542 research outputs found
A Comparative Study of Twin Delayed Deep Deterministic Policy Gradient and Soft Actor-Critic Algorithms for Robot Exploration and Navigation in Unseen Environments
This paper presents a comparison between twin-
delayed Deep Deterministic Policy Gradient (TD3) and Soft
Actor-Critic (SAC) reinforcement learning algorithms in the
context of training robust navigation policies for Jackal robots. By
leveraging an open-source framework and custom motion control
environments, the study evaluates the performance, robustness,
and transferability of the trained policies across a range of
scenarios. The primary focus of the experiments is to assess
the training process, the adaptability of the algorithms, and the
robot’s ability to navigate in previously unseen environments.
Moreover, the paper examines the influence of varying environ-
ment complexities on the learning process and the generalization
capabilities of the resulting policies. The results of this study
aim to inform and guide the development of more efficient and
practical reinforcement learning-based navigation policies for
Jackal robots in real-world scenarios
Anatomically Constrained Video-CT Registration via the V-IMLOP Algorithm
Functional endoscopic sinus surgery (FESS) is a surgical procedure used to
treat acute cases of sinusitis and other sinus diseases. FESS is fast becoming
the preferred choice of treatment due to its minimally invasive nature.
However, due to the limited field of view of the endoscope, surgeons rely on
navigation systems to guide them within the nasal cavity. State of the art
navigation systems report registration accuracy of over 1mm, which is large
compared to the size of the nasal airways. We present an anatomically
constrained video-CT registration algorithm that incorporates multiple video
features. Our algorithm is robust in the presence of outliers. We also test our
algorithm on simulated and in-vivo data, and test its accuracy against
degrading initializations.Comment: 8 pages, 4 figures, MICCA
An Open-Source Simulator for Cognitive Robotics Research: The Prototype of the iCub Humanoid Robot Simulator
This paper presents the prototype of a new computer simulator for the humanoid robot iCub. The iCub is a new open-source humanoid robot developed as a result of the “RobotCub” project, a collaborative European project aiming at developing a new open-source cognitive robotics platform. The iCub simulator has been developed as part of a joint effort with the European project “ITALK” on the integration and transfer of action and language knowledge in cognitive robots. This is available open-source to all researchers interested in cognitive robotics experiments with the iCub humanoid platform
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