Recent Advances in Robot Learning from Demonstration
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We present a system for robust robot skill acquisition from kinesthetic demonstrations. This system allows a robot to learn a simple goal-directed gesture, and correctly reproduce it despite changes in the initial conditions, and perturbations in the envir ...
Multi-robot systems have been studied in tasks that require the robots to be physically linked. In such a configuration, a group of robots may navigate a terrain that proves too difficult for a single robot. On the contrary, many collective tasks can be ac ...
Robot programming by demonstration (RPD) covers methods by which a robot learns new skills through human guidance. We present an interactive, multimodal RPD framework using active teaching methods that places the human teacher in the robot's learning loop. ...
In the high-precision industry, most operations require the use of robots able to accomplish highly accurate and repeatable motions. In order to meet the desired level of absolute accuracy, one has to limit or even suppress the effects of different sources ...
The consequence of the loss of involutivity of a specific set of vector fields on the periodicity of the joint motion is examined for redundant robots. An output task, defined as a one dimensional periodic closed curve embedded in a two dimensional working ...
This paper presents experiences from Robotics, a long-term project at the Swiss National Exposition Expo.02, where mobile robots served as tour guides. It includes a description of the design and implementation of the robot and addresses reliability and sa ...
Robot positioning is an important function of autonomous intelligent robots. However, the application of external forces to a robot can disrupt its normal operation and cause localisation errors. We present a novel approach for detecting external disturban ...
The field of adaptive robotics involves simulations and real-world implementations of robots that adapt to their environments. In this article, I introduce adaptive environmentics -- the flip side of adaptive robotics -- in which the environment adapts to ...
Robot learning by imitation makes an increasing body of robotics research. Imitation learning complements motor learning techniques by restricting the search space to a computationally tractable subset. Imitation learning search for spatial and temporal in ...
Navigation in unknown or partially unknown environments remains one of the biggest challenges in today's mobile robotics. Environmental modeling, perception, localization and mapping are all needed for a successful approach. The contribution of this paper ...
