This paper presents the software framework established to facilitate cloud-hosted robot simulation. The framework addresses the challenges associated with conducting a task-oriented and real-time robot competition, the Defense Advanced Research Projects Agency (DARPA) Virtual Robotics Challenge (VRC), designed to mimic reality. The core of the framework is the Gazebo simulator, a platform to simulate robots, objects, and environments, as well as the enhancements made for the VRC to maintain a high fidelity simulation using a high degree of freedom and multisensor robot. The other major component used is the CloudSim tool, designed to enhance the automation of robotics simulation using existing cloud technologies. The results from the VRC and a discussion are also detailed in this work.

The U.S. Defense Advanced Research Projects Agency's (DARPA) Neovision2 program aims to develop artificial vision systems based on the design principles employed by mammalian vision systems. Three such algorithms are briefly described in this paper. These neuromorphic-vision systems' performance in detecting objects in video was measured using a set of annotated clips. This paper describes the results of these evaluations including the data domains, metrics, methodologies, performance over a range of operating points and a comparison with computer vision based baseline algorithms.

This paper presents two frontal plane algorithms for 3D dynamic bipedal walking. One of which is based on the notion of symmetry and the other uses reinforcement learning algorithm to learn the lateral foot placement. The algorithms are combined with a sagittal plane algorithm and successfully applied to a simulated 3D bipedal robot to achieve level ground walking. The simulation results showed that the choice of the local control law for the stance-ankle roll joint could significantly affect the performance of the frontal plane algorithms.

We propose a general but simple bipedal walking control architecture that incorporates intuitive control and learning algorithms. The learning algorithm is mainly used to generate the key parameters for the swing leg. The intuitive control is used to maintain the height and body posture. Based on the proposed architecture, a control algorithm is constructed and applied to a planar biped and a 3D biped. By applying an appropriate local speed control mechanism, we demonstrate that the bipeds can successfully achieve walking of 100 seconds within a reasonable number of trials. No dynamic models or nominal joint trajectory data are required for the implementations.

We present a hydro-elastic actuator that has a linear spring intentionally placed in series between the hydraulic piston and actuator output. The spring strain is measured for an accurate estimate of force. This measurement alone is used in PI feedback to control the force in the actuator. The spring allows for high force fidelity, good force control, minimum impedance, and large dynamic range. A third order linear actuator model is divided into two fundamental cases: fixed load-high force (forward transfer function), and free load-zero force (impedance). These two equations completely describe the linear characteristics of the actuator. This model is presented with dimensional analysis to allow for generalization. A prototype actuator that demonstrates force control and low impedance is also presented. Dynamic analysis of the prototype actuator correlates well with the linear mathematical model.

Simple intuitive control strategies can be used to compel bipedal robots to walk over sloped terrain. We describe an algorithm for walking dynamically and steadily over sloped terrain with unknown slope gradients and transition locations. The algorithm is developed based on geometric considerations. The overall algorithm is very simple and does not require the biped to have an extensive sensory system for walking over moderate slopes. The ground is detected blindly using only foot contact switches. Using a few simple strategies, we have compelled a simulated 7-link planar biped to walk up and down slopes and over rolling terrain.

We present a stable adaptive control approach for a bipedal walking robot. This approach utilizes a self-organizing CMAC neural network mechanism which has a fast training rate, high approximation accuracy and significant reduction in space complexity. In order to apply this control approach to a bipedal walking robot, a Cartesian virtual dynamics space is introduced based on the virtual model control concept. The adaptive CMAC neural network control approach identifies the unmodelled dynamics of the bipedal robot and ensures asymptotic system stability in a Lyapunov sense. It can also better accommodate unexpected external disturbances, enhancing the control robustness of the bipedal robot. The CMAC neural network structure, its training algorithm, and bipedal locomotion control are described. The simulation results for a walking robot are presented.

Virtual model control (VMC) has previously been successfully applied to steady dynamic walking of a planar biped. This control methodology requires very low computation because it does not calculate the inverse dynamics of the biped. An adaptive control approach based on radial basis function neural networks (RBFNNs) has also been previously proposed to enhance VMC. However, such implementation is computationally intensive. We propose a simpler adaptive VMC that allows the biped to adapt to mass variations without using RBFNNs. We implement the resulting system and demonstrate the robustness of the implementation by simulating the biped walking over rolling terrain.

A biologically inspired locomotion control design approach is presented which is based on a mutually inhibited neural oscillator model. The entrainment between the dynamics of neural oscillators and the natural dynamics of the plant is very important for neural oscillator driven rhythmic control. A systematic design approach for rhythmic control is studied in the paper. First, the global system dynamics is divided into two separate parts, namely, the dynamics of neural oscillators and the natural dynamics of the controlled plant. Second, a compensator block is proposed to shape the natural dynamics of the plant so that the global dynamic entrainment and the desired plant motion can be achieved more easily. Furthermore, a design guideline for global dynamic entrainment is given. Finally, the design approach is applied to bipedal locomotion control of a simulated walking robot. The simulation results are also presented in the paper.

Bipedal robots are difficult to analyze mathematically. However, successful control strategies can be discovered using simple physical intuition and can be described in simple terms. Five things have to happen for a planar bipedal robot to walk. Height has to be stabilized. Pitch has to be stabilized. Speed has to be stabilized. The swing leg has to move so that the feet are in locations which allow for the stability of height, pitch, and speed. Finally, transitions from support leg to support leg must occur at appropriate times. If these five objectives are achieved, the robot will walk. A number of different intuitive control strategies can be used to achieve each of these five objectives. Further, each strategy can be implemented in a variety of ways. We present several strategies for each objective which we have implemented on a bipedal walking robot. Using these simple intuitive strategies, we have compelled a seven link planar bipedal robot, called Spring Flamingo, to walk. The robot walks both slowly and quickly, walks over moderate obstacles, starts, and stops.

