4 research outputs found
Operation and Imitation under Safety-Aware Shared Control
We describe a shared control methodology that can, without knowledge of the
task, be used to improve a human's control of a dynamic system, be used as a
training mechanism, and be used in conjunction with Imitation Learning to
generate autonomous policies that recreate novel behaviors. Our algorithm
introduces autonomy that assists the human partner by enforcing safety and
stability constraints. The autonomous agent has no a priori knowledge of the
desired task and therefore only adds control information when there is concern
for the safety of the system. We evaluate the efficacy of our approach with a
human subjects study consisting of 20 participants. We find that our shared
control algorithm significantly improves the rate at which users are able to
successfully execute novel behaviors. Experimental results suggest that the
benefits of our safety-aware shared control algorithm also extend to the human
partner's understanding of the system and their control skill. Finally, we
demonstrate how a combination of our safety-aware shared control algorithm and
Imitation Learning can be used to autonomously recreate the demonstrated
behaviors.Comment: Published in WAFR 201
A System for Traded Control Teleoperation of Manipulation Tasks using Intent Prediction from Hand Gestures
This paper presents a teleoperation system that includes robot perception and
intent prediction from hand gestures. The perception module identifies the
objects present in the robot workspace and the intent prediction module which
object the user likely wants to grasp. This architecture allows the approach to
rely on traded control instead of direct control: we use hand gestures to
specify the goal objects for a sequential manipulation task, the robot then
autonomously generates a grasping or a retrieving motion using trajectory
optimization. The perception module relies on the model-based tracker to
precisely track the 6D pose of the objects and makes use of a state of the art
learning-based object detection and segmentation method, to initialize the
tracker by automatically detecting objects in the scene. Goal objects are
identified from user hand gestures using a trained a multi-layer perceptron
classifier. After presenting all the components of the system and their
empirical evaluation, we present experimental results comparing our pipeline to
a direct traded control approach (i.e., one that does not use prediction) which
shows that using intent prediction allows to bring down the overall task
execution time.Comment: Accepted to IEEE-RoMAN 202
Natural Gradient Shared Control
We propose a formalism for shared control, which is the problem of defining a
policy that blends user control and autonomous control. The challenge posed by
the shared autonomy system is to maintain user control authority while allowing
the robot to support the user. This can be done by enforcing constraints or
acting optimally when the intent is clear. Our proposed solution relies on
natural gradients emerging from the divergence constraint between the robot and
the shared policy. We approximate the Fisher information by sampling a learned
robot policy and computing the local gradient to augment the user control when
necessary. A user study performed on a manipulation task demonstrates that our
approach allows for more efficient task completion while keeping control
authority against a number of baseline methods
Data-driven Koopman Operators for Model-based Shared Control of Human-Machine Systems
We present a data-driven shared control algorithm that can be used to improve
a human operator's control of complex dynamic machines and achieve tasks that
would otherwise be challenging, or impossible, for the user on their own. Our
method assumes no a priori knowledge of the system dynamics. Instead, both the
dynamics and information about the user's interaction are learned from
observation through the use of a Koopman operator. Using the learned model, we
define an optimization problem to compute the autonomous partner's control
policy. Finally, we dynamically allocate control authority to each partner
based on a comparison of the user input and the autonomously generated control.
We refer to this idea as model-based shared control (MbSC). We evaluate the
efficacy of our approach with two human subjects studies consisting of 32 total
participants (16 subjects in each study). The first study imposes a linear
constraint on the modeling and autonomous policy generation algorithms. The
second study explores the more general, nonlinear variant. Overall, we find
that model-based shared control significantly improves task and control metrics
when compared to a natural learning, or user only, control paradigm. Our
experiments suggest that models learned via the Koopman operator generalize
across users, indicating that it is not necessary to collect data from each
individual user before providing assistance with MbSC. We also demonstrate the
data-efficiency of MbSC and consequently, it's usefulness in online learning
paradigms. Finally, we find that the nonlinear variant has a greater impact on
a user's ability to successfully achieve a defined task than the linear
variant