Hand motion capture data are now relatively easy to obtain, even for complicated grasps; however, these data are of limited use without the ability to retarget it onto the hands of a specific character or robot. The target hand may differ dramatically in geometry, number of degree of freedom (DOF), or number of fingers. We present a simple but effective framework capable of kinematically retargeting human hand-object manipulations from a publicly available dataset to diverse target hands through the exploitation of contact areas. We do so by formulating the retargeting operation as a nonisometric shape matching problem and use a combination of both surface contact and marker data to progressively estimate, refine, and fit the final target hand trajectory using inverse kinematics. Foundational to our framework is the introduction of a novel shape matching process, which we show enables predictable and robust transfer of contact data over full manipulations (pregrasp, pickup, in-hand re-orientation, and release) while providing an intuitive means for artists to specify correspondences with relatively few inputs. We validate our framework through demonstrations across five different hands and six motions of different objects. We additionally demonstrate a bimanual task, perform stress tests, and compare our method against existing hand retargeting approaches. Finally, we demonstrate our method enabling novel capabilities such as object substitution and the ability to visualize the impact of hand design choices over full trajectories.

Posing high-contact interactions is challenging and time-consuming, with hand-object interactions being especially difficult due to the large number of degrees of freedom (DOF) of the hand and the fact that humans are experts at judging hand poses. This paper addresses this challenge by elevating contact areas to first-class primitives. We provide end-to-end art-directable (EAD) tools to model interactions based on contact areas, directly manipulate contact areas, and compute corresponding poses automatically. To make these operations intuitive and fast, we present a novel axis-based contact model that supports real-time approximately isometry-preserving operations on triangulated surfaces, permits movement between surfaces, and is both robust and scalable to large areas. We show that use of our contact model facilitates high quality posing even for unconstrained, high-DOF custom rigs intended for traditional keyframe-based animation pipelines. We additionally evaluate our approach with comparisons to prior art, ablation studies, user studies, qualitative assessments, and extensions to full-body interaction.

In this work, we investigate a form of dynamic contact-rich locomotion in which a robot pushes off from obstacles in order to move through its environment. We present a reflex-based approach that switches between optimized hand-crafted reflex controllers and produces smooth and predictable motions. In contrast to previous work, our approach does not rely on periodic movements, complex models of robot and contact dynamics, or extensive hand tuning. We demonstrate the effectiveness of our approach and evaluate its performance compared to a standard model-free RL algorithm. We identify continuous clusters of similar behaviours, which allows us to successfully transfer different push-off motions directly from simulation to a physical robot without further retraining.

We present a novel method for the direct transfer of grasps and manipulations between objects and hands through utilization of contact areas. Our method fully preserves contact shapes, and in contrast to existing techniques, is not dependent on grasp families, requires no model training or grasp sampling, makes no assumptions about manipulator morphology or kinematics, and allows user control over both transfer parameters and solution optimization. Despite these accommodations, we show that our method is capable of synthesizing kinematically-feasible whole hand poses in seconds even for poor initializations or hard-to-reach contacts. We additionally highlight the method's benefits in both response to design alterations as well as fast approximation over in-hand manipulation sequences. Finally, we demonstrate a solution generated by our method on a physical, custom-designed prosthetic hand.

We present a novel, low cost framework for reconstructing surface contact movements during in-hand manipulations. Unlike many existing methods focused on hand pose tracking, ours models the behavior of contact patches, and by doing so is the first to obtain detailed contact tracking estimates for multi-contact manipulations. Our framework is highly accessible, requiring only low cost, readily available paint materials, a single RGBD camera, and a simple, deterministic interpolation algorithm. Despite its simplicity, we demonstrate the framework’s effectiveness over the course of several manipulations on three common household items. Finally, we demonstrate the use of a generated contact time series in manipulation learning for a simulated robot hand.

