29,417 research outputs found
Collaboration Development through Interactive Learning between Human and Robot
In this paper, we investigated interactive learning between human subjects and robot experimentally, and its essential characteristics are examined using the dynamical systems approach. Our research concentrated on the navigation system of a specially developed humanoid robot called Robovie and seven human subjects whose eyes were covered, making them dependent on the robot for directions. We compared the usual feed-forward neural network (FFNN) without recursive connections and the recurrent neural network (RNN). Although the performances obtained with both the RNN and the FFNN improved in the early stages of learning, as the subject changed the operation by learning on its own, all performances gradually became unstable and failed. Results of a questionnaire given to the subjects confirmed that the FFNN gives better mental impressions, especially from the aspect of operability. When the robot used a consolidation-learning algorithm using the rehearsal outputs of the RNN, the performance improved even when interactive learning continued for a long time. The questionnaire results then also confirmed that the subject's mental impressions of the RNN improved significantly. The dynamical systems analysis of RNNs support these differences and also showed that the collaboration scheme was developed dynamically along with succeeding phase transitions
Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration
We present a method for learning a human-robot collaboration policy from
human-human collaboration demonstrations. An effective robot assistant must
learn to handle diverse human behaviors shown in the demonstrations and be
robust when the humans adjust their strategies during online task execution.
Our method co-optimizes a human policy and a robot policy in an interactive
learning process: the human policy learns to generate diverse and plausible
collaborative behaviors from demonstrations while the robot policy learns to
assist by estimating the unobserved latent strategy of its human collaborator.
Across a 2D strategy game, a human-robot handover task, and a multi-step
collaborative manipulation task, our method outperforms the alternatives in
both simulated evaluations and when executing the tasks with a real human
operator in-the-loop. Supplementary materials and videos at
https://sites.google.com/view/co-gail-web/homeComment: CoRL 202
Integrating Human Expertise in Continuous Spaces: A Novel Interactive Bayesian Optimization Framework with Preference Expected Improvement
Interactive Machine Learning (IML) seeks to integrate human expertise into
machine learning processes. However, most existing algorithms cannot be applied
to Realworld Scenarios because their state spaces and/or action spaces are
limited to discrete values. Furthermore, the interaction of all existing
methods is restricted to deciding between multiple proposals. We therefore
propose a novel framework based on Bayesian Optimization (BO). Interactive
Bayesian Optimization (IBO) enables collaboration between machine learning
algorithms and humans. This framework captures user preferences and provides an
interface for users to shape the strategy by hand. Additionally, we've
incorporated a new acquisition function, Preference Expected Improvement (PEI),
to refine the system's efficiency using a probabilistic model of the user
preferences. Our approach is geared towards ensuring that machines can benefit
from human expertise, aiming for a more aligned and effective learning process.
In the course of this work, we applied our method to simulations and in a real
world task using a Franka Panda robot to show human-robot collaboration
Neural control for constrained human-robot interaction with human motion intention estimation and impedance learning
In this paper, an impedance control strategy is proposed for a rigid robot collaborating with human by considering impedance learning and human motion intention estimation. The least square method is used in human impedance identification, and the robot can adjust its impedance parameters according to human impedance model for guaranteeing compliant collaboration. Neural networks (NNs) are employed in human motion intention estimation, so that the robot follows the human actively and human partner costs less control effort. On the other hand, the full-state constraints are considered for operational safety in human-robot interactive processes. Neural control is presented in the control strategy to deal with the dynamic uncertainties and improve the system robustness. Simulation results are carried out to show the effectiveness of the proposed control design
Human-Robot Interaction for Children with Cerebral Palsy: Reflection and Suggestion for Interactive Scenario Design
AbstractThis paper explains how a humanoid robot NAO can be used as an assistive technology in specific therapy for children with cerebral palsy (CP). The role of the robot is to motivate the children as to keep them engaged in therapy. To achieve this, the robot must have appropriate appearance to be able to establish affective engagement between child and robot. In addition, the robot should exhibit the right therapeutic approach of managing children with CP. How the humanoid robot NAO acts as a tool to assist in improving the outcome of conventional therapy especially by imitation learning will also be explained. Four interactive scenarios in human-robot interaction (HRI) were designed based on the measurement items in Gross Motor Functional Measure (GMFM). The scenarios will then be constructed based on suitability that will be executed by the robot. As a result from the discussions between clinicians, therapists and engineers, four interactive scenarios consists of introductory rapport, sit to stand, body balancing and ball kicking activity have been formulated. The study has been performed in collaboration between the Faculty of Medicine and the Faculty of Mechanical Engineering at the Medical Specialist Centre, Discipline of Rehabilitation Medicine, Faculty of Medicine, UiTM Sungai Buloh, Selangor, Malaysia
Interactive Text2Pickup Network for Natural Language based Human-Robot Collaboration
In this paper, we propose the Interactive Text2Pickup (IT2P) network for
human-robot collaboration which enables an effective interaction with a human
user despite the ambiguity in user's commands. We focus on the task where a
robot is expected to pick up an object instructed by a human, and to interact
with the human when the given instruction is vague. The proposed network
understands the command from the human user and estimates the position of the
desired object first. To handle the inherent ambiguity in human language
commands, a suitable question which can resolve the ambiguity is generated. The
user's answer to the question is combined with the initial command and given
back to the network, resulting in more accurate estimation. The experiment
results show that given unambiguous commands, the proposed method can estimate
the position of the requested object with an accuracy of 98.49% based on our
test dataset. Given ambiguous language commands, we show that the accuracy of
the pick up task increases by 1.94 times after incorporating the information
obtained from the interaction.Comment: 8 pages, 9 figure
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