A multi-level control architecture for the bionic handling assistant

An imitation learning approach for the control of a low-cost low-accuracy robotic arm for unstructured environments

We have developed an imitation learning approach for the image-based control of a low-cost low-accuracy robot arm. The image-based control of manipulation arms is still an unsolved problem, at least under challenging conditions such as those here addressed. Many attempts for solutions in the literature are based on machine learning, generally relying on deep neural network architectures. In typical imitation approaches, the deep network learns from a human expert. In our case the network is trained on state/action pairs obtained through a Belief Space Planning algorithm, a stochastic method that requires only a rough tuning, particularly suited to unstructured and dynamic environments. Our approach allows to obtain a lightweight manipulation system that demonstrated its efficiency, robustness and good performance in real-world tests, and that is reproducible in experiments and results, despite its inaccuracy and non-repeatable kinematics. The proposed system performs well on a simple reaching task, requiring limited training on our quite challenging platform. The main contribution of the proposed work lies in the definition and real-world testing of an efficient controller, based on the integration of Belief Space Planning with the imitation learning paradigm, that enables even inaccurate, very low-cost robotic manipulators to be actually controlled and employed in the field.

Snake Robots for Surgical Applications: A Review

Although substantial advancements have been achieved in robot-assisted surgery, the blueprint to existing snake robotics predominantly focuses on the preliminary structural design, control, and human–robot interfaces, with features which have not been particularly explored in the literature. This paper aims to conduct a review of planning and operation concepts of hyper-redundant serpentine robots for surgical use, as well as any future challenges and solutions for better manipulation. Current researchers in the field of the manufacture and navigation of snake robots have faced issues, such as a low dexterity of the end-effectors around delicate organs, state estimation and the lack of depth perception on two-dimensional screens. A wide range of robots have been analysed, such as the i²Snake robot, inspiring the use of force and position feedback, visual servoing and augmented reality (AR). We present the types of actuation methods, robot kinematics, dynamics, sensing, and prospects of AR integration in snake robots, whilst addressing their shortcomings to facilitate the surgeon’s task. For a smoother gait control, validation and optimization algorithms such as deep learning databases are examined to mitigate redundancy in module linkage backlash and accidental self-collision. In essence, we aim to provide an outlook on robot configurations during motion by enhancing their material compositions within anatomical biocompatibility standards.

Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control

Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics. However, mechanical and analytical models do not capture all aspects of a plant’s intrinsic properties and there remain unmodeled dynamics due to varying parameters, unmodeled friction or soft materials. In this context, machine learning is an alternative suitable technique to extract non-linear plant models from data. However, fully data-based models suffer from inaccuracies as well and are inefficient if they include learning of well known analytical models. This paper thus argues that feed-forward control based on hybrid models comprising an analytical model and a learned error model can significantly improve modeling accuracy. Hybrid modeling here serves the purpose to combine the best of the two modeling worlds. The hybrid modeling methodology is described and the approach is demonstrated for two typical problems in robotics, i.e., inverse kinematics control and computed torque control. The former is performed for a redundant soft robot and the latter for a rigid industrial robot with redundant degrees of freedom, where a complete analytical model is not available for any of the platforms.