We estimate the state of a noisy robot arm and underactuated hand using an implicit Manifold Particle Filter (MPF) informed by contact sensors. As the robot touches the world, its state space collapses to a contact manifold that we represent implicitly using a signed distance field. This allows us to extend the MPF to higher (six or more) dimensional state spaces. Earlier work, which explicitly represents the contact manifold, was only capable of scaling to three dimensions. Through a series of experiments, we show that the implicit MPF converges faster and is more accurate than a conventional particle filter during periods of persistent contact. We present three methods of drawing samples from an implicit contact manifold, and compare them in experiments.

Abstract Contact behaviors in physics simulations are important for real‐time interactive applications, especially in virtual reality applications where user's body parts are tracked and interact with the environment via contact. For these contact simulations, it is ideal to have small changes in initial condition yield predictable changes in the output. Predictable simulation is key for success in iterative learning processes as well, such as learning controllers for manipulations or locomotion tasks. Here, we present an extensive comparison of contact simulations using Bullet Physics, Dynamic Animation and Robotics Toolkit (DART), MuJoCo, and Open Dynamics Engine, with a focus on predictability of behavior. We first tune each engine to match an analytical solution as closely as possible and then compare the results for a more complex simulation. We found that in the commonly available physics engines, small changes in initial condition can sometimes induce different sequences of contact events to occur and ultimately lead to a vastly different result. Our results confirmed that parameter settings do matter a great deal and suggest that there may be a trade‐off between accuracy and predictability. Copyright © 2016 John Wiley & Sons, Ltd.

We present an algorithm for generating open-loop trajectories that solve the problem of rearrangement planning under uncertainty. We frame this as a selection problem where the goal is to choose the most robust trajectory from a finite set of candidates. We generate each candidate using a kinodynamic state space planner and evaluate it using noisy rollouts. Our key insight is we can formalize the selection problem as the “best arm” variant of the multi-armed bandit problem. We use the successive rejects algorithm to efficiently allocate rollouts between candidate trajectories given a rollout budget. We show that the successive rejects algorithm identifies the best candidate using fewer rollouts than a baseline algorithm in simulation. We also show that selecting a good candidate increases the likelihood of successful execution on a real robot.

Successful and robust grasping for humanoid robots is still an ongoing research topic in robotics. Applying human-inspired grasping strategies does not only correspond with more natural looking motions but can also yield good results regarding task success when having to deal with uncertainty. This study investigates human high-level grasping strategies and how they tend to change for different objects when the uncertainty of object location or orientation increases in between two grasps. We are especially interested in potential gains for humanoid robots in a common household setting. By analyzing collected data from human subject grasp experiments with a set of typical objects found in people's homes, we get better insight into how humans handle uncertainty, as well as when and how they change their applied pre-grasp strategy. By adapting the by far most often observed change from a direct grasp attempt to a tapping strategy when dealing with high uncertainty, we can demonstrate a substantial increase of grasp success rate for our robot system with a Shadow Dexterous Hand mounted on a Motoman SDA10 robot while using less than two hand correction steps on average.

Creating facial animations that convey an animator’s intent is a difficult task because animation techniques are necessarily an approximation of the subtle motion of the face. Some animation techniques may result in linearization of the motion of vertices in space (blendshapes, for example), and other, simpler techniques may result in linearization of the motion in time. In this article, we consider the problem of animating smiles and explore how these simplifications in space and time affect the perceived genuineness of smiles. We create realistic animations of spontaneous and posed smiles from high-resolution motion capture data for two computer-generated characters. The motion capture data is processed to linearize the spatial or temporal properties of the original animation. Through perceptual experiments, we evaluate the genuineness of the resulting smiles. Both space and time impact the perceived genuineness. We also investigate the effect of head motion in the perception of smiles and show similar results for the impact of linearization on animations with and without head motion. Our results indicate that spontaneous smiles are more heavily affected by linearizing the spatial and temporal properties than posed smiles. Moreover, the spontaneous smiles were more affected by temporal linearization than spatial linearization. Our results are in accordance with previous research on linearities in facial animation and allow us to conclude that a model of smiles must include a nonlinear model of velocities.

