3 research outputs found
In Time and Space: Towards Usable Adaptive Control for Assistive Robotic Arms
Robotic solutions, in particular robotic arms, are becoming more frequently
deployed for close collaboration with humans, for example in manufacturing or
domestic care environments. These robotic arms require the user to control
several Degrees-of-Freedom (DoFs) to perform tasks, primarily involving
grasping and manipulating objects. Standard input devices predominantly have
two DoFs, requiring time-consuming and cognitively demanding mode switches to
select individual DoFs. Contemporary Adaptive DoF Mapping Controls (ADMCs) have
shown to decrease the necessary number of mode switches but were up to now not
able to significantly reduce the perceived workload. Users still bear the
mental workload of incorporating abstract mode switching into their workflow.
We address this by providing feed-forward multimodal feedback using updated
recommendations of ADMC, allowing users to visually compare the current and the
suggested mapping in real-time. We contrast the effectiveness of two new
approaches that a) continuously recommend updated DoF combinations or b) use
discrete thresholds between current robot movements and new recommendations.
Both are compared in a Virtual Reality (VR) in-person study against a classic
control method. Significant results for lowered task completion time, fewer
mode switches, and reduced perceived workload conclusively establish that in
combination with feedforward, ADMC methods can indeed outperform classic mode
switching. A lack of apparent quantitative differences between Continuous and
Threshold reveals the importance of user-centered customization options.
Including these implications in the development process will improve usability,
which is essential for successfully implementing robotic technologies with high
user acceptance
AdaptiX -- A Transitional XR Framework for Development and Evaluation of Shared Control Applications in Assistive Robotics
With the ongoing efforts to empower people with mobility impairments and the
increase in technological acceptance by the general public, assistive
technologies, such as collaborative robotic arms, are gaining popularity. Yet,
their widespread success is limited by usability issues, specifically the
disparity between user input and software control along the autonomy continuum.
To address this, shared control concepts provide opportunities to combine the
targeted increase of user autonomy with a certain level of computer assistance.
This paper presents the free and open-source AdaptiX XR framework for
developing and evaluating shared control applications in a high-resolution
simulation environment. The initial framework consists of a simulated robotic
arm with an example scenario in Virtual Reality (VR), multiple standard control
interfaces, and a specialized recording/replay system. AdaptiX can easily be
extended for specific research needs, allowing Human-Robot Interaction (HRI)
researchers to rapidly design and test novel interaction methods, intervention
strategies, and multi-modal feedback techniques, without requiring an actual
physical robotic arm during the early phases of ideation, prototyping, and
evaluation. Also, a Robot Operating System (ROS) integration enables the
controlling of a real robotic arm in a PhysicalTwin approach without any
simulation-reality gap. Here, we review the capabilities and limitations of
AdaptiX in detail and present three bodies of research based on the framework.
AdaptiX can be accessed at https://adaptix.robot-research.de.Comment: Accepted submission at The 16th ACM SIGCHI Symposium on Engineering
Interactive Computing Systems (EICS'24
Adapt or Perish? Exploring the Effectiveness of Adaptive DoF Control Interaction Methods for Assistive Robot Arms
Robot arms are one of many assistive technologies used by people with motor impairments. Assistive robot arms can allow people to perform activities of daily living (ADL) involving grasping and manipulating objects in their environment without the assistance of caregivers. Suitable input devices (e.g., joysticks) mostly have two Degrees of Freedom (DoF), while most assistive robot arms have six or more. This results in time-consuming and cognitively demanding mode switches to change the mapping of DoFs to control the robot. One option to decrease the difficulty of controlling a high-DoF assistive robot arm using a low-DoF input device is to assign different combinations of movement-DoFs to the device’s input DoFs depending on the current situation (adaptive control). To explore this method of control, we designed two adaptive control methods for a realistic virtual 3D environment. We evaluated our methods against a commonly used non-adaptive control method that requires the user to switch controls manually. This was conducted in a simulated remote study that used Virtual Reality and involved 39 non-disabled participants. Our results show that the number of mode switches necessary to complete a simple pick-and-place task decreases significantly when using an adaptive control type. In contrast, the task completion time and workload stay the same. A thematic analysis of qualitative feedback of our participants suggests that a longer period of training could further improve the performance of adaptive control methods