1,705 research outputs found
Learning Dynamic Robot-to-Human Object Handover from Human Feedback
Object handover is a basic, but essential capability for robots interacting
with humans in many applications, e.g., caring for the elderly and assisting
workers in manufacturing workshops. It appears deceptively simple, as humans
perform object handover almost flawlessly. The success of humans, however,
belies the complexity of object handover as collaborative physical interaction
between two agents with limited communication. This paper presents a learning
algorithm for dynamic object handover, for example, when a robot hands over
water bottles to marathon runners passing by the water station. We formulate
the problem as contextual policy search, in which the robot learns object
handover by interacting with the human. A key challenge here is to learn the
latent reward of the handover task under noisy human feedback. Preliminary
experiments show that the robot learns to hand over a water bottle naturally
and that it adapts to the dynamics of human motion. One challenge for the
future is to combine the model-free learning algorithm with a model-based
planning approach and enable the robot to adapt over human preferences and
object characteristics, such as shape, weight, and surface texture.Comment: Appears in the Proceedings of the International Symposium on Robotics
Research (ISRR) 201
Data-driven Grip Force Variation in Robot-Human Handovers
Handovers frequently occur in our social environments, making it imperative
for a collaborative robotic system to master the skill of handover. In this
work, we aim to investigate the relationship between the grip force variation
for a human giver and the sensed interaction force-torque in human-human
handovers, utilizing a data-driven approach. A Long-Short Term Memory (LSTM)
network was trained to use the interaction force-torque in a handover to
predict the human grip force variation in advance. Further, we propose to
utilize the trained network to cause human-like grip force variation for a
robotic giver.Comment: Contributed to "Advances in Close Proximity Human-Robot
Collaboration" Workshop in 2022 IEEE-RAS International Conference on Humanoid
Robots (Humanoids 2022
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Reliable robotic handovers through tactile sensing
Joint manipulation and object exchange are common in many everyday scenarios. Although they are trivial tasks for humans, they are still very challenging for robots. Existing approaches for robot-to-human object handover assume that there is no fault during the transfer. However, unintentional perturbation forces can be occasionally applied to the object, resulting in the robot and the object being damaged, for example by being dropped. In this paper we present a novel approach to handover objects in a reliable manner while ensuring the safety of the robot and the object. Relying on tactile sensing, the system uses an effort controller to adapt the grasp forces in the presence of perturbations. Moreover, the proposed approach identifies a perturbation being applied on the object. When a perturbation event is detected, the algorithm classifies the direction of the pulling forces to decide whether to release it or not. The reliable handover system was implemented using a Shadow Robot hand equipped with BioTAC tactile sensors. Our results show that the system correctly adapts to the forces applied on the object to maintain the grasp and only releases the object if the human receiver pulls in the right direction
Optometrist's Algorithm for Personalizing Robot-Human Handovers
With an increasing interest in human-robot collaboration, there is a need to
develop robot behavior while keeping the human user's preferences in mind.
Highly skilled human users doing delicate tasks require their robot partners to
behave according to their work habits and task constraints. To achieve this, we
present the use of the Optometrist's Algorithm (OA) to interactively and
intuitively personalize robot-human handovers. Using this algorithm, we tune
controller parameters for speed, location, and effort. We study the differences
in the fluency of the handovers before and after tuning and the subjective
perception of this process in a study of non-expert users of mixed
background -- evaluating the OA. The users evaluate the interaction on trust,
safety, and workload scales, amongst other measures. They assess our tuning
process to be engaging and easy to use. Personalization leads to an increase in
the fluency of the interaction. Our participants utilize the wide range of
parameters ending up with their unique personalized handover.Comment: 7 pages, 5 figures. Accepted at IEEE-ROMAN 2023. For more information
visit: https://github.com/vivekgupte07/optometrist-algorithm-handover
A Multimodal Data Set of Human Handovers with Design Implications for Human-Robot Handovers
Handovers are basic yet sophisticated motor tasks performed seamlessly by
humans. They are among the most common activities in our daily lives and social
environments. This makes mastering the art of handovers critical for a social
and collaborative robot. In this work, we present an experimental study that
involved human-human handovers by 13 pairs, i.e., 26 participants. We record
and explore multiple features of handovers amongst humans aimed at inspiring
handovers amongst humans and robots. With this work, we further create and
publish a novel data set of 8672 handovers, bringing together human motion and
the forces involved. We further analyze the effect of object weight and the
role of visual sensory input in human-human handovers, as well as possible
design implications for robots. As a proof of concept, the data set was used
for creating a human-inspired data-driven strategy for robotic grip release in
handovers, which was demonstrated to result in better robot to human handovers.Comment: The data set of human-human handovers can be found at:
https://github.com/paragkhanna1/datase
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
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