Simultaneous learning of hierarchy and primitives for complex robot tasks

Mohseni-Kabir, Anahita; Li, Changshuo; Wu, Victoria; Miller, Delbert C.; Hylak, Benjamin; Chernova, Sonia; Berenson, Dmitry; Sidner, Candace L.; Rich, Charles

doi:10.1007/s10514-018-9749-y

Cited by 21 publications

(15 citation statements)

References 25 publications

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“…the estimation of a function that generates the desired behavior [40]; • Cost and reward functions, where it is assumed that the ideal behavior results from the optimization of a hidden function [41]; • Plans, i.e. high level structured schemes, composed of several sub-tasks or primitive actions [42]; • Multiple outcomes simultaneously, by jointly modeling complex behaviors at multiple levels of abstraction [43].…”

Section: Challenges In Lfd Learningmentioning

confidence: 99%

Towards Open and Expandable Cognitive AI Architectures for Large-Scale Multi-Agent Human-Robot Collaborative Learning

2021

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Learning from Demonstration (LfD) constitutes one of the most robust methodologies for constructing efficient cognitive robotic systems. Despite the large body of research works already reported, current key technological challenges include those of multi-agent learning and long-term autonomy. Towards this direction, a novel cognitive architecture for multi-agent LfD robotic learning is introduced in this paper, targeting to enable the reliable deployment of open, scalable and expandable robotic systems in large-scale and complex environments. In particular, the designed architecture capitalizes on the recent advances in the Artificial Intelligence (AI) (and especially the Deep Learning (DL)) field, by establishing a Federated Learning (FL)-based framework for incarnating a multi-human multi-robot collaborative learning environment. The fundamental conceptualization relies on employing multiple AI-empowered cognitive processes (implementing various robotic tasks) that operate at the edge nodes of a network of robotic platforms, while global AI models (underpinning the aforementioned robotic tasks) are collectively created and shared among the network, by elegantly combining information from a large number of human-robot interaction instances. Regarding pivotal novelties, the designed cognitive architecture a) introduces a new FL-based formalism that extends the conventional LfD learning paradigm to support large-scale multi-agent operational settings, b) elaborates previous FL-based self-learning robotic schemes so as to incorporate the human in the learning loop and c) consolidates the fundamental principles of FL with additional sophisticated AI-enabled learning methodologies for modelling the multi-level inter-dependencies among the robotic tasks. The applicability of the proposed framework is explained using an example of a realworld industrial case study (subject to ongoing research activities) for agile production-based Critical Raw Materials (CRM) recovery. INDEX TERMSLearning from demonstration, human-robot interaction, artificial intelligence, federated learning VOLUME XX, XXXX This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.

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Section: Challenges In Lfd Learningmentioning

confidence: 99%

Towards Open and Expandable Cognitive AI Architectures for Large-Scale Multi-Agent Human-Robot Collaborative Learning

2021

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“…Recent years have seen an increasing amount of work on in the intersection of language, vision and robotics. One line of work particularly focuses on teaching robots new tasks through demonstration and instruction (Rybski et al, 2007;Mohseni-Kabir et al, 2018). Originated in the robotics community, learning from demonstration (LfD) (Thomaz and Cakmak, 2009;Argall et al, 2009) enables robots to learn a mapping from world states to robots' manipulations based on human's demonstration of desired robot behaviors.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Task Learning from Language Instructions with Unified Transformers and Self-Monitoring

Zhang¹,

Chai²

2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Despite recent progress, learning new tasks through language instructions remains an extremely challenging problem. On the AL-FRED benchmark for task learning, the published state-of-the-art system only achieves a task success rate of less than 10% in an unseen environment, compared to the human performance of over 90%. To address this issue, this paper takes a closer look at task learning. In a departure from a widely applied end-toend architecture, we decomposed task learning into three sub-problems: sub-goal planning, scene navigation, and object manipulation; and developed a model HiTUT 1 (stands for Hierarchical Tasks via Unified Transformers) that addresses each sub-problem in a unified manner to learn a hierarchical task structure. On the ALFRED benchmark, HiTUT has achieved the best performance with a remarkably higher generalization ability. In the unseen environment, HiTUT achieves over 160% performance gain in success rate compared to the previous state of the art. The explicit representation of task structures also enables an in-depth understanding of the nature of the problem and the ability of the agent, which provides insight for future benchmark development and evaluation.

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“…Mohseni-Kabir et al (Mohseni-Kabir et al 2018) simultaneously learn task hierarchy and primitives through interactive demonstrations. The demonstrator's narration is leveraged to identify boundaries between primitives and a set of heuristics are used to query the demonstrator regarding the grouping of primitives.…”

Section: Related Workmentioning

confidence: 99%

Learning from Corrective Demonstrations

Gutierrez

Short

Niekum

et al. 2019

2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

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Robots operating in real-world human environments will likely encounter task execution failures. To address this, we would like to allow co-present humans to refine the robot's task model as errors are encountered. Existing approaches to task model modification require reasoning over the entire dataset and model, limiting the rate of corrective updates. We introduce the State-Indexed Task Updates (SITU) algorithm to efficiently incorporate corrective demonstrations into an existing task model by iteratively making local updates that only require reasoning over a small subset of the model. In future work, we will evaluate this approach with a user study.

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Simultaneous learning of hierarchy and primitives for complex robot tasks

Cited by 21 publications

References 25 publications

Towards Open and Expandable Cognitive AI Architectures for Large-Scale Multi-Agent Human-Robot Collaborative Learning

Towards Open and Expandable Cognitive AI Architectures for Large-Scale Multi-Agent Human-Robot Collaborative Learning

Hierarchical Task Learning from Language Instructions with Unified Transformers and Self-Monitoring

Learning from Corrective Demonstrations

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