Learning Utility Surfaces for Movement Selection

Humanoid robots are highly redundant systems with respect to the tasks they are asked to perform. This redundancy manifests itself in the number of degrees of freedom of the robot exceeding the dimensionality of the task. Traditionally this redundancy has been utilised through optimal control in the null-space. Some cost function is defined that encodes secondary movement goals and movements are optimised with respect to this functio

Behaviour Generation in Humanoids by Learning Potential-based Policies from Constrained Motion

Movement generation that is consistent with observed or demonstrated behaviour is an efficient way to seed movement planning in complex, high-dimensionalmovement systems like humanoid robots.We present a method for learning potentialbased policies from constrained motion data. In contrast to previous approaches to direct policy learning, our method can combine observations from a variety of contexts where different constraints are in force, to learn the underlying unconstrained policy in form of its potential function. This allows us to generalise and predict behaviour where novel constraints apply. We demonstrate our approach on systems of varying complexity, including kinematic data from the ASIMO humanoid robot with 22 degrees of freedom

Learning Potential-based Policies from Constrained Motion

We present a method for learning potential-based policies from constrained motion data. In contrast to previous approaches to direct policy learning, our method can combine observations from a variety of contexts where different constraints are in force, to learn the underlying unconstrained policy in form of its potential function. This allows us to generalise and predict behaviour where novel constraints apply. As a key ingredient, we first create multiple simple local models of the potential, and align those using an efficient algorithm.We can then detect and discard unsuitable subsets of the data and learn a global model from a cleanly pre-processed training set. We demonstrate our approach on systems of varying complexity, including kinematic data from the ASIMO humanoid robot with 22 degrees of freedom

Methods for Learning Control Policies from Variable Constraint Demonstrations

Many everyday human skills can be framed in terms of performing some task subject to constraints imposed by the task or the environment. Constraints are usually not observable and frequently change between contexts. In this chapter, we explore the problem of learning control policies from data containing variable, dynamic and non-linear constraints on motion. We discuss how an effective approach for doing this is to learn the unconstrained policy in a way that is consistent with the constraints. We then go on to discuss several recent algorithms for extracting policies from movement data, where observations are recorded under variable, unknown constraints. We review a number of experiments testing the performance of these algorithms and demonstrating how the resultant policy models generalise over constraints allowing prediction of behaviour under unseen settings where new constraints apply

A Novel Method for Learning Policies from Constrained Motion

Many everyday human skills can be framed in terms of performing some task subject to constraints imposed by the environment. Constraints are usually unobservable and frequently change between contexts. In this paper, we present a novel approach for learning (unconstrained) control policies from movement data, where observations come from movements under different constraints. As a key ingredient, we introduce a small but highly effective modification to the standard risk functional, allowing us to make a meaningful comparison between the estimated policy and constrained observations. We demonstrate our approach on systems of varying complexity, including kinematic data from the ASIMO humanoid robot with 27 degrees of freedom

Robust Constraint-consistent Learning

Many everyday human skills can be framed in terms of performing some task subject to constraints imposed by the environment. Constraints are usually unobservable and frequently change between contexts. In this paper, we present a novel approach for learning (unconstrained) control policies from movement data, where observations are recorded under different constraint settings. Our approach seamlessly integrates unconstrained and constrained observations by performing hybrid optimisation of two risk functionals. The first is a novel risk functional that makes a meaningful comparison between the estimated policy and constrained observations. The second is the standard risk, used to reduce the expected error under impoverished sets of constraints. We demonstrate our approach on systems of varying complexity, and illustrate its utility for transfer learning of a car washing task from human motion capture data

A Novel Method for Learning Policies from Variable Constraint Data

Many everyday human skills can be framed in terms of performing some task subject to constraints imposed by the environment. Constraints are usually unobservable and frequently change between contexts. In this paper, we present a novel approach for learning (unconstrained) control policies from movement data, where observations come from movements under different constraints. As a key ingredient, we introduce a small but highly effective modification to the standard risk functional, allowing us to make a meaningful comparison between the estimated policy and constrained observations. We demonstrate our approach on systems of varying complexity, including kinematic data from the ASIMO humanoid robot with 27 degrees of freedom, and present results for learning from human demonstration

Considerations of the Gain Spectrum

We propose the gain spectrum as an efficient means to understand the learning dynamics of MLP gradient based learning and to roughly estimate an upper bound of the necessary network complexity. The feasibility of the approach will be shown by a number of experiments. 1 Introduction The investigation of the learning process of artificial neural networks is an area that still receives a great deal of attention in the research community. Due to the nonlinearity of the governing equations and finite size effects there is still no such thing as a fully automated efficient learning algorithm, if there can be any at all. Recent theoretical results provide guide lines for the construction of learning algorithms and permit some insight into the nature of the adaptation mechanism, but even apparently simple on-line algorithms are far from being sufficiently well understood. Our goal is to contribute to the explanation of the learning process. The approach we pursue differs from some of the prev..