4,719 research outputs found
Assistive Planning in Complex, Dynamic Environments: a Probabilistic Approach
We explore the probabilistic foundations of shared control in complex dynamic
environments. In order to do this, we formulate shared control as a random
process and describe the joint distribution that governs its behavior. For
tractability, we model the relationships between the operator, autonomy, and
crowd as an undirected graphical model. Further, we introduce an interaction
function between the operator and the robot, that we call "agreeability"; in
combination with the methods developed in~\cite{trautman-ijrr-2015}, we extend
a cooperative collision avoidance autonomy to shared control. We therefore
quantify the notion of simultaneously optimizing over agreeability (between the
operator and autonomy), and safety and efficiency in crowded environments. We
show that for a particular form of interaction function between the autonomy
and the operator, linear blending is recovered exactly. Additionally, to
recover linear blending, unimodal restrictions must be placed on the models
describing the operator and the autonomy. In turn, these restrictions raise
questions about the flexibility and applicability of the linear blending
framework. Additionally, we present an extension of linear blending called
"operator biased linear trajectory blending" (which formalizes some recent
approaches in linear blending such as~\cite{dragan-ijrr-2013}) and show that
not only is this also a restrictive special case of our probabilistic approach,
but more importantly, is statistically unsound, and thus, mathematically,
unsuitable for implementation. Instead, we suggest a statistically principled
approach that guarantees data is used in a consistent manner, and show how this
alternative approach converges to the full probabilistic framework. We conclude
by proving that, in general, linear blending is suboptimal with respect to the
joint metric of agreeability, safety, and efficiency
Shared Autonomy via Hindsight Optimization
In shared autonomy, user input and robot autonomy are combined to control a
robot to achieve a goal. Often, the robot does not know a priori which goal the
user wants to achieve, and must both predict the user's intended goal, and
assist in achieving that goal. We formulate the problem of shared autonomy as a
Partially Observable Markov Decision Process with uncertainty over the user's
goal. We utilize maximum entropy inverse optimal control to estimate a
distribution over the user's goal based on the history of inputs. Ideally, the
robot assists the user by solving for an action which minimizes the expected
cost-to-go for the (unknown) goal. As solving the POMDP to select the optimal
action is intractable, we use hindsight optimization to approximate the
solution. In a user study, we compare our method to a standard
predict-then-blend approach. We find that our method enables users to
accomplish tasks more quickly while utilizing less input. However, when asked
to rate each system, users were mixed in their assessment, citing a tradeoff
between maintaining control authority and accomplishing tasks quickly
Autonomy Infused Teleoperation with Application to BCI Manipulation
Robot teleoperation systems face a common set of challenges including
latency, low-dimensional user commands, and asymmetric control inputs. User
control with Brain-Computer Interfaces (BCIs) exacerbates these problems
through especially noisy and erratic low-dimensional motion commands due to the
difficulty in decoding neural activity. We introduce a general framework to
address these challenges through a combination of computer vision, user intent
inference, and arbitration between the human input and autonomous control
schemes. Adjustable levels of assistance allow the system to balance the
operator's capabilities and feelings of comfort and control while compensating
for a task's difficulty. We present experimental results demonstrating
significant performance improvement using the shared-control assistance
framework on adapted rehabilitation benchmarks with two subjects implanted with
intracortical brain-computer interfaces controlling a seven degree-of-freedom
robotic manipulator as a prosthetic. Our results further indicate that shared
assistance mitigates perceived user difficulty and even enables successful
performance on previously infeasible tasks. We showcase the extensibility of
our architecture with applications to quality-of-life tasks such as opening a
door, pouring liquids from containers, and manipulation with novel objects in
densely cluttered environments
A hierarchic approach for path planning in virtual reality.
This work considers path-planning processes for manipu- lation tasks such as assembly, maintenance or disassem- bly in a virtual reality (VR) context. The approach con- sists in providing a collaborative system associating a user immersed in VR and an automatic path planning process. It is based on semantic, topological and geometric representations of the environment and the planning process is split in two phases: coarse and fine planning. The automatic planner suggests a path to the user and guides him trough a haptic device. The user can escape from the proposed solution if he wants to explore a possible better way. In this case, the interactive system detects the users intention and computes in real-time a new path starting from the users guess. Experiments illustrate the different aspects of the approach: multi-representation of the en- vironment, path planning process, users intent prediction and control sharing
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Goal Blending for Responsive Shared Autonomy in a Navigating Vehicle
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