Many robotic systems deal with uncertainty by performing a sequence of information gathering actions. In this work, we focus on the problem of efficiently constructing such a sequence by drawing an explicit connection to submodularity. Ideally, we would like a method that finds the optimal sequence, taking the minimum amount of time while providing sufficient information. Finding this sequence, however, is generally intractable. As a result, many well-established methods select actions greedily. Surprisingly, this often performs well. Our work first explains this high performance - we note a commonly used metric, reduction of Shannon entropy, is submodular under certain assumptions, rendering the greedy solution comparable to the optimal plan in the offline setting. However, reacting online to observations can increase performance. Recently developed notions of adaptive submodularity provide guarantees for a greedy algorithm in this online setting. In this work, we develop new methods based on adaptive submodularity for selecting a sequence of information gathering actions online. In addition to providing guarantees, we can capitalize on submodularity to attain additional computational speedups. We demonstrate the effectiveness of these methods in simulation and on a robot.

We investigate the problem of estimating the state of an object during manipulation. Contact sensors provide valuable information about the object state during actions which involve persistent contact, e.g. pushing. However, contact sensing is very discriminative by nature, and therefore the set of object states that contact a sensor constitutes a lower-dimensional manifold in the state space of the object. This causes stochastic state estimation methods, such as particle filters, to perform poorly when contact sensors are used. We propose a new algorithm, the manifold particle filter, which uses dual particles directly sampled from the contact manifold to avoid this problem. The algorithm adapts to the probability of contact by dynamically changing the number of dual particles sampled from the manifold. We compare our algorithm to the conventional particle filter through extensive experiments and we show that our algorithm is both faster and better at estimating the state. Unlike the conventional particle filter, our algorithm's performance improves with increasing sensor accuracy and the filter's update rate. We implement the algorithm on a real robot using commercially available tactile sensors to track the pose of a pushed object.

The problem of mapping a data flow graph onto a reconfigurable architecture has been difficult to solve quickly and optimally. Anytime algorithms have the potential to meet both goals by generating a good solution quickly and improving that solution over time, but they have not been shown to be practical for mapping. The key insight into this paper is that mapping algorithms based on search trees can be accelerated using a database of examples of high quality mappings. The depth of the search tree is reduced by placing patterns of nodes rather than single nodes at each level. The branching factor is reduced by placing patterns only in arrangements present in a dictionary constructed from examples. We present two anytime algorithms that make use of patterns and dictionaries: Anytime A*and Anytime Multiline Tree Rollup. We compare these algorithms to simulated annealing and to results from human mappers playing the online game UNTANGLED. The anytime algorithms outperform simulated annealing and the best game players in the majority of cases, and the combined results from all algorithms provide an informative comparison between architecture choices.

Although there has been great progress in robot grasp planning, automatically generated grasp sets using a quality metric are not as robust as human-generated grasp sets when applied to real problems. Most previous research on grasp quality metrics has focused on measuring the quality of established grasp contacts after grasping, but it is difficult to reproduce the same planned final grasp configuration with a real robot hand, which makes the quality evaluation less useful in practice. In this study, we focus more on the grasping process, which usually involves changes in contact and object location, and explore the efficacy of using dynamic simulation in estimating the likely success or failure of a grasp in the real environment. Among many factors that can possibly affect the result of grasping, we particularly investigated the effect of considering object dynamics and pose uncertainty on the performance in estimating the actual grasp success rates measured from experiments. We observed that considering both dynamics and uncertainty improved the performance significantly, and when applied to automatic grasp set generation, this method generated more stable and natural grasp sets compared with a commonly used method based on kinematic simulation and force-closure analysis.

In this paper new grasp quality measures considering both object dynamics and pose uncertainty are proposed. Dynamics of the object is incorporated into our grasping simulation to capture the change of its pose and contact points during grasping. Pose uncertainty is considered by running multiple simulations starting from slightly different initial poses sampled from a probability distribution model. A simple robotic grasping strategy is simulated and the quality score of the resulting grasp is evaluated from the simulation result. The effectiveness of the new quality measures on predicting the actual grasp success rate is shown through a real robot experiment.

