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Center of Mass(CoM) estimation in rough terrains is hampered by complicated body dynamics yet remains critically important in the study of human and robot motion planning. Current techniques for CoM estimation are encumbered by lengthy calibration periods requiring the use of specialized tools(force plates, motion capture, etc). This paper presents a novel and straightforward geometric method for...
Operating and maneuvering in difficult terrains has remained a challenging problem in the field of legged robots. One of the major challenges arises from the high dimensionality inherent in planning foot placements coupled with center of mass motion along terrains that are multifaceted and highly diverse. Previous work has resolved these issues to an extent by constraining the center of mass to fixed...
This paper presents a thorough analysis of the computational complexity of optimal reconfiguration planning problem for chain-type modular robots, i.e. finding the least number of reconfiguration steps to transform from the initial configuration into the goal configuration. It establishes a formal proof that this problem is NP-complete, even if the configurations are acyclic. This result gives a compelling...
This paper presents a learning algorithm called surprise-based learning (SBL) capable of providing a physical robot the ability to autonomously learn and plan in an unknown environment without any prior knowledge of its actions or their impact on the environment. This is achieved by creating a model of the environment using prediction rules. A prediction rule describes the observations of the environment...
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