Proceedings of the NASA Conference on Space Telerobotics, volume 5

Papers presented at the NASA Conference on Space Telerobotics are compiled. The theme of the conference was man-machine collaboration in space. The conference provided a forum for researchers and engineers to exchange ideas on the research and development required for the application of telerobotics technology to the space systems planned for the 1990's and beyond. Volume 5 contains papers related to the following subject areas: robot arm modeling and control, special topics in telerobotics, telerobotic space operations, manipulator control, flight experiment concepts, manipulator coordination, issues in artificial intelligence systems, and research activities at the Johnson Space Center

A review on reinforcement learning for contact-rich robotic manipulation tasks

Research and application of reinforcement learning in robotics for contact-rich manipulation tasks have exploded in recent years. Its ability to cope with unstructured environments and accomplish hard-to-engineer behaviors has led reinforcement learning agents to be increasingly applied in real-life scenarios. However, there is still a long way ahead for reinforcement learning to become a core element in industrial applications. This paper examines the landscape of reinforcement learning and reviews advances in its application in contact-rich tasks from 2017 to the present. The analysis investigates the main research for the most commonly selected tasks for testing reinforcement learning algorithms in both rigid and deformable object manipulation. Additionally, the trends around reinforcement learning associated with serial manipulators are explored as well as the various technological challenges that this machine learning control technique currently presents. Lastly, based on the state-of-the-art and the commonalities among the studies, a framework relating the main concepts of reinforcement learning in contact-rich manipulation tasks is proposed. The final goal of this review is to support the robotics community in future development of systems commanded by reinforcement learning, discuss the main challenges of this technology and suggest future research directions in the domain

Structured machine learning models for robustness against different factors of variability in robot control

An important feature of human sensorimotor skill is our ability to learn to reuse them across different environmental contexts, in part due to our understanding of attributes of variability in these environments. This thesis explores how the structure of models used within learning for robot control could similarly help autonomous robots cope with variability, hence achieving skill generalisation. The overarching approach is to develop modular architectures that judiciously combine different forms of inductive bias for learning. In particular, we consider how models and policies should be structured in order to achieve robust behaviour in the face of different factors of variation - in the environment, in objects and in other internal parameters of a policy - with the end goal of more robust, accurate and data-efficient skill acquisition and adaptation. At a high level, variability in skill is determined by variations in constraints presented by the external environment, and in task-specific perturbations that affect the specification of optimal action. A typical example of environmental perturbation would be variation in lighting and illumination, affecting the noise characteristics of perception. An example of task perturbations would be variation in object geometry, mass or friction, and in the specification of costs associated with speed or smoothness of execution. We counteract these factors of variation by exploring three forms of structuring: utilising separate data sets curated according to the relevant factor of variation, building neural network models that incorporate this factorisation into the very structure of the networks, and learning structured loss functions. The thesis is comprised of four projects exploring this theme within robotics planning and prediction tasks. Firstly, in the setting of trajectory prediction in crowded scenes, we explore a modular architecture for learning static and dynamic environmental structure. We show that factorising the prediction problem from the individual representations allows for robust and label efficient forward modelling, and relaxes the need for full model re-training in new environments. This modularity explicitly allows for a more flexible and interpretable adaptation of trajectory prediction models to using pre-trained state of the art models. We show that this results in more efficient motion prediction and allows for performance comparable to the state-of-the-art supervised 2D trajectory prediction. Next, in the domain of contact-rich robotic manipulation, we consider a modular architecture that combines model-free learning from demonstration, in particular dynamic movement primitives (DMP), with modern model-free reinforcement learning (RL), using both on-policy and off-policy approaches. We show that factorising the skill learning problem to skill acquisition and error correction through policy adaptation strategies such as residual learning can help improve the overall performance of policies in the context of contact-rich manipulation. Our empirical evaluation demonstrates how to best do this with DMPs and propose “residual Learning from Demonstration“ (rLfD), a framework that combines DMPs with RL to learn a residual correction policy. Our evaluations, performed both in simulation and on a physical system, suggest that applying residual learning directly in task space and operating on the full pose of the robot can significantly improve the overall performance of DMPs. We show that rLfD offers a gentle to the joints solution that improves the task success and generalisation of DMPs. Last but not least, our study shows that the extracted correction policies can be transferred to different geometries and frictions through few-shot task adaptation. Third, we employ meta learning to learn time-invariant reward functions, wherein both the objectives of a task (i.e., the reward functions) and the policy for performing that task optimally are learnt simultaneously. We propose a novel inverse reinforcement learning (IRL) formulation that allows us to 1) vary the length of execution by learning time-invariant costs, and 2) relax the temporal alignment requirements for learning from demonstration. We apply our method to two different types of cost formulations and evaluate their performance in the context of learning reward functions for simulated placement and peg in hole tasks executed on a 7DoF Kuka IIWA arm. Our results show that our approach enables learning temporally invariant rewards from misaligned demonstration that can also generalise spatially to out of distribution tasks. Finally, we employ our observations to evaluate adversarial robustness in the context of transfer learning from a source trained on CIFAR 100 to a target network trained on CIFAR 10. Specifically, we study the effects of using robust optimisation in the source and target networks. This allows us to identify transfer learning strategies under which adversarial defences are successfully retained, in addition to revealing potential vulnerabilities. We study the extent to which adversarially robust features can preserve their defence properties against black and white-box attacks under three different transfer learning strategies. Our empirical evaluations give insights on how well adversarial robustness under transfer learning can generalise.