We describe the software components of a robotics system designed to autonomously grasp objects and perform dexterous manipulation tasks with only high-level supervision. The system is centered on the tight integration of several core functionalities, including perception, planning and control, with the logical structuring of tasks driven by a Behavior Tree architecture. The advantage of the implementation is to reduce the execution time while integrating advanced algorithms for autonomous manipulation. We describe our approach to 3-D perception, real-time planning, force compliant motions, and audio processing. Performance results for object grasping and complex manipulation tasks of in-house tests and of an independent evaluation team are presented.

Abstract In this paper, we present a continuous approach to ordering 2D images when compositing. Previous methods for stacking image layers require them to appear in a single (though possibly different) order at every point in the image. Our soft stacking approach removes this restriction — allowing layers to stack as if they were volumes of fog, appearing partially in front of and partially in back of other layers within the same pixel, and moving smoothly through other layers across the image. Our approach involves augmenting each pixel with stacking coefficients— a necessary and sufficient representation for sub‐pixel stacking complexity. These stacking coefficients arise naturally when considering sub‐pixel stacking complexity, much as continuous (alpha) transparency arises when considering sub‐pixel coverage complexity. While the number of stacking coefficients required to represent all possible sub‐pixel stacking arrangements is factorial in the number of layers in the stack, in many practical situations only a small subset of the stacking coefficients are nonzero. We use this sparsity as the basis of a prototype that allows artists to interactively paint stacking adjustments into composites. Additionally, we demonstrate how to generate optimally‐stacked images under a generalized notion of stacking consistency.

Manipulators with compliant actuation exhibit passive joint displacements when exposed to external forces or collisions. This paper demonstrates that this displacement information is sufficient to infer a coarse estimate of the location of an incidental collision. Three techniques for contact point detection are compared: a closed-form inference model based on a serial chain with joint springs, a variation on Self Posture Changeability, and an empirical memory-based model of joint trajectories. The methods were experimentally tested using a Shadow Hand on an industrial Motoman SDA10 arm to quantify localization performance, actively discover and avoid a thin obstacle and localize and grasp a cup.

We propose a fast physically-based simulation system for skeleton-driven deformable body characters. Our system can generate realistic motions of self-propelled deformable body characters by considering the two-way interactions among the skeleton, the deformable body, and the environment in the dynamic simulation. It can also compute the passive jiggling behavior of a deformable body driven by a kinematic skeletal motion. We show that a well-coordinated combination of: (1) a reduced deformable body model with nonlinear finite elements, (2) a linear-time algorithm for skeleton dynamics, and (3) explicit integration can boost simulation speed to orders of magnitude faster than existing methods, while preserving modeling accuracy as much as possible. Parallel computation on the GPU has also been implemented to obtain an additional speedup for complicated characters. Detailed discussions of our engineering decisions for speed and accuracy of the simulation system are presented in the article. We tested our approach with a variety of skeleton-driven deformable body characters, and the tested characters were simulated in real time or near real time.

Studies of human manipulation strategies suggest that pre-grasp object manipulation, such as rotation or sliding of the object to be grasped, can improve task performance by increasing both the task success rate and the quality of load-supporting postures. In previous demonstrations, pre-grasp object rotation by a robot manipulator was limited to manually-programmed actions. We present a method for automating the planning of pre-grasp rotation for object transport tasks. Our technique optimizes the grasp acquisition point by selecting a target object pose that can be grasped by high-payload manipulator configurations. Careful selection of the transition states leads to successful transport plans for tasks that are otherwise infeasible. In addition, optimization of the grasp acquisition posture also indirectly improves the transport plan quality, as measured by the safety margin of the manipulator payload limits.

