This paper addresses a new control strategy to reduce the damage to a humanoid robot during a fall. Instead of following the traditional approach of finding a favorable configuration with which to fall to the ground, this method attempts to stop the robot from falling all the way to the ground. This prevents the full transfer of the robot's potential energy to kinetic energy, and consequently results in a milder impact. The controlled motion of the falling robot involves a sequence of three deliberate contacts to the ground with the swing foot and two hands, in that order. In the final configuration the robot's center of mass (CoM) remains relatively high from the floor and the robot has a relatively stable three-point contact with the ground; hence the name tripod fall. The optimal location of the three contacts are learned through reinforcement learning algorithm. The controller is simulated on a full size humanoid, and experimentally tested on the NAO humanoid robot. In this work we apply our fall controller only to a forward fall.

This paper presents a decentralized algorithm for the coordinated assembly of 3-D objects that consist of multiple types of parts, using a networked team of robots. We describe the algorithm and analyze its convergence and adaptation properties. We partition construction in two tasks: tool delivery and assembly. Each task is performed by a networked team of specialized robots. We analyze the performance of the algorithms using the balls into bins problem and show their adaptation to failure of robots, dynamic constraints, multiple types of elements, and reconfiguration. We instantiate the algorithm to building truss-like objects using rods and connectors. We implement the algorithm in simulation and show results to construct 2-D and 3-D parts. Finally, we describe hardware implementation of the algorithms, where mobile manipulators assemble smart parts with IR beacons.

Although the fall of a humanoid robot is rare in controlled environments, it cannot be avoided in the real world where the robot may physically interact with the environment. Our earlier work [1], [2] introduced the strategy of direction-changing fall, in which the robot attempts to reduce the chance of human injury by changing its default fall direction in real-time and falling in a safer direction. The current paper reports further theoretical developments culminating in a successful hardware implementation of this fall strategy conducted on the Aldebaran NAO robot[3]. This includes new algorithms for humanoid kinematics and Jacobians involving coupled joints and a complete estimation of the body frame attitude using an additional inertial measurement unit. Simulations and experiments are smoothly handled by our platform independent humanoid control software package called Locomote. We report experiment scenarios where we demonstrate the effectiveness of the proposed strategies in changing humanoid fall direction.

This paper presents decentralized algorithms for coverage with mobile robots on a graph. Coverage is an important capability of multi-robot systems engaged in a number of different applications, including placement for environmental modeling, deployment for maximal quality surveillance, and even coordinated construction. We use distributed vertex substitution for locational optimization, and the controllers minimize the corresponding cost functions. We prove that the proposed controller with two-hop communication guarantees convergence to the locally optimal configuration. We evaluate the algorithms in simulations and compare them to the coverage algorithms in a continuous domain.

This paper presents a momentum-based reactive stepping controller for humanoid robot push recovery. By properly regulating combinations of linear and angular momenta, the controller can selectively encourage the robot to recover its balance with or without taking a step. A reference stepping location is computed by modeling the humanoid as a passive rimless wheel with two spokes such that stepping on the location leads to a complete stop of the wheel at the vertically upright position. In contrast to most reference points for stepping based on pendulum models such as the capture point, our reference point exists on both level and non-level grounds. Moreover, in contrast with continuously evolving step locations, our step location is stationary. The computation of the location of the reference point also generates the duration of step which can be used for designing a stepping trajectory. Momentum-based stepping for push recovery is implemented in simulations of a full size humanoid on 3D non-level ground.

Our prior work [1] presented a decentralized algorithm for coordinating the construction of a truss structure out of multiple components. In this paper, we discuss adaptation in decentralized construction. We partition construction in two tasks, tool delivery and assembly. Each task is performed by a networked team of specialized robots. We analyze the performance of the algorithms using the balls into bins problem, and show their adaptation to failure of robots, dynamic constraints, multiple types of elements and reconfiguration. The algorithms can be used for general types of source elements.

Our prior work presented a decentralized algorithm for coordinating the construction of truss shaped objects out of multiple components (rods and connectors). In this paper, we consider how to transfer the theory to practice, implementing the algorithm to create a decentralized multi robot construction system. The system is composed of mobile manipulators and smarts parts with an embedded communication device. We discuss the delivery and assembly algorithms that comprise this system and the assumptions behind them. We present data from extensive hardware experiments with 4 robots coordinating an assembly task.

Although fall is a rare event in the life of a humanoid robot, we must be prepared for it because its consequences are serious. In this paper we present a fall strategy which rapidly modifies the robot's fall direction in order to avoid hitting a person or an object in the vicinity. Our approach is based on the key observation that during “toppling” the rotational motion of a robot necessarily occurs at the leading edge or the leading corner of its support base polygon. To modify the fall direction the robot needs to change the position and orientation of this edge or corner vis-a-vis the prohibited direction. We achieve it through intelligent stepping as soon as a fall is detected. We compute the optimal stepping location which results in the safest fall. Additional improvement to the fall controller is achieved through inertia shaping techniques aimed at controlling the centroidal inertia of the robot. We demonstrate our results through the simulation of an Asimo-like humanoid robot. To our knowledge, this is the first implementation of a controller that attempts to change the fall direction of a humanoid robot.

