the use of this model in situations when the performance of the rigid body model is not satisfactory. Our general framework allows for online diagnostics that enable the automatic switching between models to maximize efficiency while avoiding ambiguous situations. We illustrate the basic ideas by simulating the dynamics of several whole arm grasps.

The development of an intelligent fixture for automatic assembly by the robots that can manipulate and fix a heavy object in three dimensions and make the relocation of the jigs unnecessary is addressed. A new mechanism, which is constructed by the rotational base and the one-DOF fingers, is proposed. It makes the manipulation (mainly rotation) of the heavy target object possible because of its simplicity, but the simplicity causes some problems. The kinematics and the dynamics of manipulation by this mechanism is derived, and it is shown how to plan the manipulation. Simulation and experimental results are reported.

The authors propose a type of jig which can automatically manipulate an object into a desired position and orientation. The jig consists of multifingered hands and jig base. The characteristics of the jig are that multifingered hands with one degree of freedom are arranged. Its advantages are that the mechanism is simple and that heavy objects can be manipulated. The disadvantage is that sliding occurs between the object and fingertip. The manipulations considered are in large motions of an object with sliding contact by simple mechanism multifingered hands. The kinematics and dynamics of manipulation involving sliding in the plane are discussed. The manipulation planning concerning kinematics and dynamics is presented. The results of the simulation are illustrated.>