Given multiple time sequences with missing values, we propose DynaMMo which summarizes, compresses, and finds latent variables. The idea is to discover hidden variables and learn their dynamics, making our algorithm able to function even when there are missing values.We performed experiments on both real and synthetic datasets spanning several megabytes, including motion capture sequences and chlorine levels in drinking water. We show that our proposed DynaMMo method (a) can successfully learn the latent variables and their evolution; (b) can provide high compression for little loss of reconstruction accuracy; (c) can extract compact but powerful features for segmentation, interpretation, and forecasting; (d) has complexity linear on the duration of sequences.

In a conventional 2d painting or compositing program, graphical objects are stacked in a user-specified global order, as if each were printed on an image-sized sheet of transparent film. In this paper we show how to relax this restriction so that users can make stacking decisions on a per-overlap basis, as if the layers were pictures cut from a magazine. This allows for complex and visually exciting overlapping patterns, without painstaking layer-splitting, depth-value painting, region coloring, or mask-drawing. Instead, users are presented with a layers dialog which acts locally. Behind the scenes, we divide the image into overlap regions and track the ordering of layers in each region. We formalize this structure as a graph of stacking lists, define the set of orderings where layers do not interpenetrate as consistent, and prove that our local stacking operators are both correct and sufficient to reach any consistent stacking. We also provide a method for updating the local stacking when objects change shape or position due to user editing - this scheme prevents layer updates from producing undesired intersections. Our method extends trivially to both animation compositing and local visibility adjustment in depth-peeled 3d scenes; the latter of which allows for the creation of impossible figures which can be viewed and manipulated in real-time.

Abstract An increasing number of projects have examined the perceptual magnitude of visible artifacts in animated motion. These studies have been performed using a mix of character types, from detailed human models to abstract geometric objects such as spheres. We explore the extent to which character morphology influences user sensitivity to errors in a fixed set of ballistic motions replicated on three different character types. We find user sensitivity responds to changes in error type or magnitude in a similar manner regardless of character type, but that users display a higher sensitivity to some types of errors when these errors are displayed on more human‐like characters. Further investigation of those error types suggests that being able to observe a period of preparatory motion before the onset of ballistic motion may be important. However, we found no evidence to suggest that a mismatch between the preparatory phase and the resulting ballistic motion was responsible for the higher sensitivity to errors that was observed for the most humanlike character.

We present an image editing program which allows artists to paint in the gradient domain with real-time feedback on megapixel-sized images. Along with a pedestrian, though powerful, gradient-painting brush and gradient-clone tool, we introduce an edge brush designed for edge selection and replay. These brushes, coupled with special blending modes, allow users to accomplish global lighting and contrast adjustments using only local image manipulations --- e.g. strengthening a given edge or removing a shadow boundary. Such operations would be tedious in a conventional intensity-based paint program and hard for users to get right in the gradient domain without real-time feedback. The core of our paint program is a simple-to-implement GPU multigrid method which allows integration of megapixel-sized full-color gradient fields at over 20 frames per second on modest hardware. By way of evaluation, we present example images produced with our program and characterize the iteration time and convergence rate of our integration method.

This paper explores the planning and control of a manipulation task accomplished in conditions of high uncertainty. Statistical techniques, like particle filters, provide a framework for expressing the uncertainty and partial observability of the real world and taking actions to reduce them. We explore a classic manipulation problem of planar batting, but with a new twist of shape, pose and impact uncertainty. We demonstrate a technique for characterizing and reducing this uncertainty using a particle filter coupled with a lookahead planner that maximizes information gain. We show that a two-step planner that first acts for information gain and then acts to maximize the expectation of achieving a desired goal is effective at managing shape, pose and impact uncertainty

Although the human hand is a complex biomechanical system, only a small set of features may be necessary for observation learning of functional grasp classes. We explore how to methodically select a minimal set of hand pose features from optical marker data for grasp recognition. Supervised feature selection is used to determine a reduced feature set of surface marker locations on the hand that is appropriate for grasp classification of individual hand poses. Classifiers trained on the reduced feature set of five markers retain at least 92% of the prediction accuracy of classifiers trained on a full feature set of thirty markers. The reduced model also generalizes better to new subjects. The dramatic reduction of the marker set size and the success of a linear classifier from local marker coordinates recommend optical marker techniques as a practical alternative to data glove methods for observation learning of grasping.

