We introduce Pressure Field Contact (PFC), an approximate model for predicting the contact surface, pressure distribution, and net contact wrench between nominally rigid objects. PFC combines and generalizes two ideas: a bed of springs (an `elastic foundation') and hydrostatic pressure. Continuous pressure fields are computed offline for the interior of each nominally rigid object. Unlike hydrostatics or elastic foundations, the pressure fields need not satisfy mechanical equilibrium conditions. When two objects nominally overlap, a contact surface is defined where the two pressure fields are equal. This static pressure is supplemented with a dissipative rate-dependent pressure and friction to determine tractions on the contact surface. The contact wrench between pairs of objects is an integral of traction contributions over this surface. PFC evaluates much faster than elasticity-theory models, while showing the essential trends of force, moment, and stiffness increase with contact load. It yields continuous wrenches even for non-convex objects and coarse meshes. The method shows promise as sufficiently fast, accurate, physical, and robust for robotics applications including motion and tactile sensor simulation, controller learning and synthesis, state estimation, and design-in-simulation.

Consider how a new robot is designed. Starting from a relative size (e.g., nanometer, centimeter, meter), the roboticist picks a morphological type (manipulator, quadruped, biped), and then uses intuition, experience, biological inspiration, or some combination of the three to select kinematic, dynamic, and geometric parameters. The designer then conducts preliminary checks to see whether the robot can satisfy its intended function: manipulation, locomotion, or both. The designer then iteratively adjusts the physical parameters and conducts checks using sample tasks, presumably until convergence is reached. This paper describes a means to automate part of this approach by combining interactive elements with powerful tools that use multi-rigid body simulation. We describe and demonstrate a virtual testing phase that first determines whether the physically situated robot would serve its intended purpose. If the robot is not capable of performing its target task, the virtual testing phase can determine which of the robot's morphological parameters should be modified in order to do so. The process keeps a human in the loop to help account for hard to quantify design aspects like appearance, quirks of the fabrication procedure (i.e., laser cutting, milling, 3D printing processes), or even expert knowledge. We intend for the described approach to be used as an interactive tool that gives a robot designer feedback on what morphological parameters are likely to limit the performance of a robot and how to modify the design to fix or offset such limitations. We demonstrate that, through simulated prototyping and testing methods, we can improve a robot design and iteratively locate morphological parameters that make efficient use of available hardware.

In this paper we evaluate the predictive performance of six commonly used rigid body impact models on real planar impacts captured with a motion tracking system. We propose a metric to evaluate the performance of impact models on a task (based on predicting post impact momentum) and use this metric to tune the six parametric models. We evaluate model performance in predicting impact outcomes against the defined metric and discuss the implications of uncertainty in geometric models and initial conditions. We show that the models can fairly effectively predict the outcomes of single impacts on our chosen task. We motivate further study into consensus and hybrid impact models by showing that a hypothetical hybrid model would significantly outperform the isolated models by providing a post-hoc model that demonstrates an upper bound on the combined predictive power of the models. We use perturbation analysis to compute the predictive range of the models and show that bifurcations can cause the model predictions to cluster into regions of the state space.

Most current approaches for simulating robots in 3D with rigid body dynamics and contact operate by taking fixed, first order integration steps. In this paper, we investigate the viability of various integration approaches (semi implicit and fully explicit first-order, fourth order Runge Kutta, and variable step first order) for maximizing computational efficiency (accuracy and stability vs. running time) on typical robotics manipulation and locomotion applications. After arguing that first-order accuracy to the dynamics models is sufficient for most manipulator and locomotion robotics applications, we focus our investigation on methods for efficient, stable dynamic simulations. We describe a novel algorithm that attempts to provide smooth convergence to the true dynamics solution, which allows us to estimate the error in kinetic energy around an integration step size (as a proxy for the simulation stability). We use this algorithm to evaluate multiple hypotheses using simulation-based experiments with two virtual robot models toward developing methods for faster dynamic simulations.

This technical correspondence is concerned with the problem of autonomous robot exploration in a 3-D world. It provides a better understanding of exploration problems with regard to their mathematical formulation, and present a means to evaluate the performance of future algorithms designed to solve it. In addition, an approach capable of being implemented on articulated robotic systems is proposed. The presented algorithm possesses completeness properties, and typically chooses navigation paths that increase the robot's knowledge of its environment while expending minimal energy. It is compared to other exploration approaches capable of being implemented on high-degree-of-freedom robots. The comparison, performed in simulation, shows the significance of reducing energy during the exploration process.

Inverse dynamics control is an extremely effective nonlinear control strategy if the inverse dynamics computation can be performed with sufficient speed.We describe our method for inverse dynamics control of legged robots that can deal with both sticking and slipping frictional contacts and mitigates the problems introduced by indeterminate rigid body contact. We improve this work, which previously used quadratically constrained quadratic programs (QCQPs), to use faster-to-solve quadratic programs via linear algebraic simplifications and a nullspace. We also show that Lemke's Algorithm is significantly faster than alternatives and permits multi-stage optimizations within control loops running as fast as 125Hz on commodity hardware.

