6,498 research outputs found
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Beyond Basins of Attraction: Quantifying Robustness of Natural Dynamics
Properly designing a system to exhibit favorable natural dynamics can greatly
simplify designing or learning the control policy. However, it is still unclear
what constitutes favorable natural dynamics and how to quantify its effect.
Most studies of simple walking and running models have focused on the basins of
attraction of passive limit-cycles and the notion of self-stability. We instead
emphasize the importance of stepping beyond basins of attraction. We show an
approach based on viability theory to quantify robust sets in state-action
space. These sets are valid for the family of all robust control policies,
which allows us to quantify the robustness inherent to the natural dynamics
before designing the control policy or specifying a control objective. We
illustrate our formulation using spring-mass models, simple low dimensional
models of running systems. We then show an example application by optimizing
robustness of a simulated planar monoped, using a gradient-free optimization
scheme. Both case studies result in a nonlinear effective stiffness providing
more robustness.Comment: 15 pages. This work has been accepted to IEEE Transactions on
Robotics (2019
A causal-based approach to explain, predict and prevent failures in robotic tasks
Robots working in human environments need to adapt to unexpected changes to avoid failures. This is an open and complex challenge that requires robots to timely predict and identify the causes of failures in order to prevent them. In this paper, we present a causal-based method that will enable robots to predict when errors are likely to occur and prevent them from happening by executing a corrective action. Our proposed method is able to predict immediate failures and also failures that will occur in the future. The latter type of failure is very challenging, and we call them timely-shifted action failures (e.g., the current action was successful but will negatively affect the success of future actions). First, our method detects the cause–effect relationships between task executions and their consequences by learning a causal Bayesian network (BN). The obtained model is transferred from simulated data to real scenarios to demonstrate the robustness and generalization of the obtained models. Based on the causal BN, the robot can predict if and why the executed action will succeed or not in its current state. Then, we introduce a novel method that finds the closest success state through a contrastive Breadth-First-Search if the current action was predicted to fail. We evaluate our approach for the problem of stacking cubes in two cases; (a) single stacks (stacking one cube) and; (b) multiple stacks (stacking three cubes). In the single-stack case, our method was able to reduce the error rate by 97%. We also show that our approach can scale to capture various actions in one model, allowing us to measure the impact of an imprecise stack of the first cube on the stacking success of the third cube. For these complex situations, our model was able to prevent around 95% of the stacking errors. Thus, demonstrating that our method is able to explain, predict, and prevent execution failures, which even scales to complex scenarios that require an understanding of how the action history impacts future actions
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
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