31 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
A Study of Human-Robot Handover through Human-Human Object Transfer
In this preliminary study, we investigate changes in handover behaviour when
transferring hazardous objects with the help of a high-resolution touch sensor.
Participants were asked to hand over a safe and hazardous object (a full cup
and an empty cup) while instrumented with a modified STS sensor. Our data shows
a clear distinction in the length of handover for the full cup vs the empty
one, with the former being slower. Sensor data further suggests a change in
tactile behaviour dependent on the object's risk factor. The results of this
paper motivate a deeper study of tactile factors which could characterize a
risky handover, allowing for safer human-robot interactions in the future.Comment: 8 pages, 5 figures, appeared in NeurIPS 2022 Workshop on Human in the
Loop Learnin
Human-AI Collaboration in Content Moderation: The Effects of Information Cues and Time Constraints
An extremely large amount of user-generated content is produced by users worldwide every day with the rapid development of online social media. Content moderation has emerged to ensure the quality of posts on various social media platforms. This process typically demands collaboration between humans and AI because of the complementarity of the two agents in different facets. Wondering how AI can better assist humans to make final judgment in the “machine-in-the-loop” paradigm, we propose a lab experiment to explore the influence of different types of cues provided by AI through a nudging approach as well as time constraints on human moderators’ performance. The proposed study contributes to the literature on the AI-assisted decision-making pattern, and helps social media platforms in creating an effective human-AI collaboration framework for content moderation
Affordance-Aware Handovers With Human Arm Mobility Constraints
Reasoning about object handover configurations allows an assistive agent to
estimate the appropriateness of handover for a receiver with different arm
mobility capacities. While there are existing approaches for estimating the
effectiveness of handovers, their findings are limited to users without arm
mobility impairments and to specific objects. Therefore, current
state-of-the-art approaches are unable to hand over novel objects to receivers
with different arm mobility capacities. We propose a method that generalises
handover behaviours to previously unseen objects, subject to the constraint of
a user's arm mobility levels and the task context. We propose a
heuristic-guided hierarchically optimised cost whose optimisation adapts object
configurations for receivers with low arm mobility. This also ensures that the
robot grasps consider the context of the user's upcoming task, i.e., the usage
of the object. To understand preferences over handover configurations, we
report on the findings of an online study, wherein we presented different
handover methods, including ours, to users with different levels of arm
mobility. We find that people's preferences over handover methods are
correlated to their arm mobility capacities. We encapsulate these preferences
in a statistical relational model (SRL) that is able to reason about the most
suitable handover configuration given a receiver's arm mobility and upcoming
task. Using our SRL model, we obtained an average handover accuracy of
when generalising handovers to novel objects.Comment: Accepted for RA-L 202
In-Mouth Robotic Bite Transfer with Visual and Haptic Sensing
Assistance during eating is essential for those with severe mobility issues
or eating risks. However, dependence on traditional human caregivers is linked
to malnutrition, weight loss, and low self-esteem. For those who require eating
assistance, a semi-autonomous robotic platform can provide independence and a
healthier lifestyle. We demonstrate an essential capability of this platform:
safe, comfortable, and effective transfer of a bite-sized food item from a
utensil directly to the inside of a person's mouth. Our system uses a
force-reactive controller to safely accommodate the user's motions throughout
the transfer, allowing full reactivity until bite detection then reducing
reactivity in the direction of exit. Additionally, we introduce a novel
dexterous wrist-like end effector capable of small, unimposing movements to
reduce user discomfort. We conduct a user study with 11 participants covering 8
diverse food categories to evaluate our system end-to-end, and we find that
users strongly prefer our method to a wide range of baselines. Appendices and
videos are available at our website: https://tinyurl.com/btICRA.Comment: Accepted to ICRA 202