The Linear Complementarity Problem (LCP) is a key problem in robot dynamics, optimization, and simulation. Common experience with dynamic robotic simulations suggests that the numerical robustness of the LCP solver often determines simulation usability: if the solver fails to find a solution or finds a solution with significant residual error, interpenetration can result, the simulation can gain energy, or both. This paper undertakes the first comprehensive evaluation of LCP solvers across the space of multi-rigid body contact problems. We evaluate the performance of these solvers along the dimensions of solubility, solution quality, and running time.

Modeling contact in multibody simulation is a difficult problem frequently characterized by numerically brittle algorithms, long running times, and inaccurate (with respect to theory) models. We present a comprehensive evaluation of four methods for contact modeling on seven benchmark scenarios in order to quantify the performance of these methods with respect to robustness and speed. We also assess the accuracy of these methods where possible. We conclude the paper with a prescriptive description in order to guide the user of multibody simulation.

Treating “resting” contacts (i.e., contacts with zero normal relative velocity), using forces is problematic due to inconsistent configurations. For this reason, treating resting contacts with impulses instead of forces has become common, but this approach also suffers from a significant problem: applying impulses at the time-of-contact can keep the simulation from advancing. This scenario is analogous to one of the paradoxes devised by the philosopher Zeno, and has been referred to as a Zeno point in the simulation community. I describe how to avoid Zeno points without violating the theoretical dynamic behavior of the simulated bodies and without permitting interpenetration. Two experiments demonstrate that the method works as desired where alternative approaches that required accepting interpenetration or longer running times were previously required.

Two methods have been used extensively to model resting contact for rigid body simulation. The first approach, the penalty method, applies virtual springs to surfaces in contact to minimize interpenetration. This method, as typically implemented, results in oscillatory behavior and considerable penetration. The second approach, based on formulating resting contact as a linear complementarity problem, determines the resting contact forces analytically to prevent interpenetration. The analytical method exhibits expected-case polynomial complexity in the number of contact points, and may fail to find a solution in polynomial time when friction is modeled. We present a fast penalty method that minimizes oscillatory behavior and leads to little penetration during resting contact; our method compares favorably to the analytical method with regard to these two measures, while exhibiting much faster performance both asymptotically and empirically.

The successful acquisition and organization of a large number of skills for humanoid robots can be facilitated with a collection of performable tasks organized in a task matrix. Tasks in the matrix can utilize particular preconditions and in conditions to enable execution, motion trajectories to specify desired movement, and references to other tasks to perform subtasks. Interaction between the matrix and external modules such as goal planners is achieved via a high-level interface that categorizes a task using its semantics and execution parameters, allowing queries on the matrix to be performed using different selection criteria. Performable tasks are stored in an XML-based file format that can be readily edited and processed by other applications. In its current implementation, the matrix is populated with sets of primitive tasks (e.g. reaching, grasping, arm-waving) and macro tasks that reference multiple primitive tasks (pick-and-place and facing-and-waving)

Reaching is a critical task for humanoid robots, requiring the application of state-of-the-art algorithms for motion planning and inverse kinematics. Practical algorithms for solving these subproblems are currently not complete, offering resolution completeness or probabilistic completeness instead. While this lesser provision of completeness is acceptable in many cases, naively combining state-of-the-art approaches in motion planning and inverse kinematics can lead to a method that provides no measure of completeness. We present a probabilistically complete solution to the reaching problem for humanoid robots in static environments, and evaluate it against two other methods using a kinematically simulated humanoid in a virtual environment

We present a unified methodology for humanoid robot control and activity, classification using motor primitives (Mataric, M, 2002), computationally efficient behaviors capable of perception and control. These primitives constitute a vocabulary for humanoid control capable of generating a large variety of complex movement through sequencing and superposition. We demonstrate how such primitives can be automatically derived from human motion-capture data, how they can be used to construct upperbody controllers, and how they can be applied to classification of observed humanoid behavior in real time.

We introduce a computationally efficient methodology for generating and recognizing free-space movements for humanoid robots. This methodology operates on exemplar-based representations of behaviors. Our method for actuating humanoid robots allows us to perform variations on a given behavior, resulting in a very humanlike movement appearance. Besides control, this method also facilitates classification of perceived human and humanoid robot movement. We demonstrate the method on a physically-simulated humanoid robot with 132 degrees-offreedom and evaluate our movement classification methodology on two data sets: human motion-capture data and joint-angle data sampled from the simulated robot.

We show that fuzzy neural nets can rate the emotional feedback message implicit in human facial expressions at a level comparable to human performance. We also identify primitive and derived features in facial expressions and animations. We also report on a partial solution to the difficult inverse problem of generating naturalistic facial expressions from summary abstract descriptions of the emotional feedback to be conveyed.

Biomolecular computing (BMC) aims to capture the innumerable advantages that biological molecules have gained in the course of millions of years of evolution to perform computation unfeasible on conventional electronic computers. While biomolecules have resolved fundamental problems as a parallel computer system that we are just beginning to decipher, BMC still suffers from our inability to harness these properties to bring biomolecular computations to levels of reliability, efficiency and scalability that are now taken for granted with conventional solid-state based computers. The authors explore an alternative approach to exploiting these properties by building virtual test tubes in software that would capture the fundamental advantages of biomolecules, in the same way that evolutionary algorithms capture in silico the key properties of Darwinian evolution. We use a previously built tool, Edna, to explore the capabilities of the new paradigm.