Deep Visual Foresight for Planning Robot Motion

A key challenge in scaling up robot learning to many skills and environments is removing the need for human supervision, so that robots can collect their own data and improve their own performance without being limited by the cost of requesting human feedback. Model-based reinforcement learning holds the promise of enabling an agent to learn to predict the effects of its actions, which could provide flexible predictive models for a wide range of tasks and environments, without detailed human supervision. We develop a method for combining deep action-conditioned video prediction models with model-predictive control that uses entirely unlabeled training data. Our approach does not require a calibrated camera, an instrumented training set-up, nor precise sensing and actuation. Our results show that our method enables a real robot to perform nonprehensile manipulation -- pushing objects -- and can handle novel objects not seen during training.Comment: ICRA 2017. Supplementary video: https://sites.google.com/site/robotforesight

Adaptive physical human-robot interaction (PHRI) with a robotic nursing assistant.

Recently, more and more robots are being investigated for future applications in health-care. For instance, in nursing assistance, seamless Human-Robot Interaction (HRI) is very important for sharing workspaces and workloads between medical staff, patients, and robots. In this thesis we introduce a novel robot - the Adaptive Robot Nursing Assistant (ARNA) and its underlying components. ARNA has been designed specifically to assist nurses with day-to-day tasks such as walking patients, pick-and-place item retrieval, and routine patient health monitoring. An adaptive HRI in nursing applications creates a positive user experience, increase nurse productivity and task completion rates, as reported by experimentation with human subjects. ARNA has been designed to include interface devices such as tablets, force sensors, pressure-sensitive robot skins, LIDAR and RGBD camera. These interfaces are combined with adaptive controllers and estimators within a proposed framework that contains multiple innovations. A research study was conducted on methods of deploying an ideal HumanMachine Interface (HMI), in this case a tablet-based interface. Initial study points to the fact that a traded control level of autonomy is ideal for tele-operating ARNA by a patient. The proposed method of using the HMI devices makes the performance of a robot similar for both skilled and un-skilled workers. A neuro-adaptive controller (NAC), which contains several neural-networks to estimate and compensate for system non-linearities, was implemented on the ARNA robot. By linearizing the system, a cross-over usability condition is met through which humans find it more intuitive to learn to use the robot in any location of its workspace, A novel Base-Sensor Assisted Physical Interaction (BAPI) controller is introduced in this thesis, which utilizes a force-torque sensor at the base of the ARNA robot manipulator to detect full body collisions, and make interaction safer. Finally, a human-intent estimator (HIE) is proposed to estimate human intent while the robot and user are physically collaborating during certain tasks such as adaptive walking. A NAC with HIE module was validated on a PR2 robot through user studies. Its implementation on the ARNA robot platform can be easily accomplished as the controller is model-free and can learn robot dynamics online. A new framework, Directive Observer and Lead Assistant (DOLA), is proposed for ARNA which enables the user to interact with the robot in two modes: physically, by direct push-guiding, and remotely, through a tablet interface. In both cases, the human is being “observed” by the robot, then guided and/or advised during interaction. If the user has trouble completing the given tasks, the robot adapts their repertoire to lead users toward completing goals. The proposed framework incorporates interface devices as well as adaptive control systems in order to facilitate a higher performance interaction between the user and the robot than was previously possible. The ARNA robot was deployed and tested in a hospital environment at the School of Nursing of the University of Louisville. The user-experience tests were conducted with the help of healthcare professionals where several metrics including completion time, rate and level of user satisfaction were collected to shed light on the performance of various components of the proposed framework. The results indicate an overall positive response towards the use of such assistive robot in the healthcare environment. The analysis of these gathered data is included in this document. To summarize, this research study makes the following contributions: Conducting user experience studies with the ARNA robot in patient sitter and walker scenarios to evaluate both physical and non-physical human-machine interfaces. Evaluation and Validation of Human Intent Estimator (HIE) and Neuro-Adaptive Controller (NAC). Proposing the novel Base-Sensor Assisted Physical Interaction (BAPI) controller. Building simulation models for packaged tactile sensors and validating the models with experimental data. Description of Directive Observer and Lead Assistance (DOLA) framework for ARNA using adaptive interfaces

Force-based control for human-robot cooperative object manipulation

In Physical Human-Robot Interaction (PHRI), humans and robots share the workspace and physically interact and collaborate to perform a common task. However, robots do not have human levels of intelligence or the capacity to adapt in performing collaborative tasks. Moreover, the presence of humans in the vicinity of the robot requires ensuring their safety, both in terms of software and hardware. One of the aspects related to safety is the stability of the human-robot control system, which can be placed in jeopardy due to several factors such as internal time delays. Another aspect is the mutual understanding between humans and robots to prevent conflicts in performing a task. The kinesthetic transmission of the human intention is, in general, ambiguous when an object is involved, and the robot cannot distinguish the human intention to rotate from the intention to translate (the translation/rotation problem).This thesis examines the aforementioned issues related to PHRI. First, the instability arising due to a time delay is addressed. For this purpose, the time delay in the system is modeled with the exponential function, and the effect of system parameters on the stability of the interaction is examined analytically. The proposed method is compared with the state-of-the-art criteria used to study the stability of PHRI systems with similar setups and high human stiffness. Second, the unknown human grasp position is estimated by exploiting the interaction forces measured by a force/torque sensor at the robot end effector. To address cases where the human interaction torque is non-zero, the unknown parameter vector is augmented to include the human-applied torque. The proposed method is also compared via experimental studies with the conventional method, which assumes a contact point (i.e., that human torque is equal to zero). Finally, the translation/rotation problem in shared object manipulation is tackled by proposing and developing a new control scheme based on the identification of the ongoing task and the adaptation of the robot\u27s role, i.e., whether it is a passive follower or an active assistant. This scheme allows the human to transport the object independently in all degrees of freedom and also reduces human effort, which is an important factor in PHRI, especially for repetitive tasks. Simulation and experimental results clearly demonstrate that the force required to be applied by the human is significantly reduced once the task is identified

