Humans can autonomously learn the relationship between sensation and motion in their own bodies, estimate and control their own body states, and move while continuously adapting to the current environment. On the other hand, current robots control their bodies by learning the network structure described by humans from their experiences, making certain assumptions on the … [Read more...] about GeMuCo: Generalized Multisensory Correlational Model for Body Schema Learning
State estimation
Learning Fast and Precise Pixel-to-Torque Control: A Platform for Reproducible Research of Learning on Hardware
In the field, robots often need to operate in unknown and unstructured environments, where accurate sensing and state estimation (SE) become a major challenge. Cameras have been used with great success in mapping and planning in such environments [1] as well as complex but quasi-static tasks, such as grasping [2], but are rarely integrated into the control loop for unstable … [Read more...] about Learning Fast and Precise Pixel-to-Torque Control: A Platform for Reproducible Research of Learning on Hardware
