Proceedings ArticleDOI
Optimization of sequential attractor-based movement for compact behaviour generation
Marc Toussaint,Michael Gienger,Christian Goerick +2 more
- pp 122-129
Reads0
Chats0
TLDR
A novel method to generate optimal robot motion based on a sequence of attractor dynamics in task space, motivated by the biological evidence that movements in the motor cortex of animals are encoded in a similar fashion and by the need for compact movement representations on which efficient optimization can be performed.Abstract:
In this paper, we propose a novel method to generate optimal robot motion based on a sequence of attractor dynamics in task space This is motivated by the biological evidence that movements in the motor cortex of animals are encoded in a similar fashion- and by the need for compact movement representations on which efficient optimization can be performed We represent the motion as a sequence of attractor points acting in the task space of the motion Based on this compact and robust representation, we present a scheme to generate optimal movements Unlike traditional optimization techniques, this optimization is performed on the low-dimensional representation of the attractor points and includes the underlying control loop itself as subject to optimization We incorporate optimality criteria such as eg the smoothness of the motion, collision distance measures, or joint limit avoidance The optimization problem is solved efficiently employing the analytic equations of the overall system Due to the fast convergence, the method is suited for dynamic environments, including the interaction with humans We will present the details of the optimization scheme, and give a description of the chosen optimization criteria Simulation and experimental results on the humanoid robot ASIMO will underline the potential of the proposed approachread more
Citations
More filters
Journal ArticleDOI
Dopamine, affordance and active inference.
Karl J. Friston,Tamara Shiner,Thomas H. B. FitzGerald,Joseph M. Galea,Rick A. Adams,Harriet R. Brown,Raymond J. Dolan,Rosalyn J. Moran,Klaas E. Stephan,Sven Bestmann +9 more
TL;DR: This paper focuses on the consequences of changing tonic levels of dopamine firing using simulations of cued sequential movements and uses these simulations to demonstrate how a single functional role for dopamine at the synaptic level can manifest in different ways at the behavioural level.
Proceedings ArticleDOI
Fast smoothing of manipulator trajectories using optimal bounded-acceleration shortcuts
Kris Hauser,Victor Ng-Thow-Hing +1 more
TL;DR: This paper construct segments that interpolate between endpoints with specified velocity in a time-optimal fashion, while respecting velocity and acceleration bounds, and can be computed in closed form.
Proceedings ArticleDOI
Task-level imitation learning using variance-based movement optimization
TL;DR: An imitation learning framework is presented, which allows the robot to learn the important elements of an observed movement task by application of probabilistic encoding with Gaussian Mixture Models and shows that the proposed system is suitable for transferring information from a human demonstrator to the robot.
Journal ArticleDOI
Collision-free and smooth trajectory computation in cluttered environments
TL;DR: A novel trajectory computation algorithm to smooth piecewise linear collision-free trajectories computed by sample-based motion planners and a fast and reliable algorithm for collision checking between a robot and the environment along the B-spline trajectories.
Journal ArticleDOI
Dynamic walking and whole-body motion planning for humanoid robots: an integrated approach
Sébastien Dalibard,Antonio El Khoury,Florent Lamiraux,Alireza Nakhaei,Michel Taix,Jean-Paul Laumond +5 more
TL;DR: First, a randomized algorithm for constrained motion planning is presented, that is used to generate collision-free statically balanced paths solving manipulation tasks, and it is shown that dynamic walking makes humanoid robots small-space controllable.
References
More filters
Journal ArticleDOI
Probabilistic roadmaps for path planning in high-dimensional configuration spaces
TL;DR: Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (/spl ap/150 MIPS), after learning for relatively short periods of time (a few dozen seconds).
Proceedings ArticleDOI
RRT-connect: An efficient approach to single-query path planning
TL;DR: A simple and efficient randomized algorithm is presented for solving single-query path planning problems in high-dimensional configuration spaces by incrementally building two rapidly-exploring random trees rooted at the start and the goal configurations.
Proceedings ArticleDOI
Movement imitation with nonlinear dynamical systems in humanoid robots
TL;DR: The results demonstrate that multi-joint human movements can be encoded successfully by the CPs, that a learned movement policy can readily be reused to produce robust trajectories towards different targets, and that the parameter space which encodes a policy is suitable for measuring to which extent two trajectories are qualitatively similar.
Proceedings Article
Learning Attractor Landscapes for Learning Motor Primitives
TL;DR: By nonlinearly transforming the canonical attractor dynamics using techniques from nonparametric regression, almost arbitrary new nonlinear policies can be generated without losing the stability properties of the canonical system.
Journal ArticleDOI
Linear combinations of primitives in vertebrate motor control
TL;DR: It is found that the simultaneous stimulation of two sites leads to the vector summation of the endpoint forces generated by each site separately, providing strong support to the view that the central nervous system may generate a wide repertoire of motor behaviors through the vectorial superposition of a few motor primitives stored within the neural circuits in the spinal cord.