Robotic object manipulation via hierarchical and affordance learning

With the rise of computation power and machine learning techniques, a shift of research interest is happening to roboticists. Against this background, this thesis seeks to develop or enhance learning-based grasping and manipulation systems. This thesis first proposes a method, named A2, to improve the sample efficiency of end-to-end deep reinforcement learning algorithms for long horizon, multi-step and sparse reward manipulation. The named A2 comes from the fact that it uses Abstract demonstrations to guide the learning process and Adaptively adjusts exploration according to online performances. Experiments in a series of multi-step grid world tasks and manipulation tasks demonstrate significant performance gains over baselines. Then, this thesis develops a hierarchical reinforcement learning approach towards solving the long-horizon manipulation tasks. Specifically, the proposed universal option framework integrates the knowledge-sharing advantage of goal-conditioned reinforcement learning into hierarchical reinforcement learning. An analysis of the parallel training non-stationarity problem is also conducted, and the A2 method is employed to address the issue. Experiments in a series of continuous multi-step, multi-outcome block stacking tasks demonstrate significant performance gains as well as reductions of memory and repeated computation over baselines. Finally, this thesis studies the interplay between grasp generation and manipulation motion generation, arguing that selecting a good grasp before manipulation is essential for contact-rich manipulation tasks. A theory of general affordances based on the reinforcement learning paradigm is developed and used to represent the relationship between grasp generation and manipulation performances. This leads to the general affordance-aware manipulation framework, which selects task-agnostic grasps for downstream manipulation based on the predicted manipulation performances. Experiments on a series of contact-rich hook separation tasks prove the effectiveness of the proposed framework and showcase significant performance gains by filtering away unsatisfactory grasps

Visual Neuroscience of Robotic Grasping

Supporting Informatio

Trust in Robots

Robots are increasingly becoming prevalent in our daily lives within our living or working spaces. We hope that robots will take up tedious, mundane or dirty chores and make our lives more comfortable, easy and enjoyable by providing companionship and care. However, robots may pose a threat to human privacy, safety and autonomy; therefore, it is necessary to have constant control over the developing technology to ensure the benevolent intentions and safety of autonomous systems. Building trust in (autonomous) robotic systems is thus necessary. The title of this book highlights this challenge: “Trust in robots—Trusting robots”. Herein, various notions and research areas associated with robots are unified. The theme “Trust in robots” addresses the development of technology that is trustworthy for users; “Trusting robots” focuses on building a trusting relationship with robots, furthering previous research. These themes and topics are at the core of the PhD program “Trust Robots” at TU Wien, Austria