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A user interface for the remote control of vision-based robot navigation in previously unknown, indoor or outdoor environments has been developed. Visual feedback from the camera(s) of the robot is provided to the user, allowing him to select a visual target to be reached by the robot and launch an autonomous navigation process. Manual control can be taken back by the user at any time. This work was supported by the project 'Sistema reconfigurable para la navegación basada en visión de robots caminantes y rodantes en entornos naturales.' (00). International Workshop on Telerobotics and Augmented Reality for Teleoperation, 2005, Madrid (España) Peer Reviewed

Thesis (M.A.)--Özyeğin University, Graduate School of Sciences and Engineering, Department of Computer Science, May 2017. In robotics, flexible and the dexterous manipulation are one of the most desired type of skills. To this end, we investigate dexterous manipulation skills on an anthropomorphic robot hand. In the first part of the study, a sensorless grasping method is described. Although the high-precision sensing is highly relevant for precise grasps, precision is often not necessary to perform power grasps. An alternative approach is proposed for robotic grasping tasks based on external force estimation. Estimation accuracy is confirmed using a force sensor and the estimations are found to be useful for creating soft/power grasp behavior. In the second part, human-in-the-loop heterogeneous control for dexterous manipulation is investigated on a setup with a robotic hand and a robotic arm. The goal of the study is to experimentally verify that in tasks where the manual and explicit trajectory tuning is not possible, the autonomous movement can be learned by giving a basic policy to a robotic system, after which a human can learn and transfer an orthogonal complex part of the policy. The approach is shown on a ball swapping task in which a robotic arm is controlled by the human and a robotic hand is given an initial basic policy. In the results, we experimentally show that, in certain tasks, complex autonomous policies can be constructed by delegating the complex learning part to a human, the simple part to an autonomous agent, finally creating an autonomous control policy by recombining the parts. İnsan eli obje kavramadan hassas manipulasyona oldukça farklı fonksiyonları yerine getirebilmektedir. Bu işlemler gelişmiş anatomik el yapısının yanısıra motor korteksin büyük bir kısmını kullanan kontrol mekanizmasını da kullanmaktadır. Robotik bağlamında, esnek ve çevik manipülasyon en çok ihtiyaç duyulan becerilerdendeir. Basit manipülasyon görevleri kinematiği karmaşık olmayan tutucularla yapılabilmesine rağmen, kısıtlamalar yaygın olarak bilinmektedir. Bu sebeple, bu tezde, insansı bir robot el üzerinde çevik manipülasyon becerileri araştırılmıştır. Bu tezde birbiriyle alakalı iki manipülasyon becerisi araştırılmıştır. Çalışmanın ilk kısmında, sensörsüz bir kavrama yöntemi açıklanmıştır. İnsan elindeki yüksek hassasiyete sahip sensör geribeslemesi hassas kavrama için gerekli olsa da, güçlü kavrama için yüksek hassasiyet çoğu zaman ihtiyaç duyulmamaktadır. Karmaşık sensör kurulumlarına alternatif olarak, robotik kavrama için elin dinamik modeline ve dışsal kuvvet tahminine dayalı bir sensörsüz kavrama yöntemi geliştirilmiştir. Tahmin doğruluğu bir kuvvet sensörü kullanılarak onaylanmıştır ve önerilen yöntemle yumuşak veya sıkı kavrama yeteneklerinin gerçekleştirilebildiği görülmüştür. İkinci kısımda, çevik manipülasyon için heterojen insanlı-döngü kontrolü, robot kol ve elden oluşan bir sistem üzerinde incelenmiştir. Çalışmanın amacı, elle veya otomatik yörünge ayarlamanın mümkün olmadığı durumlarda, robota temel bir idare vererek idarenin karmaşık kısmının insandan elde edilebileceğini ve görev için gerekli otonom idarenin bulunabileceğini olarak göstermektir. İnsanlar zor görevleri düşük hızda öğrenebileceği ve öğrenimden sonra diğer tekniklerle idare geliştirilebildiği için bu yaklaşım çevik manipülasyon görevleri için özellikle uygundur. Yöntem, örnek olarak bir robot kolun insan tarafından kullanıldığı ve bir robot elin otonom çalışarak gerçekleştirdiği avuç-içi top çevirme görevinde gösterilmiştir. Sonuçlarda, karmaşık otonom idarelerin, karmaşık kısımlarını öğrenmeyi insana delege ederek, insan öğreniminden sonra otonom control idaresinin elde edilebilecği gösterilmiştir.

In this paper the statics and the instantaneous kinematics of serial and parallel robot manipulators are studied. A projective interpretation of the concepts of twist, wrench, twist space and wrench space -based on the concept of extensor- is presented and a description of the dualistic relation between twist and wrench spaces of serial and parallel robot manipulators is given in terms of the Grassmann-Cayley algebra. The importance of this algebra is that its joint and meet operators are very effective tools for joining and intersecting linear subspaces involved in the kinestatic analysis of manipulators when they are represented by extensors. International Symposium on Advances in Robot Kinematics (ARK), 2000, Piran (Eslovenia) Peer Reviewed

Proposes a reinforcement learning architecture that allows an autonomous robot to acquire efficient navigation strategies in a few trials. Besides fast learning, the architecture has 3 further appealing features. (1) Since it learns from built-in reflexes, the robot is operational from the very beginning. (2) The robot improves its performance incrementally as it interacts with an initially unknown environment, and it ends up learning to avoid collisions even if its sensors cannot detect the obstacles. This is a definite advantage over non-learning reactive robots. (3) The robot exhibits high tolerance to noisy sensory data and good generalization abilities. All these features make this learning robot's architecture very well suited to real-world applications. The authors report experimental results obtained with a real mobile robot in an indoor environment that demonstrate the feasibility of this approach. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 1994, Múnich (Alemania) Peer Reviewed

Social robots are receiving much interest in the robotics community. The most important goal for such robots lies in their interaction capabilities. An attention system is crucial, both as a filter to center the robot’s perceptual resources and as a mean of letting the observer know that the robot has intentionality. In this paper a simple but flexible and functional attentional model is described. The model, which has been implemented in an interactive robot currently under development, fuses both visual and auditive information extracted from the robot’s environment, and can incorporate knowledge-based influences on attention.