1,861 research outputs found
A global workspace theory model for trust estimation in human-robot interaction
Successful and genuine social connections between humans are based on trust, even more when the people involved have to collaborate to reach a shared goal. With the advent of new findings and technologies in the field of robotics, it appears that this same key factor that regulates relationships between humans also applies with the same importance to human-robot interactions (HRI). Previous studies have proven the usefulness of a robot able to estimate the trustworthiness of its human collaborators and in this position paper we discuss a method to extend an existing state-of-the-art trust model with considerations based on social cues such as emotions. The proposed model follows the Global Workspace Theory (GWT) principles to build a novel system able to combine multiple specialised expert systems to determine whether the partner can be considered trustworthy or not. Positive results would demonstrate the usefulness of using constructive biases to enhance the teaming skills of social robots
Human-Robot Trust Integrated Task Allocation and Symbolic Motion planning for Heterogeneous Multi-robot Systems
This paper presents a human-robot trust integrated task allocation and motion
planning framework for multi-robot systems (MRS) in performing a set of tasks
concurrently. A set of task specifications in parallel are conjuncted with MRS
to synthesize a task allocation automaton. Each transition of the task
allocation automaton is associated with the total trust value of human in
corresponding robots. Here, the human-robot trust model is constructed with a
dynamic Bayesian network (DBN) by considering individual robot performance,
safety coefficient, human cognitive workload and overall evaluation of task
allocation. Hence, a task allocation path with maximum encoded human-robot
trust can be searched based on the current trust value of each robot in the
task allocation automaton. Symbolic motion planning (SMP) is implemented for
each robot after they obtain the sequence of actions. The task allocation path
can be intermittently updated with this DBN based trust model. The overall
strategy is demonstrated by a simulation with 5 robots and 3 parallel subtask
automata
Occupational health and safety issues in human-robot collaboration: State of the art and open challenges
Human-Robot Collaboration (HRC) refers to the interaction of workers and robots in a shared workspace. Owing to the integration of the industrial automation strengths with the inimitable cognitive capabilities of humans, HRC is paramount to move towards advanced and sustainable production systems. Although the overall safety of collaborative robotics has increased over time, further research efforts are needed to allow humans to operate alongside robots, with awareness and trust. Numerous safety concerns are open, and either new or enhanced
technical, procedural and organizational measures have to be investigated to design and implement inherently safe and ergonomic automation solutions, aligning the systems performance and the human safety. Therefore, a bibliometric analysis and a literature review are carried out in the present paper to provide a comprehensive overview of Occupational Health and Safety (OHS) issues in HRC. As a result, the most researched topics and application areas, and the possible future lines of research are identified. Reviewed articles stress the central role
played by humans during collaboration, underlining the need to integrate the human factor in the hazard analysis and risk assessment. Human-centered design and cognitive engineering principles also require further investigations to increase the worker acceptance and trust during collaboration. Deepened studies are compulsory in the healthcare sector, to investigate the social and ethical implications of HRC. Whatever the application context is, the implementation of more and more advanced technologies is fundamental to overcome the current HRC safety concerns, designing low-risk HRC systems while ensuring the system productivity
Multi-Robot Symbolic Task and Motion Planning Leveraging Human Trust Models: Theory and Applications
Multi-robot systems (MRS) can accomplish more complex tasks with two or more robots and have produced a broad set of applications. The presence of a human operator in an MRS can guarantee the safety of the task performing, but the human operators can be subject to heavier stress and cognitive workload in collaboration with the MRS than the single robot. It is significant for the MRS to have the provable correct task and motion planning solution for a complex task. That can reduce the human workload during supervising the task and improve the reliability of human-MRS collaboration. This dissertation relies on formal verification to provide the provable-correct solution for the robotic system. One of the challenges in task and motion planning under temporal logic task specifications is developing computationally efficient MRS frameworks. The dissertation first presents an automaton-based task and motion planning framework for MRS to satisfy finite words of linear temporal logic (LTL) task specifications in parallel and concurrently. Furthermore, the dissertation develops a computational trust model to improve the human-MRS collaboration for a motion task. Notably, the current works commonly underemphasize the environmental attributes when investigating the impacting factors of human trust in robots. Our computational trust model builds a linear state-space (LSS) equation to capture the influence of environment attributes on human trust in an MRS. A Bayesian optimization based experimental design (BOED) is proposed to sequentially learn the human-MRS trust model parameters in a data-efficient way. Finally, the dissertation shapes a reward function for the human-MRS collaborated complex task by referring to the above LTL task specification and computational trust model. A Bayesian active reinforcement learning (RL) algorithm is used to concurrently learn the shaped reward function and explore the most trustworthy task and motion planning solution
Trust-Preserved Human-Robot Shared Autonomy enabled by Bayesian Relational Event Modeling
Shared autonomy functions as a flexible framework that empowers robots to
operate across a spectrum of autonomy levels, allowing for efficient task
execution with minimal human oversight. However, humans might be intimidated by
the autonomous decision-making capabilities of robots due to perceived risks
and a lack of trust. This paper proposed a trust-preserved shared autonomy
strategy that allows robots to seamlessly adjust their autonomy level, striving
to optimize team performance and enhance their acceptance among human
collaborators. By enhancing the relational event modeling framework with
Bayesian learning techniques, this paper enables dynamic inference of human
trust based solely on time-stamped relational events communicated within
human-robot teams. Adopting a longitudinal perspective on trust development and
calibration in human-robot teams, the proposed trust-preserved shared autonomy
strategy warrants robots to actively establish, maintain, and repair human
trust, rather than merely passively adapting to it. We validate the
effectiveness of the proposed approach through a user study on a human-robot
collaborative search and rescue scenario. The objective and subjective
evaluations demonstrate its merits on both task execution and user
acceptability over the baseline approach that does not consider the
preservation of trust.Comment: Submitted to RA-
Active End-Effector Pose Selection for Tactile Object Recognition through Monte Carlo Tree Search
This paper considers the problem of active object recognition using touch
only. The focus is on adaptively selecting a sequence of wrist poses that
achieves accurate recognition by enclosure grasps. It seeks to minimize the
number of touches and maximize recognition confidence. The actions are
formulated as wrist poses relative to each other, making the algorithm
independent of absolute workspace coordinates. The optimal sequence is
approximated by Monte Carlo tree search. We demonstrate results in a physics
engine and on a real robot. In the physics engine, most object instances were
recognized in at most 16 grasps. On a real robot, our method recognized objects
in 2--9 grasps and outperformed a greedy baseline.Comment: Accepted to International Conference on Intelligent Robots and
Systems (IROS) 201
Active End-Effector Pose Selection for Tactile Object Recognition through Monte Carlo Tree Search
This paper considers the problem of active object recognition using touch
only. The focus is on adaptively selecting a sequence of wrist poses that
achieves accurate recognition by enclosure grasps. It seeks to minimize the
number of touches and maximize recognition confidence. The actions are
formulated as wrist poses relative to each other, making the algorithm
independent of absolute workspace coordinates. The optimal sequence is
approximated by Monte Carlo tree search. We demonstrate results in a physics
engine and on a real robot. In the physics engine, most object instances were
recognized in at most 16 grasps. On a real robot, our method recognized objects
in 2--9 grasps and outperformed a greedy baseline.Comment: Accepted to International Conference on Intelligent Robots and
Systems (IROS) 201
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