A Posture Sequence Learning System for an Anthropomorphic Robotic Hand

The paper presents a cognitive architecture for posture learning of an anthropomorphic robotic hand. Our approach is aimed to allow the robotic system to perform complex perceptual operations, to interact with a human user and to integrate the perceptions by a cognitive representation of the scene and the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces and to perform complex interaction with the human operator

Proceedings of the NASA Conference on Space Telerobotics, volume 4

Papers presented at the NASA Conference on Space Telerobotics are compiled. The theme of the conference was man-machine collaboration in space. The conference provided a forum for researchers and engineers to exchange ideas on the research and development required for the application of telerobotic technology to the space systems planned for the 1990's and beyond. Volume 4 contains papers related to the following subject areas: manipulator control; telemanipulation; flight experiments (systems and simulators); sensor-based planning; robot kinematics, dynamics, and control; robot task planning and assembly; and research activities at the NASA Langley Research Center

Proceedings of the NASA Conference on Space Telerobotics, volume 2

These proceedings contain papers presented at the NASA Conference on Space Telerobotics held in Pasadena, January 31 to February 2, 1989. The theme of the Conference was man-machine collaboration in space. The Conference provided a forum for researchers and engineers to exchange ideas on the research and development required for application of telerobotics technology to the space systems planned for the 1990s and beyond. The Conference: (1) provided a view of current NASA telerobotic research and development; (2) stimulated technical exchange on man-machine systems, manipulator control, machine sensing, machine intelligence, concurrent computation, and system architectures; and (3) identified important unsolved problems of current interest which can be dealt with by future research

Smart Technologies for Precision Assembly

This open access book constitutes the refereed post-conference proceedings of the 9th IFIP WG 5.5 International Precision Assembly Seminar, IPAS 2020, held virtually in December 2020. The 16 revised full papers and 10 revised short papers presented together with 1 keynote paper were carefully reviewed and selected from numerous submissions. The papers address topics such as assembly design and planning; assembly operations; assembly cells and systems; human centred assembly; and assistance methods in assembly

Robot learning from demonstration of force-based manipulation tasks

One of the main challenges in Robotics is to develop robots that can interact with humans in a natural way, sharing the same dynamic and unstructured environments. Such an interaction may be aimed at assisting, helping or collaborating with a human user. To achieve this, the robot must be endowed with a cognitive system that allows it not only to learn new skills from its human partner, but also to refine or improve those already learned. In this context, learning from demonstration appears as a natural and userfriendly way to transfer knowledge from humans to robots. This dissertation addresses such a topic and its application to an unexplored field, namely force-based manipulation tasks learning. In this kind of scenarios, force signals can convey data about the stiffness of a given object, the inertial components acting on a tool, a desired force profile to be reached, etc. Therefore, if the user wants the robot to learn a manipulation skill successfully, it is essential that its cognitive system is able to deal with force perceptions. The first issue this thesis tackles is to extract the input information that is relevant for learning the task at hand, which is also known as the what to imitate? problem. Here, the proposed solution takes into consideration that the robot actions are a function of sensory signals, in other words the importance of each perception is assessed through its correlation with the robot movements. A Mutual Information analysis is used for selecting the most relevant inputs according to their influence on the output space. In this way, the robot can gather all the information coming from its sensory system, and the perception selection module proposed here automatically chooses the data the robot needs to learn a given task. Having selected the relevant input information for the task, it is necessary to represent the human demonstrations in a compact way, encoding the relevant characteristics of the data, for instance, sequential information, uncertainty, constraints, etc. This issue is the next problem addressed in this thesis. Here, a probabilistic learning framework based on hidden Markov models and Gaussian mixture regression is proposed for learning force-based manipulation skills. The outstanding features of such a framework are: (i) it is able to deal with the noise and uncertainty of force signals because of its probabilistic formulation, (ii) it exploits the sequential information embedded in the model for managing perceptual aliasing and time discrepancies, and (iii) it takes advantage of task variables to encode those force-based skills where the robot actions are modulated by an external parameter. Therefore, the resulting learning structure is able to robustly encode and reproduce different manipulation tasks. After, this thesis goes a step forward by proposing a novel whole framework for learning impedance-based behaviors from demonstrations. The key aspects here are that this new structure merges vision and force information for encoding the data compactly, and it allows the robot to have different behaviors by shaping its compliance level over the course of the task. This is achieved by a parametric probabilistic model, whose Gaussian components are the basis of a statistical dynamical system that governs the robot motion. From the force perceptions, the stiffness of the springs composing such a system are estimated, allowing the robot to shape its compliance. This approach permits to extend the learning paradigm to other fields different from the common trajectory following. The proposed frameworks are tested in three scenarios, namely, (a) the ball-in-box task, (b) drink pouring, and (c) a collaborative assembly, where the experimental results evidence the importance of using force perceptions as well as the usefulness and strengths of the methods