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We introduce a framework for model learning and planning in stochastic domains with continuous state and action spaces and non-Gaussian transition models. It is efficient because (1) local models are estimated only when the planner requires them; (2) the planner focuses on the most relevant states to the current planning problem; and (3) the planner focuses on the most informative and/or high-value...
Robots that are operating for extended periods of time need to be able to deal with changes in their environment and represent them adequately in their maps. In this paper, we present a novel 3D reconstruction algorithm based on an extended Truncated Signed Distance Function (TSDF) that enables to continuously refine the static map while simultaneously obtaining 3D reconstructions of dynamic objects...
We present an approach to learning control policies for physical robots that achieves high efficiency by adjusting existing policies that have been learned on similar source systems, such as a similar robot with different physical parameters, or an approximate dynamics model simulator. This can be viewed as calibrating a policy learned on a source system, to match a desired behaviour in similar target...
This paper presents a semi-parametric algorithm for online learning of a robot inverse dynamics model. It combines the strength of the parametric and non-parametric modeling. The former exploits the rigid body dynamics equation, while the latter exploits a suitable kernel function. We provide an extensive comparison with other methods from the literature using real data from the iCub humanoid robot...
One of the key challenges in applying reinforcement learning to complex robotic control tasks is the need to gather large amounts of experience in order to find an effective policy for the task at hand. Model-based reinforcement learning can achieve good sample efficiency, but requires the ability to learn a model of the dynamics that is good enough to learn an effective policy. In this work, we develop...
An important problem in human-robot interaction is for a human to be able to tell the robot go to a particular location with instructions on how to get there or what to avoid on the way. This paper provides a solution to problems where the human wants the robot not only to optimize some objective but also to honor “soft” or “hard” topological constraints, i.e. “go quickly from A to B while avoiding...
We explore a temporal decomposition of dynamics in order to enhance policy learning with unknown dynamics. There are model-free methods and model-based methods for policy learning with unknown dynamics, but both approaches have problems: in general, model-free methods have less generalization ability, while model-based methods are often limited by the assumed model structure or need to gather many...
Independent Joint Learning (IJL) was recently introduced as a learning-based approach to account for inverse dynamics (ID) model errors. The fundamental idea is to combine an ID model with learned torque error estimators that only rely on joint-local information. This approach improves task-to-task generalization and reduces learning times as each torque error estimators depends only on the state...
This paper introduces a new planning algorithm to minimize the damage of humanoid falls by utilizing multiple contact points. Given an unstable initial state of the robot, our approach plans for the optimal sequence of contact points such that the initial momentum is dissipated with minimal impacts on the robot. Instead of switching among a collection of individual control strategies, we propose a...
In this paper, we develop an receding horizon control (RHC) law for controlling the pursuers in a pursuit-evasion problem arising in a harbor defense scenario and describe its real-time implementation that we apply experimentally to a robotic testbed. Our implementation of the RHC law makes use of a min-max formulation of the underlying optimal problem that must be solved at each sample time, which...
This paper deals with the generation of motion for complex dynamical systems (such as humanoid robots) to achieve several concurrent objectives. Hierarchy of tasks and optimal control are two frameworks commonly used to this aim. The first one specifies control objectives as a number of quadratic functions to be minimized under strict priorities. The second one minimizes an arbitrary user-defined...
Personal robots need to manipulate a variety of articulated mechanisms as part of day-to-day tasks. These tasks are often specific, goal-driven, and permit very little bootstrap time for learning the articulation type. In this work, we address the problem of purposefully manipulating an articulated object, with uncertainty in the type of articulation. To this end, we provide two primary contributions:...
In this work we present a fast kinodynamic RRT-planner that uses dynamic nonprehensile actions to rearrange cluttered environments. In contrast to many previous works, the presented planner is not restricted to quasi-static interactions and monotonicity. Instead the results of dynamic robot actions are predicted using a black box physics model. Given a general set of primitive actions and a physics...
In many complex robot applications, such as grasping and manipulation, it is difficult to program desired task solutions beforehand, as robots are within an uncertain and dynamic environment. In such cases, learning tasks from experience can be a useful alternative. To obtain a sound learning and generalization performance, machine learning, especially, reinforcement learning, usually requires sufficient...
This paper presents the dynamic modeling of floating systems with application for three-dimensional swimming eel-like robot and rowing-like system. To obtain the Cartesian evolution during the design or control of these systems the dynamic models must be used. Owing to the complexity of such systems efficient and simple tools are needed to obtain their model. For this goal we propose an efficient...
We propose in this paper a general analytic scheme based on Gauss principle of least constraint for the derivation of the Lagrangian dynamics equation of motion of arbitrarily parameterized free-floating-base articulated mechanisms. The free-floating base of the mechanism is a non-actuated rigid object evolving in the 6D Lie group SE(3), the SO(3) component of which can be parameterized using arbitrary...
The ability to act in a socially-aware way is a key skill for robots that share a space with humans. In this paper we address the problem of socially-aware navigation among people that meets objective criteria such as travel time or path length as well as subjective criteria such as social comfort. Opposed to model-based approaches typically taken in related work, we pose the problem as an unsupervised...
We present an online trajectory optimization method and software platform applicable to complex humanoid robots performing challenging tasks such as getting up from an arbitrary pose on the ground and recovering from large disturbances using dexterous acrobatic maneuvers. The resulting behaviors, illustrated in the attached video, are computed only 7 × slower than real time, on a standard PC. The...
In this paper, we present a novel path planning algorithm based on properties that reaction-diffusion (RD) models exhibit by the underlying non-linear dynamics of the considered system. In particular herein considered a two-variable RD model provides advantages of natural parallelism, noise resistance, and especially the non-annihilating feature that traveling fronts separating two stable states exhibit...
In this paper, we propose an Artificial Bee Colony (ABC) algorithm, a swarm-based artificial intelligence algorithm, for computing a connected dominating set (CDS) in wireless networks. ABC Algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. Wireless ad hoc networks appear in a wide variety of applications. In this work ABC algorithm is used for optimizing...
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