Dynamically-Stable Motion Planning for Humanoid Robots
Autonomous Robots
A spatio-temporal extension to Isomap nonlinear dimension reduction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces
ACM SIGGRAPH 2004 Papers
Incremental Online Learning in High Dimensions
Neural Computation
Planning Algorithms
Non-isometric manifold learning: analysis and an algorithm
Proceedings of the 24th international conference on Machine learning
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Topologically-constrained latent variable models
Proceedings of the 25th international conference on Machine learning
Synthesising Novel Movements through Latent Space Modulation of Scalable Control Policies
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
Proceedings of the 2008 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
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Motion synthesis for humanoid robot behaviours is made difficult by the combination of task space, joint space and kinodynamic constraints that define realisability. Solving these problems by general purpose methods such as sampling based motion planning has involved significant computational complexity, and has also required specialised heuristics to handle constraints. In this paper we propose an approach to incorporate specifications and constraints as a bias in the exploration process of such planning algorithms. We present a general approach to solving this problem wherein a subspace, of the configuration space and consisting of poses involved in a specific task, is identified in the form of a nonlinear manifold, which is in turn used to focus the exploration of a sampling based motion planning algorithm. This allows us to solve the motion planning problem so that we synthesize previously unseen paths for novel goals in a way that is strongly biased by known good or feasible paths, e.g., from human demonstration. We demonstrate this result with a simulated humanoid robot performing a number of bipedal tasks.