Improving robot manipulation with data-driven object-centric models of everyday forces

Jain, Abhinav; Kemp, Charles C.

doi:10.1007/s10514-013-9344-1

Cited by 24 publications

(17 citation statements)

References 53 publications

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“…The demonstration is recorded and expressed from an objectcentric viewpoint. The object-centric viewpoint (Okamura et al, 2000;Jain and Kemp, 2013;Li et al, 2014) centers the representation of the manipulation task on the manipulated object, rather than on the robot. This suggests that the goal of a manipulation task is to produce a desired object movement rather than a robot end-effector movement.…”

Section: Learning Manipulation Tasksmentioning

confidence: 99%

A modular approach to learning manipulation strategies from human demonstration

Huang

Souza

et al. 2015

Auton Robot

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Object manipulation is a challenging task for robotics, as the physics involved in object interaction is complex and hard to express analytically. Here we introduce a modular approach for learning a manipulation strategy from human demonstration. Firstly we record a human performing a task that requires an adaptive control strategy in different conditions, i.e. different task contexts. We then perform modular decomposition of the control strategy, using phases of the recorded actions to guide segmentation. Each module represents a part of the strategy, encoded as a pair of forward and inverse models. All modules contribute to the final control policy; their recommendations are integrated via a system of weighting based on their own estimated error in the current task context. We validate our approach by demonstrating it, both in a simulation for clarity, and on a real robot platform to demonstrate robustness and capacity to generalise. The robot task is opening bottle caps. We show that our approach can modularize an adaptive control strat-

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Section: Learning Manipulation Tasksmentioning

confidence: 99%

A modular approach to learning manipulation strategies from human demonstration

Huang

Souza

et al. 2015

Auton Robot

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“…Hawkins et al used contact forces from participants wiping and shaving to create task models to inform a robot of acceptable and anomalous force characteristics when performing the task [1]. Jain and Kemp present a method for improving robot manipulation by creating datadriven object-centric force models of tasks [16]. For this paper, we use a simple kinematic task model that represents a task as a sparse set of goal poses for the robot's end effector.…”

Section: Related Workmentioning

confidence: 99%

Task-centric selection of robot and environment initial configurations for assistive tasks

Kapusta

Park

Kemp

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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When a mobile manipulator functions as an assistive device, the robot's initial configuration and the configuration of the environment can impact the robot's ability to provide effective assistance. Selecting initial configurations for assistive tasks can be challenging due to the high number of degrees of freedom of the robot, the environment, and the person, as well as the complexity of the task. In addition, rapid selection of initial conditions can be important, so that the system will be responsive to the user and will not require the user to wait a long time while the robot makes a decision. To address these challenges, we present Task-centric initial Configuration Selection (TCS), which unlike previous work uses a measure of task-centric manipulability to accommodate state estimation error, considers various environmental degrees of freedom, and can find a set of configurations from which a robot can perform a task. TCS performs substantial offline computation, so that it can rapidly provide solutions at run time. At run time, the system performs an optimization over candidate initial configurations using a utility function that can include factors such as movement costs for the robot's mobile base. To evaluate TCS, we created models of 11 activities of daily living (ADLs) and evaluated TCS's performance with these 11 assistive tasks in a computer simulation of a PR2, a robotic bed, and a model of a human body. TCS performed as well or better than a baseline algorithm in all of our tests against state estimation error.

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“…In another class of work, Jain and Kemp [6] propose the use of shared haptic data to identify specific instances of an object type while manipulating it. They demonstrate their method on the task of identifying a specific door instance, such as a cabinet door or a refrigerator door, by using a datadriven approach.…”

Section: Related Workmentioning

confidence: 99%

Task-oriented planning for manipulating articulated mechanisms under model uncertainty

Narayanan

Likhachev

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Personal robots need to manipulate a variety of articulated mechanisms as part of day-to-day tasks. These tasks are often specific, goal-driven, and permit very little bootstrap time for learning the articulation type. In this work, we address the problem of purposefully manipulating an articulated object, with uncertainty in the type of articulation. To this end, we provide two primary contributions: first, an efficient planning algorithm that, given a set of candidate articulation models, is able to correctly identify the underlying model and simultaneously complete a task; and second, a representation for articulated objects called the Generalized Kinematic Graph (GK-Graph), that allows for modeling complex mechanisms whose articulation varies as a function of the state space. Finally, we provide a practical method to auto-generate candidate articulation models from RGB-D data and present extensive results on the PR2 robot to demonstrate the utility of our representation and the efficiency of our planner.

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Improving robot manipulation with data-driven object-centric models of everyday forces

Cited by 24 publications

References 53 publications

A modular approach to learning manipulation strategies from human demonstration

A modular approach to learning manipulation strategies from human demonstration

Task-centric selection of robot and environment initial configurations for assistive tasks

Task-oriented planning for manipulating articulated mechanisms under model uncertainty

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