Robot Motion Planning and Control
Robot Motion Planning and Control
Robot Motion Planning
Planning biped locomotion using motion capture data and probabilistic roadmaps
ACM Transactions on Graphics (TOG)
CASA '03 Proceedings of the 16th International Conference on Computer Animation and Social Agents (CASA 2003)
A 2-stages locomotion planner for digital actors
Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation
Learning physics-based motion style with nonlinear inverse optimization
ACM SIGGRAPH 2005 Papers
Planning Algorithms
On the nonholonomic nature of human locomotion
Autonomous Robots
Real-time path planning for humanoid robot navigation
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
An Optimality Principle Governing Human Walking
IEEE Transactions on Robotics
Planning 3-D Collision-Free Dynamic Robotic Motion Through Iterative Reshaping
IEEE Transactions on Robotics
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The purpose of this paper is to present inverse optimal control as a promising approach to transfer biological motions to robots. Inverse optimal control helps (a) to understand and identify the underlying optimality criteria of biological motions based on measurements, and (b) to establish optimal control models that can be used to control robot motion. The aim of inverse optimal control problems is to determine--for a given dynamic process and an observed solution--the optimization criterion that has produced the solution. Inverse optimal control problems are difficult from a mathematical point of view, since they require to solve a parameter identification problem inside an optimal control problem. We propose a pragmatic new bilevel approach to solve inverse optimal control problems which rests on two pillars: an efficient direct multiple shooting technique to handle optimal control problems, and a state-of-the art derivative free trust region optimization technique to guarantee a match between optimal control problem solution and measurements. In this paper, we apply inverse optimal control to establish a model of human overall locomotion path generation to given target positions and orientations, based on newly collected motion capture data. It is shown how the optimal control model can be implemented on the humanoid robot HRP-2 and thus enable it to autonomously generate natural locomotion paths.