24,079 research outputs found
Goal Set Inverse Optimal Control and Iterative Re-planning for Predicting Human Reaching Motions in Shared Workspaces
To enable safe and efficient human-robot collaboration in shared workspaces
it is important for the robot to predict how a human will move when performing
a task. While predicting human motion for tasks not known a priori is very
challenging, we argue that single-arm reaching motions for known tasks in
collaborative settings (which are especially relevant for manufacturing) are
indeed predictable. Two hypotheses underlie our approach for predicting such
motions: First, that the trajectory the human performs is optimal with respect
to an unknown cost function, and second, that human adaptation to their
partner's motion can be captured well through iterative re-planning with the
above cost function. The key to our approach is thus to learn a cost function
which "explains" the motion of the human. To do this, we gather example
trajectories from pairs of participants performing a collaborative assembly
task using motion capture. We then use Inverse Optimal Control to learn a cost
function from these trajectories. Finally, we predict reaching motions from the
human's current configuration to a task-space goal region by iteratively
re-planning a trajectory using the learned cost function. Our planning
algorithm is based on the trajectory optimizer STOMP, it plans for a 23 DoF
human kinematic model and accounts for the presence of a moving collaborator
and obstacles in the environment. Our results suggest that in most cases, our
method outperforms baseline methods when predicting motions. We also show that
our method outperforms baselines for predicting human motion when a human and a
robot share the workspace.Comment: 12 pages, Accepted for publication IEEE Transaction on Robotics 201
Benchmarking of a software stack for autonomous racing against a professional human race driver
The way to full autonomy of public road vehicles requires the step-by-step
replacement of the human driver, with the ultimate goal of replacing the driver
completely. Eventually, the driving software has to be able to handle all
situations that occur on its own, even emergency situations. These particular
situations require extreme combined braking and steering actions at the limits
of handling to avoid an accident or to diminish its consequences. An average
human driver is not trained to handle such extreme and rarely occurring
situations and therefore often fails to do so. However, professional race
drivers are trained to drive a vehicle utilizing the maximum amount of possible
tire forces. These abilities are of high interest for the development of
autonomous driving software. Here, we compare a professional race driver and
our software stack developed for autonomous racing with data analysis
techniques established in motorsports. The goal of this research is to derive
indications for further improvement of the performance of our software and to
identify areas where it still fails to meet the performance level of the human
race driver. Our results are used to extend our software's capabilities and
also to incorporate our findings into the research and development of public
road autonomous vehicles.Comment: Accepted at 2020 Fifteenth International Conference on Ecological
Vehicles and Renewable Energies (EVER
Smooth and Non-Smooth Dependence of Lyapunov Vectors upon the Angle Variable on a Torus in the Context of Torus-Doubling Transitions in the Quasiperiodically Forced Henon Map
A transition from a smooth torus to a chaotic attractor in quasiperiodically
forced dissipative systems may occur after a finite number of torus-doubling
bifurcations. In this paper we investigate the underlying bifurcational
mechanism which seems to be responsible for the termination of the
torus-doubling cascades on the routes to chaos in invertible maps under
external quasiperiodic forcing. We consider the structure of a vicinity of a
smooth attracting invariant curve (torus) in the quasiperiodically forced Henon
map and characterize it in terms of Lyapunov vectors, which determine
directions of contraction for an element of phase space in a vicinity of the
torus. When the dependence of the Lyapunov vectors upon the angle variable on
the torus is smooth, regular torus-doubling bifurcation takes place. On the
other hand, the onset of non-smooth dependence leads to a new phenomenon
terminating the torus-doubling bifurcation line in the parameter space with the
torus transforming directly into a strange nonchaotic attractor. We argue that
the new phenomenon plays a key role in mechanisms of transition to chaos in
quasiperiodically forced invertible dynamical systems.Comment: 24 pages, 9 figure
Adaptive Smoothing for Trajectory Reconstruction
Trajectory reconstruction is the process of inferring the path of a moving
object between successive observations. In this paper, we propose a smoothing
spline -- which we name the V-spline -- that incorporates position and velocity
information and a penalty term that controls acceleration. We introduce a
particular adaptive V-spline designed to control the impact of irregularly
sampled observations and noisy velocity measurements. A cross-validation scheme
for estimating the V-spline parameters is given and we detail the performance
of the V-spline on four particularly challenging test datasets. Finally, an
application of the V-spline to vehicle trajectory reconstruction in two
dimensions is given, in which the penalty term is allowed to further depend on
known operational characteristics of the vehicle.Comment: 25 pages, submitte
Quantitative Kinematic Characterization of Reaching Impairments in Mice After a Stroke
Background and Objective. Kinematic analysis of reaching movements is increasingly used to evaluate upper extremity function after cerebrovascular insults in humans and has also been applied to rodent models. Such analyses can require time-consuming frame-by-frame inspections and are affected by the experimenter's bias. In this study, we introduce a semi-automated algorithm for tracking forepaw movements in mice. This methodology allows us to calculate several kinematic measures for the quantitative assessment of performance in a skilled reaching task before and after a focal cortical stroke. Methods. Mice were trained to reach for food pellets with their preferred paw until asymptotic performance was achieved. Photothrombosis was then applied to induce a focal ischemic injury in the motor cortex, contralateral to the trained limb. Mice were tested again once a week for 30 days. A high frame rate camera was used to record the movements of the paw, which was painted with a nontoxic dye. An algorithm was then applied off-line to track the trajectories and to compute kinematic measures for motor performance evaluation. Results. The tracking algorithm proved to be fast, accurate, and robust. A number of kinematic measures were identified as sensitive indicators of poststroke modifications. Based on end-point measures, ischemic mice appeared to improve their motor performance after 2 weeks. However, kinematic analysis revealed the persistence of specific trajectory adjustments up to 30 days poststroke, indicating the use of compensatory strategies. Conclusions. These results support the use of kinematic analysis in mice as a tool for both detection of poststroke functional impairments and tracking of motor improvements following rehabilitation. Similar studies could be performed in parallel with human studies to exploit the translational value of this skilled reaching analysis
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