338 research outputs found
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
Learn and Transfer Knowledge of Preferred Assistance Strategies in Semi-autonomous Telemanipulation
Enabling robots to provide effective assistance yet still accommodating the
operator's commands for telemanipulation of an object is very challenging
because robot's assistive action is not always intuitive for human operators
and human behaviors and preferences are sometimes ambiguous for the robot to
interpret. Although various assistance approaches are being developed to
improve the control quality from different optimization perspectives, the
problem still remains in determining the appropriate approach that satisfies
the fine motion constraints for the telemanipulation task and preference of the
operator. To address these problems, we developed a novel preference-aware
assistance knowledge learning approach. An assistance preference model learns
what assistance is preferred by a human, and a stagewise model updating method
ensures the learning stability while dealing with the ambiguity of human
preference data. Such a preference-aware assistance knowledge enables a
teleoperated robot hand to provide more active yet preferred assistance toward
manipulation success. We also developed knowledge transfer methods to transfer
the preference knowledge across different robot hand structures to avoid
extensive robot-specific training. Experiments to telemanipulate a 3-finger
hand and 2-finger hand, respectively, to use, move, and hand over a cup have
been conducted. Results demonstrated that the methods enabled the robots to
effectively learn the preference knowledge and allowed knowledge transfer
between robots with less training effort
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
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