In game environments, animated character motion must rapidly adapt to changes in player input - for example, if a directional signal from the player's gamepad is not incorporated into the character's trajectory immediately, the character may blithely run off a ledge. Traditional schemes for data-driven character animation lack the split-second reactivity required for this direct control; while they can be made to work, motion artifacts will result. We describe an on-line character animation controller that assembles a motion stream from short motion fragments, choosing each fragment based on current player input and the previous fragment. By adding a simple model of player behavior we are able to improve an existing reinforcement learning method for precalculating good fragment choices. We demonstrate the efficacy of our model by comparing the animation selected by our new controller to that selected by existing methods and to the optimal selection, given knowledge of the entire path. This comparison is performed over real-world data collected from a game prototype. Finally, we provide results indicating that occasional low-quality transitions between motion segments are crucial to high-quality on-line motion generation; this is an important result for others crafting animation systems for directly-controlled characters, as it argues against the common practice of transition thresholding.

Realistic and directable humanlike characters are an ongoing goal in animation. Motion graph data structures hold much promise for achieving this goal; however, the quality of the results obtainable from a motion graph may not be easy to predict from its input motion clips. This article describes a method for using task-based metrics to evaluate the capability of a motion graph to create the set of animations required by a particular application. We examine this capability for typical motion graphs across a range of tasks and environments. We find that motion graph capability degrades rapidly with increases in the complexity of the target environment or required tasks, and that addressing deficiencies in a brute-force manner tends to lead to large, unwieldy motion graphs. The results of this method can be used to evaluate the extent to which a motion graph will fulfill the requirements of a particular application, lessening the risk of the data structure performing poorly at an inopportune moment. The method can also be used to characterize the deficiencies of motion graphs whose performance will not be sufficient, and to evaluate the relative effectiveness of different options for improving those motion graphs.

Human grasps, especially whole-hand grasps, are difficult to animate because of the high number of degrees of freedom of the hand and the need for the hand to conform naturally to the object surface. Captured human motion data provides us with a rich source of examples of natural grasps. However, for each new object, we are faced with the problem of selecting the best grasp from the database and adapting it to that object. This paper presents a data-driven approach to grasp synthesis. We begin with a database of captured human grasps. To identify candidate grasps for a new object, we introduce a novel shape matching algorithm that matches hand shape to object shape by identifying collections of features having similar relative placements and surface normals. This step returns many grasp candidates, which are clustered and pruned by choosing the grasp best suited for the intended task. For pruning undesirable grasps, we develop an anatomically-based grasp quality measure specific to the human hand. Examples of grasp synthesis are shown for a variety of objects not present in the original database. This algorithm should be useful both as an animator tool for posing the hand and for automatic grasp synthesis in virtual environments.

Grasp quality measures are important for understanding how to plan for and maintain appropriate and secure grasps for pick and place operations and tool use. Most grasp quality measures assume certain symmetries about the mechanism or the task. For example, contact points may be considered to be independent and identical, or an ellipsoidal measure such as the force manipulability ellipsoid may be used. However, many tasks have strong asymmetries, where wrenches in certain directions dominate. Tendon driven hand designs may also have strong asymmetries, leading to differing abilities to apply contact forces in different directions. This paper begins to explore empirically the validity of some of the symmetry assumptions employed by common grasp quality metrics. We examine the human hand and the shadow robot hand, and find that force abilities vary with finger choice and with location of the contact on the finger for both hands. However, while the human hand shows dramatic changes for different poses due to its asymmetric design, the shadow hand, with a symmetric design shows much smaller changes and resembles the assumption of identical and independent contact points reasonably well. Thus, we suggest that the underlying design of the hand is a very important factor to consider for grasp quality metrics and for grasp planning and control. The specific grasp quality metric we study in this paper also brings together a variety of previous research. We outline a linear programming approach for computing a grasp quality metric that includes tendon force constraints and contact constraints and can handle any task described as a polytope in wrench space

