10 research outputs found
Hierarchical Salient Object Detection for Assisted Grasping
Visual scene decomposition into semantic entities is one of the major
challenges when creating a reliable object grasping system. Recently, we
introduced a bottom-up hierarchical clustering approach which is able to
segment objects and parts in a scene. In this paper, we introduce a transform
from such a segmentation into a corresponding, hierarchical saliency function.
In comprehensive experiments we demonstrate its ability to detect salient
objects in a scene. Furthermore, this hierarchical saliency defines a most
salient corresponding region (scale) for every point in an image. Based on
this, an easy-to-use pick and place manipulation system was developed and
tested exemplarily.Comment: Accepted for ICRA 201
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
Online User Assessment for Minimal Intervention During Task-Based Robotic Assistance
We propose a novel criterion for evaluating user input for human-robot
interfaces for known tasks. We use the mode insertion gradient (MIG)---a tool
from hybrid control theory---as a filtering criterion that instantaneously
assesses the impact of user actions on a dynamic system over a time window into
the future. As a result, the filter is permissive to many chosen strategies,
minimally engaging, and skill-sensitive---qualities desired when evaluating
human actions. Through a human study with 28 healthy volunteers, we show that
the criterion exhibits a low, but significant, negative correlation between
skill level, as estimated from task-specific measures in unassisted trials, and
the rate of controller intervention during assistance. Moreover, a MIG-based
filter can be utilized to create a shared control scheme for training or
assistance. In the human study, we observe a substantial training effect when
using a MIG-based filter to perform cart-pendulum inversion, particularly when
comparing improvement via the RMS error measure. Using simulation of a
controlled spring-loaded inverted pendulum (SLIP) as a test case, we observe
that the MIG criterion could be used for assistance to guarantee either task
completion or safety of a joint human-robot system, while maintaining the
system's flexibility with respect to user-chosen strategies.Comment: 10 page
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Approaches to Safety in Inverse Reinforcement Learning
As the capabilities of robotic systems increase, we move closer to the vision of ubiquitous robotic assistance throughout our everyday lives. In transitioning robots and autonomous systems from traditional factory and industrial settings, it is critical that these systems are able to adapt to uncertain environments and the humans who populate them. In order to better understand and predict the behavior of these humans, Inverse Reinforcement Learning (IRL) uses demonstrations to infer the underlying motivations driving human actions. The information gained from IRL can be used to improve a robot’s understanding of the environment as well as to allow the robot to better interact with or assist humans.In this dissertation, we address the challenge of incorporating safety into the application of IRL. We first consider safety in the context of using IRL for assisting humans in shared control tasks. Through a user study, we show how incorporating haptic feedback into human assistance can increase humans’ sense of control while improving safety in the presence of imperfect learning. Further, we present our method for using IRL to automatically create such haptic feedback policies from task demonstrations.We further address safety in IRL by incorporating notions of safety directly into the learning process. Currently, most work on IRL focuses on learning explanatory rewards that humans are modeled as optimizing. However, pure reward optimization can fail to effectively capture hard requirements, such as safety constraints. We draw on the definition of safety from Hamilton-Jacobi reachability analysis to infer human perceptions of safety and to modify robot behavior to respect these learned safety constraints. We also extend this work on learning constraints by adapting the framework of Maximum Entropy IRL in order to learn hard constraints given nominal task rewards, and we show how this technique infers the most likely constraints to align expected behavior with observed demonstrations
Scaled Autonomy for Networked Humanoids
Humanoid robots have been developed with the intention of aiding in environments designed for humans. As such, the control of humanoid morphology and effectiveness of human robot interaction form the two principal research issues for deploying these robots in the real world. In this thesis work, the issue of humanoid control is coupled with human robot interaction under the framework of scaled autonomy, where the human and robot exchange levels of control depending on the environment and task at hand. This scaled autonomy is approached with control algorithms for reactive stabilization of human commands and planned trajectories that encode semantically meaningful motion preferences in a sequential convex optimization framework.
The control and planning algorithms have been extensively tested in the field for robustness and system verification. The RoboCup competition provides a benchmark competition for autonomous agents that are trained with a human supervisor. The kid-sized and adult-sized humanoid robots coordinate over a noisy network in a known environment with adversarial opponents, and the software and routines in this work allowed for five consecutive championships. Furthermore, the motion planning and user interfaces developed in the work have been tested in the noisy network of the DARPA Robotics Challenge (DRC) Trials and Finals in an unknown environment.
Overall, the ability to extend simplified locomotion models to aid in semi-autonomous manipulation allows untrained humans to operate complex, high dimensional robots. This represents another step in the path to deploying humanoids in the real world, based on the low dimensional motion abstractions and proven performance in real world tasks like RoboCup and the DRC