The robustness of biped walking can be enhanced by the use of adaptive control and learning. The paper describes one such approach, radial basis function (RBF) neural network adaptive control (NNAC). The adaptive control mechanism is designed in a virtual space utilizing the virtual model control paradigm. The neural network is parameterized and trained in an unsupervised learning mode. There are two advantages to this approach. First, the NNAC can identify the unmodelled dynamics of the robot and ensure asymptotic system stability in a Lyapunov sense. Second, the controller can better accommodate unexpected external disturbances. The system's design is described and simulation results are presented.

The transformation from high level task specification to low level motion control is a fundamental issue in sensorimotor control in animals and robots. This paper describes a control scheme called virtual model control that addresses this issue. Virtual model control is a motion control language that uses simulations of imagined mechanical components to create forces, which are applied through real joint torques, thereby creating the illusion that the virtual components are connected to the robot. Due to the intuitive nature of this technique, designing a virtual model controller requires the same skills as designing the mechanism itself. A high level control system can be cascaded with the low level virtual model controller to modulate the parameters of the virtual mechanisms. Discrete commands from the high level controller would then result in fluid motion. Virtual model control has been applied to a physical bipedal walking robot. A simple algorithm utilizing a simple set of virtual components has successfully compelled the robot to walk continuously over level terrain.

Robots typically have an individual actuator at each joint which can result in a nonintuitive and difficult control problem. In this paper we present a control method in which the real joint actuators are used to mimic virtual actuators which can be more intuitive and hence make the control problem more straightforward. Our virtual actuator control method requires a solution to the force distribution problem when applied to parallel mechanisms. An extension of Gardner's partitioned actuator set control method (1991) is presented. This extended method allows for dealing with constrained degrees of freedom in which the torque cannot be specified but can be measured. A simulated hexapod robot was developed to test the proposed control method. The virtual actuators allowed textbook control solutions to be used in controlling this highly nonlinear, parallel mechanism. Using a simple linear control law, the robot walked while simultaneously balancing a pendulum and tracking an object.

Communication between independently-clocked digital subsystems typically involves a finite probability of synchronization failure whose minimization introduces delays and consequent performance costs. This paper explores a technique that eliminates both the inherent unreliability of such communication and the performance overhead it implies. Our approach maintains a known phase relationship, between clocks whose frequencies are related by a rational factor, and exploits the predictability of their relative phases to algorithmically time communications without run-time arbitration contests.

It is traditional to make the interface between an actuator and its load as stiff as possible. Despite this tradition, reducing interface stiffness offers a number of advantages, including greater shock tolerance, lower reflected inertia, more accurate and stable force control, less inadvertent damage to the environment, and the capacity for energy storage. As a trade-off, reducing interface stiffness also lowers zero motion force bandwidth. In this paper, the authors propose that for natural tasks, zero motion force bandwidth isn't everything, and incorporating series elasticity as a purposeful element within the actuator is a good idea. The authors use the term elasticity instead of compliance to indicate the presence of a passive mechanical spring in the actuator. After a discussion of the trade-offs inherent in series elastic actuators, the authors present a control system for their use under general force or impedance control. The authors conclude with test results from a revolute series-elastic actuator meant for the arms of the MIT humanoid robot Cog and for a small planetary rover.

It has historically been difficult to distribute a well-aligned hardware clock throughout the physical extent of a synchronous processor. Traditionally, this task has been accomplished by distributing the output of a central oscillator over a tree-like network, with repeaters at necessary intervals. While straightforward in concept, this method suffers from poor reliability, poor scalability and high skew. In this paper, we present an alternative approach-Distributed Synchronous Clocking-that maintains the simplicity of synchronous operation without suffering the drawbacks of centralized clocking. A network of independent oscillators takes the place of the centralized clock source, providing separate clock signals to the physically distant parts of a computing system. A distributed error correction algorithm effects global phase alignment by utilizing Local comparisons of neighboring oscillator phase. In contrast to centralized clock distribution, distributed clocking has the inherent potential for complete scalability and graceful degradation. However, because oscillator phase is a modular quantity, a naive implementation of distributed synchronous clocking can suffer from mode-lock-the trapping of local oscillator phase in undesirable stable equilibria where global phase is not aligned. We present a simple method for eliminating this problem in k-ary Cartesian meshes and give a proof of its correctness for two-dimensional networks. An electronic implementation is also presented and several engineering issues relating to error tolerance are discussed.>

Many standardized hardware communication interfaces offer runtime flexibility and configurability at the cost of efficiency.An alternate approach is the use of a highly-efficien~minimal communication element with as much communication decision-making as possible done at compile time.NuMesh is a packaging and interconnect technology supporting high-bandwidth systolic communications on a 3D nearest-neighbor lattice; our goal is to combine Lego-like modularity with supercomputer performance.To date, the primary focus of the project has been the class of applications whose static communication patterns can be precompiled into independent and carefully choreographed finite state machines running on each node.Several extensions of the NuMesh to more general communication paradigms have been implemented, and the issues involved are under active exploration.This paper presents an overview of our approach, as well as an introduction to our current-generation prototype.We also discuss our software environment and simulation technology, and enumerate some of the applications and programming models we have developed to make full use of the capabdities of the NuMesh.