High-Level MLN-Based Approach for Spatial Context Disambiguation

Adjali, Omar; Ramdane-Chérif, Amar

doi:10.1109/icra.2018.8460923

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…With regards to adaption policies, we do not yet model the spatial relations amongst the actors of interest; namely, the robot (end-effector), active objects (like objects to be gripped and the packaging box), and the world (support surfaces like tables and floor). These relationships provide important context for decision making and are recently attracting more attention [51][52][53][54]. Without spatial relation understanding, the solutions learned in Exp.…”

Section: Limitations Comparisons and Future Workmentioning

confidence: 99%

Endowing Robots with Longer-term Autonomy by Recovering from External Disturbances in Manipulation Through Grounded Anomaly Classification and Recovery Policies

Luo

Duan

et al. 2021

J Intell Robot Syst

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Robots are poised to interact with humans in unstructured environments. Despite increasingly robust control algorithms, failure modes arise whenever the underlying dynamics are poorly modeled, especially in unstructured environments. We contribute a set of recovery policies to deal with anomalies produced by external disturbances. The recoveries work when various different types of anomalies are triggered any number of times at any point in the task, including during already running recoveries. Our recovery critic stands atop of a tightly-integrated, graph-based online motion-generation and introspection system. Policies, skills, and introspection models are learned incrementally and contextually over time. Recoveries are studied via a collaborative kitting task where a wide range of anomalous conditions are experienced in the system. We also contribute an extensive analysis of the performance of the tightly integrated anomaly identification, classification, and recovery system under extreme anomalous conditions. We show how the integration of such a system achieves performances greater than the sum of its parts.

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Section: Limitations Comparisons and Future Workmentioning

confidence: 99%

Endowing Robots with Longer-term Autonomy by Recovering from External Disturbances in Manipulation Through Grounded Anomaly Classification and Recovery Policies

Luo

Duan

et al. 2021

J Intell Robot Syst

View full text Add to dashboard Cite

show abstract

“…With regards to adaption policies, we do not yet model the spatial relations amongst the actors of interest; namely, the robot (end-effector), active objects (like objects to be gripped and the packaging box), and the world (support surfaces like tables and floor). These relationships provide important context for decision making and are recently attracting more attention (Philipp Jund and Burgard 2018;Adjali and Ramdane-Cherif 2018;Aly and Taniguchi 2018;Gong and Zhang 2018). Without spatial relation understanding, the solutions learned in Exp.…”

Section: Limitations Comparisons and Future Workmentioning

confidence: 99%

Endowing Robots with Longer-term Autonomy by Recovering from External Disturbances in Manipulation through Grounded Anomaly Classification and Recovery Policies

Wu¹,

Luo²,

Chen³

et al. 2018

Preprint

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Robot manipulation is increasingly poised to interact with humans in co-shared workspaces. Despite increasingly robust manipulation and control algorithms, failure modes continue to exist whenever models do not capture the dynamics of the unstructured environment. To obtain longer-term horizons in robot automation, robots must develop introspection and recovery abilities. We contribute a set of recovery policies to deal with anomalies produced by external disturbances as well as anomaly classification through the use of non-parametric statistics with memoized variational inference with scalable adaptation. A recovery critic stands atop of a tightly-integrated, graph-based online motion-generation and introspection system that resolves a wide range of anomalous situations. Policies, skills, and introspection models are learned incrementally and contextually in a task. Two task-level recovery policies: re-enactment and adaptation resolve accidental and persistent anomalies respectively. Re-enactment policies model human decision making to reenact the best skill in the task-graph. Adaptive recoveries leverage human intuition about the task-state to overcome persistent errors. The system is capable of fast and robust anomaly identification and classification during all phases of a task including during the execution of newly learned recovery skills. The introspection system uses non-parametric priors along with Markov jump linear systems and memoized variational inference with scalable adaptation to learn a model from the data in an incremental way and yield compact interpretable models that enhance classification and identification accuracy. Extensive real-robot experimentation with various strenuous anomalous conditions in a co-bot scenario is induced and resolved at different phases of a task and in different combinations. The system executes around-the-clock introspection and recovery and even elicited self-recovery when misclassifications occurred.

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High-Level MLN-Based Approach for Spatial Context Disambiguation

Cited by 2 publications

References 12 publications

Endowing Robots with Longer-term Autonomy by Recovering from External Disturbances in Manipulation Through Grounded Anomaly Classification and Recovery Policies

Endowing Robots with Longer-term Autonomy by Recovering from External Disturbances in Manipulation Through Grounded Anomaly Classification and Recovery Policies

Endowing Robots with Longer-term Autonomy by Recovering from External Disturbances in Manipulation through Grounded Anomaly Classification and Recovery Policies

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