A Continuous Grasp Representation for the Imitation Learning of Grasps on Humanoid Robots

Models and methods are presented which enable a humanoid robot to learn reusable, adaptive grasping skills. Mechanisms and principles in human grasp behavior are studied. The findings are used to develop a grasp representation capable of retaining specific motion characteristics and of adapting to different objects and tasks. Based on the representation a framework is proposed which enables the robot to observe human grasping, learn grasp representations, and infer executable grasping actions

Generative and predictive models for robust manipulation

Probabilistic modelling of manipulation skills, perception and uncertainty pose many challenges at different stages of a typical robot manipulation pipeline. This thesis is about devising algorithms and strategies for improving robustness in object manipulation skills acquired from demonstration and derived from learnt physical models in non-prehensile tasks such as pushing. Manipulation skills can be made robust in different ways: first by improving time performance for grasp synthesis, second by employing active perceptual strategies that exploit generated grasp action hypothesis to more efficiently gather task-relevant information for grasp generation, and finally via exploiting predictive uncertainty in learnt physical models. Hence, robust manipulation skills emerge from the interplay of a triad of capabilities: generative modelling for action synthesis, active perception, and finally learning and exploiting uncertainty in physical interactions. This thesis addresses these problems by • Showing how parametric models for approximating multimodal distributions can be used as a computationally faster method for generative grasp synthesis. • Exploiting generative methods for dexterous grasp synthesis and investigating how active vision strategies can be applied to improve grasp execution safety, success rate, and utilise fewer camera views of an object for grasp generation. • Outlining methods to model and exploit predictive uncertainty from learnt forward models to achieve robust, uncertainty-averse non-prehensile manipulation, such as push manipulation. In particular, the thesis: (i) presents a framework for generative grasp synthesis with applications for real-time grasp synthesis suitable for multi-fingered robot hands; (ii) describes a sensorisation method for under-actuated hands, such as the Pisa/IIT SoftHand, which allows us to deploy the aforementioned grasp synthesis framework to this type of robotic hand; (iii) provides an active vision approach for view selection that makes use of generative grasp synthesis methods to perform perceptual predictions in order to leverage grasp performance, taking into account grasp execution safety and contact information; and (iv) finally, going beyond prehensile skills, provides an approach to model and exploit predictive uncertainty from learnt physics applied to push manipulation. Experimental results are presented in simulation and on real robot platforms to validate the proposed methods

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field