Intent Recognition Of Rotation Versus Translation Movements In Human-Robot Collaborative Manipulation Tasks

The goal of this thesis is to enable a robot to actively collaborate with a person to move an object in an efficient, smooth and robust manner. For a robot to actively assist a person it is key that the robot recognizes the actions or phases of a collaborative tasks. This requires the robot to have the ability to estimate a person’s movement intent. A hurdle in collaboratively moving an object is determining whether the partner is trying to rotate or translate the object (the rotation versus translation problem). In this thesis, Hidden Markov Models (HMM) are used to recognize human intent of rotation or translation in real-time. Based on this recognition, an appropriate impedance control mode is selected to assist the person. The approach is tested on a seven degree-of-freedom industrial robot, KUKA LBR iiwa 14 R820, working with a human partner during manipulation tasks. Results show the HMMs can estimate human intent with accuracy of 87.5% by using only haptic data recorded from the robot. Integrated with impedance control, the robot is able to collaborate smoothly and efficiently with a person during the manipulation tasks. The HMMs are compared with a switching function based approach that uses interaction force magnitudes to recognize rotation versus translation. The results show that HMMs can predict correctly when fast rotation or slow translation is desired, whereas the switching function based on force magnitudes performs poorly

Resolving conflicts during human-robot co-manipulation

UK Research and Innovation, UKRI: EP/S033718/2, EP/T022493/1, EP/V00784XThis work is partially funded by UKRI and CHIST-ERA (HEAP: EP/S033718/2; Horizon: EP/T022493/1; TAS Hub: EP/V00784X).This paper proposes a machine learning (ML) approach to detect and resolve motion conflicts that occur between a human and a proactive robot during the execution of a physically collaborative task. We train a random forest classifier to distinguish between harmonious and conflicting human-robot interaction behaviors during object co-manipulation. Kinesthetic information generated through the teamwork is used to describe the interactive quality of collaboration. As such, we demonstrate that features derived from haptic (force/torque) data are sufficient to classify if the human and the robot harmoniously manipulate the object or they face a conflict. A conflict resolution strategy is implemented to get the robotic partner to proactively contribute to the task via online trajectory planning whenever interactive motion patterns are harmonious, and to follow the human lead when a conflict is detected. An admittance controller regulates the physical interaction between the human and the robot during the task. This enables the robot to follow the human passively when there is a conflict. An artificial potential field is used to proactively control the robot motion when partners work in harmony. An experimental study is designed to create scenarios involving harmonious and conflicting interactions during collaborative manipulation of an object, and to create a dataset to train and test the random forest classifier. The results of the study show that ML can successfully detect conflicts and the proposed conflict resolution mechanism reduces human force and effort significantly compared to the case of a passive robot that always follows the human partner and a proactive robot that cannot resolve conflicts. © 2023 Copyright is held by the owner/author(s).2-s2.0-8515037875

Trust-Based Control of Robotic Manipulators in Collaborative Assembly in Manufacturing

Human-robot interaction (HRI) is vastly addressed in the field of automation and manufacturing. Most of the HRI literature in manufacturing explored physical human-robot interaction (pHRI) and invested in finding means for ensuring safety and optimized effort sharing amongst a team of humans and robots. The recent emergence of safe, lightweight, and human-friendly robots has opened a new realm for human-robot collaboration (HRC) in collaborative manufacturing. For such robots with the new HRI functionalities to interact closely and effectively with a human coworker, new human-centered controllers that integrate both physical and social interaction are demanded. Social human-robot interaction (sHRI) has been demonstrated in robots with affective abilities in education, social services, health care, and entertainment. Nonetheless, sHRI should not be limited only to those areas. In particular, we focus on human trust in robot as a basis of social interaction. Human trust in robot and robot anthropomorphic features have high impacts on sHRI. Trust is one of the key factors in sHRI and a prerequisite for effective HRC. Trust characterizes the reliance and tendency of human in using robots. Factors within a robotic system (e.g. performance, reliability, or attribute), the task, and the surrounding environment can all impact the trust dynamically. Over-reliance or under-reliance might occur due to improper trust, which results in poor team collaboration, and hence higher task load and lower overall task performance. The goal of this dissertation is to develop intelligent control algorithms for the manipulator robots that integrate both physical and social HRI factors in the collaborative manufacturing. First, the evolution of human trust in a collaborative robot model is identified and verified through a series of human-in-the-loop experiments. This model serves as a computational trust model estimating an objective criterion for the evolution of human trust in robot rather than estimating an individual\u27s actual level of trust. Second, an HRI-based framework is developed for controlling the speed of a robot performing pick and place tasks. The impact of the consideration of the different level of interaction in the robot controller on the overall efficiency and HRI criteria such as human perceived workload and trust and robot usability is studied using a series of human-in-the-loop experiments. Third, an HRI-based framework is developed for planning and controlling the robot motion in performing hand-over tasks to the human. Again, series of human-in-the-loop experimental studies are conducted to evaluate the impact of implementation of the frameworks on overall efficiency and HRI criteria such as human workload and trust and robot usability. Finally, another framework is proposed for the cooperative manipulation of a common object by a team of a human and a robot. This framework proposes a trust-based role allocation strategy for adjusting the proactive behavior of the robot performing a cooperative manipulation task in HRC scenarios. For the mentioned frameworks, the results of the experiments show that integrating HRI in the robot controller leads to a lower human workload while it maintains a threshold level of human trust in robot and does not degrade robot usability and efficiency