Optimization is an appealing way to compute the motion of an animated character because it allows the user to specify the desired motion in a sparse, intuitive way. The difficulty of solving this problem for complex characters such as humans is due in part to the high dimensionality of the search space. The dimensionality is an artifact of the problem representation because most dynamic human behaviors are intrinsically low dimensional with, for example, legs and arms operating in a coordinated way. We describe a method that exploits this observation to create an optimization problem that is easier to solve. Our method utilizes an existing motion capture database to find a low-dimensional space that captures the properties of the desired behavior. We show that when the optimization problem is solved within this low-dimensional subspace, a sparse sketch can be used as an initial guess and full physics constraints can be enabled. We demonstrate the power of our approach with examples of forward, vertical, and turning jumps; with running and walking; and with several acrobatic flips.

Optimization is a promising way to generate new animations from a minimal amount of input data. Physically based optimization techniques, however, are difficult to scale to complex animated characters, in part because evaluating and differentiating physical quantities becomes prohibitively slow. Traditional approaches often require optimizing or constraining parameters involving joint torques; obtaining first derivatives for these parameters is generally an O ( D 2 ) process, where D is the number of degrees of freedom of the character. In this paper, we describe a set of objective functions and constraints that lead to linear time analytical first derivatives. The surprising finding is that this set includes constraints on physical validity, such as ground contact constraints. Considering only constraints and objective functions that lead to linear time first derivatives results in fast per-iteration computation times and an optimization problem that appears to scale well to more complex characters. We show that qualities such as squash-and-stretch that are expected from physically based optimization result from our approach. Our animation system is particularly useful for synthesizing highly dynamic motions, and we show examples of swinging and leaping motions for characters having from 7 to 22 degrees of freedom.

Motion capture data and techniques for blending, editing, and sequencing that data can produce rich, realistic character animation; however, the output of these motion processing techniques sometimes appears unnatural. For example, the motion may violate physical laws or reflect unreasonable forces from the character or the environment. While problems such as these can be fixed, doing so is not yet feasible in real time environments. We are interested in developing ways to estimate perceived error in animated human motion so that the output quality of motion processing techniques can be better controlled to meet user goals.This paper presents results of a study of user sensitivity to errors in animated human motion. Errors were systematically added to human jumping motion, and the ability of subjects to detect these errors was measured. We found that users were able to detect motion with errors, and noted some interesting trends: errors in horizontal velocity were easier to detect than errors in vertical velocity, and added accelerations were easier to detect than added decelerations. On the basis of our results, we propose a perceptually based metric for measuring errors in ballistic human motion.

Human motion can provide a rich source of examples for use in robot grasping and manipulation. Adapting human examples to a robot manipulator is a difficult problem, however, in part due to differences between human and robot hands. Even hands that are anthropomorphic in external design may differ dramatically from the human hand in ability to grasp and manipulate objects due to internal design differences. For example, force transmission mechanisms in robot fingers are generally symmetric about flexion/extension axes, but in human fingers they are focused toward flexion. This paper describes how a tendon driven robot finger can be optimized for force transmission capability equivalent to the human index finger. We show that two distinct tendon arrangements that are similar to those that have been used in robot hands can achieve the same range of forces as the human finger with minimal additional cost in total muscle force requirements.

Using the pre-recorded human motion and trajectory tracking, we can control the motion of a humanoid robot for free-space, upper body gestures. However, the number of degrees of freedom, range of joint motion, and achievable joint velocities of today's humanoid robots are far more limited than those of the average human subject. In this paper, we explore a set of techniques for limiting human motion of upper body gestures to that achievable by a Sarcos humanoid robot located at ATR. We assess the quality of the results by comparing the motion of the human actor to that of the robot, both visually and quantitatively.

