Robot learning from demonstration by constructing skill trees

Konidaris, George; Kuindersma, Scott; Grupen, Roderic A.; Barto, Andrew G.

doi:10.1177/0278364911428653

Cited by 262 publications

(198 citation statements)

References 30 publications

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“…There is some recent work in interpreting human actions and interaction with objects [25,1,17] in context of learning to perform actions from demonstrations. Lopes et al [25] use context from objects in terms of possible grasp a↵ordances to focus the attention of their recognition system.…”

Section: Related Workmentioning

confidence: 99%

Anticipatory Planning for Human-Robot Teams

Koppula

Jain

Saxena

2015

Experimental Robotics

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Abstract. When robots work alongside humans for performing collaborative tasks, they need to be able to anticipate human's future actions and plan appropriate actions. The tasks we consider are performed in contextually-rich environments containing objects, and there is a large variation in the way humans perform these tasks. We use a graphical model to represent the state-space, where we model the humans through their low-level kinematics as well as their high-level intent, and model their interactions with the objects through physically-grounded object a↵ordances. This allows our model to anticipate a belief about possible future human actions, and we model the human's and robot's behavior through an MDP in this rich state-space. We further discuss that due to perception errors and the limitations of the model, the human may not take the optimal action and therefore we present robot's anticipatory planning with di↵erent behaviors of the human within the model's scope. In experiments on Cornell Activity Dataset, we show that our method performs better than various baselines for collaborative planning.

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Section: Related Workmentioning

confidence: 99%

Anticipatory Planning for Human-Robot Teams

Koppula

Jain

Saxena

2015

Experimental Robotics

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“…Aldoma et al [2] proposed a method to find affordances which depends solely on the objects of interest and their position and orientation in the scene. There is some recent work in interpreting human actions and interaction with objects [26,1,20] in context of learning to perform actions from demonstrations. Lopes et al [26] use context from objects in terms of possible grasp affordances to focus the attention of their recognition system.…”

Section: Related Workmentioning

confidence: 99%

Physically Grounded Spatio-temporal Object Affordances

Koppula

Saxena

2014

Lecture Notes in Computer Science

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Abstract. Objects in human environments support various functionalities which govern how people interact with their environments in order to perform tasks. In this work, we discuss how to represent and learn a functional understanding of an environment in terms of object affordances. Such an understanding is useful for many applications such as activity detection and assistive robotics. Starting with a semantic notion of affordances, we present a generative model that takes a given environment and human intention into account, and grounds the affordances in the form of spatial locations on the object and temporal trajectories in the 3D environment. The probabilistic model also allows uncertainties and variations in the grounded affordances. We apply our approach on RGB-D videos from Cornell Activity Dataset, where we first show that we can successfully ground the affordances, and we then show that learning such affordances improves performance in the labeling tasks.

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“…Clustering was also used to cluster subgoals to prevent the creation of multiple options that all correspond to the same underlying skill (Niekum and Barto 2011). Konidaris and colleagues (Konidaris and Barto 2009b, Konidaris et al 2011a, 2012b illustrated the utility of setting the goal of an option to be reaching the initiation set of an already-formed option in a process called "skill chaining." This method is used in the example described in Section 5.1 below.…”

Section: Hierarchical Reinforcement Learningmentioning

confidence: 99%

Behavioral Hierarchy: Exploration and Representation

Barto

Konidaris

Vigorito

2013

Computational and Robotic Models of the Hierarchical Organization of Behavior

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Behavioral modules are units of behavior providing reusable building blocks that can be composed sequentially and hierarchically to generate extensive ranges of behavior. Hierarchies of behavioral modules facilitate learning complex skills and planning at multiple levels of abstraction and enable agents to incrementally improve their competence for facing new challenges that arise over extended periods of time. This chapter focusses on two features of behavioral hierarchy that appear to be less well recognized: its influence on exploratory behavior and the opportunity it affords to reduce the representational challenges of planning and learning in large, complex domains. Four computational examples are described that use methods of hierarchical reinforcement learning to illustrate the influence of behavioral hierarchy on exploration and representation. Beyond illustrating these features, the examples provide support for the central role of behavioral hierarchy in development and learning for both artificial and natural agents.

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Robot learning from demonstration by constructing skill trees

Cited by 262 publications

References 30 publications

Anticipatory Planning for Human-Robot Teams

Anticipatory Planning for Human-Robot Teams

Physically Grounded Spatio-temporal Object Affordances

Behavioral Hierarchy: Exploration and Representation

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