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Applying reinforcement learning to humanoid robots is challenging because humanoids have a large number of degrees of freedom and state and action spaces are continuous. Thus, most reinforcement learning algorithms would become computationally infeasible and require a prohibitive amount of trials to explore such high-dimensional spaces. In this paper, we present a probabilistic reinforcement learning...
In most activities of daily living, related tasks are encountered over and over again. This regularity allows humans and robots to reuse existing solutions for known recurring tasks. We expect that reusing a set of standard solutions to solve similar tasks will facilitate the design and on-line adaptation of the control systems of robots operating in human environments. In this paper, we derive a...
Analytic modeling, imitation, and experience-based learning are three approaches that enable robots to acquire models of their morphology and skills. In this paper, we combine these three approaches to efficiently gather training data to learn a model of reachability for a typical mobile manipulation task: approaching a worksurface in order to grasp an object. The core of the approach is experience-based...
In mobile manipulation, the position to which the robot navigates has a large influence on the ease with which a subsequent manipulation action can be performed. Whether a manipulation action succeeds depends on many factors, such as the robot's hardware configuration, the controllers the robot uses to achieve navigation and manipulation, the task context, and uncertainties in state estimation. In...
Autonomous personal robots are currently being equipped with hands and arms that have kinematic redundancy similar to those of humans. Humans exploit the redundancy in their motor system by optimizing secondary criteria. Tasks which are executed repeatedly lead to movements that are highly optimized over time, which leads to stereotypical and pre-planned motion patterns. This stereotypical motion...
What it means for an object to be dasiawithin reachpsila depends very much on the morphology and skills of a robot. In this paper, we enable a mobile manipulation robot to learn a concept of PLACE from which successful manipulation is possible through trial-and-error interaction with the environment. Due to this developmental approach, PLACE is very much grounded in observed experience, and takes...
Opportunities for interleaving or parallelizing actions are abundant in everyday activities. Being able to perceive, predict and exploit such opportunities leads to more efficient and robust behavior. In this paper, we present a mobile manipulation platform that exploits such opportunities to optimize its behavior, e.g. grasping two objects from one location simultaneously, rather than navigating...
Our system runs at 10 fps on a 2.0 GHz processor and an image resolution of 640times480 pixels. High quality objective functions that are learned from annotated example images ensure both an accurate and fast computation of the model parameters. Our demonstrator for facial expression estimation has been presented at several events with political audience and on TV. However, the approach of robust...
Due to their use of information contained in texture, active appearance models (AAM) generally outperform active shape models (ASM) in terms of fitting accuracy. Although many extensions and improvements over the original AAM have been proposed, on of the main drawbacks of AAMs remains its dependence on good initial model parameters to achieve accurate fitting results. In this paper, we determine...
This paper introduces the assistive kitchen as a comprehensive demonstration and challenge scenario for technical cognitive systems. We describe its hardware and software infrastructure. Within the assistive kitchen application, we select particular domain activities as research subjects and identify the cognitive capabilities needed for perceiving, interpreting, analyzing, and executing these activities...
Geometric models allow to determine semantic information about real-world objects. Model fitting algorithms need to find the best match between a parameterized model and a given image. This task inherently requires an objective function to estimate the error between a model parameterization and an image. The accuracy of this function directly influences the accuracy of the entire process of model...
Model-based techniques have proven to be successful in interpreting the large amount of information contained in images. Associated fitting algorithms search for the global optimum of an objective function, which should correspond to the best model fit in a given image. Although fitting algorithms have been the subject of intensive research and evaluation, the objective function is usually designed...
Model-based image interpretation extracts high-level information from images using a priori knowledge about the object of interest. The computational challenge in model fitting is to determine the model parameters that best match a given image, which corresponds to finding the global optimum of the objective function. When it comes to the robustness and accuracy of fitting models to specific images,...
One of the most notable and recognizable features of robot motion is the abrupt transitions between actions in action sequences. In contrast, humans and animals perform sequences of actions efficiently, and with seamless transitions between subsequent actions. This smoothness is not a goal in itself, but a side-effect of the evolutionary optimization of other performance measures. In this paper, we...
Many application tasks require the cooperation of two or more robots. Humans are good at cooperation in shared workspaces, because they anticipate and adapt to the intentions and actions of others. In contrast, multi-agent and multi-robot systems rely on communication to exchange their intentions. This causes problems in domains where perfect communication is not guaranteed, such as rescue robotics,...
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