Real-time control of three-dimensional avatars is an important problem in the context of computer games and virtual environments. Avatar animation and control is difficult, however, because a large repertoire of avatar behaviors must be made available, and the user must be able to select from this set of behaviors, possibly with a low-dimensional input device. One appealing approach to obtaining a rich set of avatar behaviors is to collect an extended, unlabeled sequence of motion data appropriate to the application. In this paper, we show that such a motion database can be preprocessed for flexibility in behavior and efficient search and exploited for real-time avatar control. Flexibility is created by identifying plausible transitions between motion segments, and efficient search through the resulting graph structure is obtained through clustering. Three interface techniques are demonstrated for controlling avatar motion using this data structure: the user selects from a set of available choices, sketches a path through an environment, or acts out a desired motion in front of a video camera. We demonstrate the flexibility of the approach through four different applications and compare the avatar motion to directly recorded human motion.

This paper describes a fast technique for modifying motion sequences for complex articulated mechanisms in a way that preserves physical properties of the motion. This technique is relevant to the problem of teaching motion tasks by demonstration, because it allows a single example to be adapted to a range of situations. Motion may be obtained from any source, e.g., it may be captured from a human user. A model of applied forces is extracted from the motion data, and forces are scaled to achieve new goals. Each scaled force model is checked to ensure that frictional and kinematic constraints are maintained for a rigid body approximation of the character. Scale factors can be obtained in closed form, and constraints can be approximated analytically, making motion editing extremely fast. To demonstrate the effectiveness of this approach, we show that a variety of simulated jumps can be created by modifying a single key-framed jumping motion. We also scale a simulated running motion to a new character and to a range of new velocities.

This paper describes a parallel, dynamic programming algorithm for computing the space of high quality, whole-hand grasps of a target object that match a given grasp prototype, or example grasp. The grasp prototype is used to define a quality measure that allows the contacts of a grasp to be optimized independently. Link configurations that achieve high quality contacts are identified and chained together to form good hand configurations. This algorithm results in a six-dimensional projection of the global solution space. Grasps synthesized using this technique were tested using the Salisbury hand. An example grasp is shown.

This paper describes an algorithm for automatically adapting existing simulated behaviors to new characters. Animating a new character is difficult because a control system tuned for one character will not, in general, work on a character with different limb lengths, masses, or moments of inertia. The algorithm presented here adapts the control system to a new character in two stages. First, the control system parameters are scaled based on the sizes, masses, and moments of inertia of the new and the original characters. Then a subset of the parameters is fine-tuned using a search process based on simulated annealing. To demonstrate the effectiveness of this approach, we animate the running motion of a woman, child, and imaginary character by modifying the control system for a man. We also animate the bicycling motion of a second imaginary character by modifying the control system for a man. We evaluate the results of this approach by comparing the motion of the simulated human runners with video of an actual child and with data for men, women, and children in the literature. In addition to adapting a control system for a new model, this approach can also be used to adapt the control system in an on-line fashion to produce a physically realistic metamorphosis from the original to the new model while the morphing character is performing the behavior. We demonstrate this on-line adaptation with a morph from a man to a woman over a period of twenty seconds.

This paper introduces a grasp synthesis algorithm that can use any grasp prototype as the starting point in a search for a good grasp. The algorithm makes the given prototype more effective by generalizing it for a specific task. This generalization step expands the range of application of the prototype to a wide variety of target object geometries, while ensuring that the resulting grasps are appropriate for the intended task. An example whole-hand grasp synthesized for the Salisbury hand is shown at the end of the paper.

A system for generating a stable, feasible grasp of a polyhedral object is presented. A set of contact points on the object that can result in a stable grasp is found, and a feasible grasp in which the robot contacts the object at those contact points is determined. The algorithm is designed for the Salisbury hand mounted on a Puma 560 arm, but a similar approach could be used to develop grasping systems for other robots. Simulations show that the system can generate a wide range of grasps in difficult situations.>