75,662 research outputs found
Recovering from External Disturbances in Online Manipulation through State-Dependent Revertive Recovery Policies
Robots are increasingly entering uncertain and unstructured environments.
Within these, robots are bound to face unexpected external disturbances like
accidental human or tool collisions. Robots must develop the capacity to
respond to unexpected events. That is not only identifying the sudden anomaly,
but also deciding how to handle it. In this work, we contribute a recovery
policy that allows a robot to recovery from various anomalous scenarios across
different tasks and conditions in a consistent and robust fashion. The system
organizes tasks as a sequence of nodes composed of internal modules such as
motion generation and introspection. When an introspection module flags an
anomaly, the recovery strategy is triggered and reverts the task execution by
selecting a target node as a function of a state dependency chart. The new
skill allows the robot to overcome the effects of the external disturbance and
conclude the task. Our system recovers from accidental human and tool
collisions in a number of tasks. Of particular importance is the fact that we
test the robustness of the recovery system by triggering anomalies at each node
in the task graph showing robust recovery everywhere in the task. We also
trigger multiple and repeated anomalies at each of the nodes of the task
showing that the recovery system can consistently recover anywhere in the
presence of strong and pervasive anomalous conditions. Robust recovery systems
will be key enablers for long-term autonomy in robot systems. Supplemental info
including code, data, graphs, and result analysis can be found at [1].Comment: 8 pages, 8 figures, 1 tabl
The 1990 progress report and future plans
This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers
Improving Task-Parameterised Movement Learning Generalisation with Frame-Weighted Trajectory Generation
Learning from Demonstration depends on a robot learner generalising its
learned model to unseen conditions, as it is not feasible for a person to
provide a demonstration set that accounts for all possible variations in
non-trivial tasks. While there are many learning methods that can handle
interpolation of observed data effectively, extrapolation from observed data
offers a much greater challenge. To address this problem of generalisation,
this paper proposes a modified Task-Parameterised Gaussian Mixture Regression
method that considers the relevance of task parameters during trajectory
generation, as determined by variance in the data. The benefits of the proposed
method are first explored using a simulated reaching task data set. Here it is
shown that the proposed method offers far-reaching, low-error extrapolation
abilities that are different in nature to existing learning methods. Data
collected from novice users for a real-world manipulation task is then
considered, where it is shown that the proposed method is able to effectively
reduce grasping performance errors by and extrapolate to unseen
grasp targets under real-world conditions. These results indicate the proposed
method serves to benefit novice users by placing less reliance on the user to
provide high quality demonstration data sets.Comment: 8 pages, 6 figures, submitted to 2019 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS
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