In this paper we describe an optimal reconfiguration planning algorithm that morphs a grounded truss structure of known geometry into a new geometry. The plan consists of a sequence of paths to move truss elements to their new locations that generate the new truss geometry. The trusses are grounded and remain connected at all time. Intuitively, the algorithm grows gradually the new truss structure from the old one. The truss elements are rigid bars joined with 18-way connectors. The paper also introduces the design of a truss-climbing robot that can execute the plan.

We examine the usefulness of passive compliance in a manipulator that learns contact motion. Based on the notice that humans outperforms robots with the contact motion, we follow two aspects of human manipulation: passive compliance and learning. As imitation of human’s arm and learning, we use a robot arm with passive compliant joints and it learns a policy for peg in hole by the proposed gradient descending method. We present that the passive compliance provides with quick and stable learning as well as a slow control sampling time.

We describe self-assembling robot arm systems composed of active modular robots and passive bars.We present a case study where the robotic module is the Shady3D robot and the passive component is a rigid bar with embedded IR LEDs. We propose algorithms that demonstrate the cooperative aggregation of modular robotic manipulators with greater capability and workspace out of these two types of elements. We present results from physical experiments in which two 3DOF Shady3D robots and one rigid bar coordinate to self-assemble into a 6DOF manipulator. We then demonstrate cooperative algorithms for forward and inverse kinematics, grasping, and mobility with this arm.

We describe algorithms to build self-assembling robot systems composed of active modular robots and passive bars. The distributed algorithms are based on locally optimal matching. We demonstrate how to build an active structure by the cooperative aggregation and disassembly of modular robotic manipulators. A target structure is modeled as a dynamic graph. We prove that the same optimality - quadratic competitive ratio - as for the static graph can be achieved for the algorithms. We demonstrate how this algorithm can be used to build truss-like structures.

This paper considers the problem of allocating tasks among robots that operate on a 3D truss. Each robot is commanded to navigate to a different location for work. When the information about the robots' initial and desired locations are centrally known, this problem reduced to a classical disjoint-path problem. In this paper we consider the distributed problem where each robot knows its own goals only and we wish to plan an optimal set of steps for each robot that minimizes energy while fulfilling the task requirements. The challenge is to cope with possible path collisions. We present and analyze a distributed algorithm. We describe a simulation of this algorithm and show data from a physical experiment.

Tangible interface is a newly defined concept, which provides effective and seamless interaction between a human as a subjective existence and a cyberspace as an objective existence. As one of the tangible interface, tactile interface is essential for many exploration and manipulation tasks in the tangible space. In this paper, we suggest a design of an integrated tactile sensor-display system that enables both of tactile sensing and display with kinesthetic force, pressure distribution, and vibration. A human-like tactile sensing system with PVDF strips and a display system with bimorph actuators have been developed and integrated in the tangible space. In the demonstration scenario of haptic navigation in the tangible space, the developed tactile sensing to display system is successfully experimented.

In this paper, design and control of a safe arm with passive compliant joints (PCJ) are presented. A new manipulator with passive compliance property has been developed at KIST. Strategy for the joint control method is implemented. Because the PCJ is a flexible joint, unwanted vibration is occurred during the joint motion. To reduce this vibration, we use the input-preshaping method which is motivated by the input shaping technique (IST) based on impulse response. The control method for a time-varying system like the safe arm is described in detail. The result of simulations and experiments proves that a fast motion of the safe arm without residual vibration can be achieved.

When we use a tactile sensor, sensing mechanism and restoration of texture from electric sensing signals are important issues. The objectives of this research are to design a new texture sensing system and to develop a new signal processing algorithm which can restore various texture. The new texture sensing system is designed to get texture with high resolution and wide velocity range, which uses a PVDF sensor and has fixing components for several types of objects in precise condition. Next, a new signal processing algorithm is developed to restore texture. In the previous researches, forward model is needed and then it is inverted by using some regularized inversion formula in frequency domain, where there exist problems such as amplification of noise due to ill-posedness, modeling uncertainty due to orientation and position of PVDF films and silicon rubber, and difficulty to add nonlinear terms into model in frequency domain. While, in this paper, we model directly the relation containing transient-state from measured signals to texture by using model structure of multi-input multi-output nonlinear autoregressive moving average and a time domain least squares estimation. The direct modeling can be done by the use of F/T sensor which is not used in the previous researches. Finally the several texture is experimentally reconstructed from sensing signals using the developed signal processing algorithm.