Improving robot manipulation with datadriven obje

Authors: Advait Jain Charles C Kemp

Publish Date: 2013/06/19

Volume: 35, Issue: 2-3, Pages: 143-159

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Abstract

Based on a lifetime of experience people anticipate the forces associated with performing a manipulation task In contrast most robots lack common sense about the forces involved in everyday manipulation tasks In this paper we present datadriven methods to inform robots about the forces that they are likely to encounter when performing specific tasks In the context of door opening we demonstrate that datadriven objectcentric models can be used to haptically recognize specific doors haptically recognize classes of door eg refrigerator vs kitchen cabinet and haptically detect anomalous forces while opening a door even when opening a specific door for the first time We also demonstrate that two distinct robots can use forces captured from people opening doors to better detect anomalous forces These results illustrate the potential for robots to use shared databases of forces to better manipulate the world and attain common sense about everyday forcesLittle is known about the statistics of realworld forces associated with everyday tasks in human environments While vast quantities of everyday auditory and visual data are publicly available for use by humans and machines publicly available haptic data is much less common Capturing and modeling the forces associated with everyday tasks could benefit robots by enabling them to better interact with the physical worldFor example despite progress towards service robots that autonomously open doors and drawers the answers to basic questions have been unclear such as “How hard does a robot need to pull to open most doors” Given the wide variation in the forces required to initially open a door eg sim 60 N for a springloaded door and 5 N for a kitchen cabinet a robot without common sense about everyday forces risks damaging a locked cabinet or giving up prematurely on a functioning springloaded door Likewise while opening a door for someone distinguishing forces due to the door opening properly versus the door being in contact with the person could improve safetyWithin this paper we present datadriven methods to inform robots about the forces that they are likely to encounter when performing specific tasks We focus on the example task of pulling open a door As Sect 24 discusses dooropening robots have lacked compelling ways to deal with many common situations such as a door that is locked blocked or damaged In this paper we provide evidence that models of realworld forces can be used by robots to better handle these situationsTaskspecific Each model is specific to a narrowly defined manipulation task to reduce the complexity of the model and the data requirements For this paper we defined the task to be smoothly and slowly pulling open a door with contact restricted to the handle For our data the average linear velocity of the door handle while pulling open a door in a single trial was between 46 and 794 cm/s The mean of this average velocity across all trials with all doors was 182 cm/s and the median was 144 cm/sDatadriven The models directly use forces points of application of the forces and kinematics captured during realworld performance of the task With a datadriven approach we intend to capture the natural variation that a robot will encounter For this paper we used data captured while opening 26 doors in 6 homes and one office in Atlanta GA USAObjectcentric Each model relates the relevant state of the manipulated object to the relevant forces applied to the object to make the models independent of the robot or human manipulating the object For this paper the models are quasistatic They relate the opening angle of the door to the component of the applied force that is tangential to the trajectory of the point of contact on the door handle Our choice of objectcentric representations is intended to make the models useful for distinct robots and methods of manipulation For example it should not matter how the robot applies the forces whether with its left hand its right hand or some other part of its bodySection 2 discusses related work and contrasts it with our approach The next three sections present our contributions to three areas First Sect 3 describes our methods of collecting force data from robots and humans a quasistatic model for doors and the objectcentric representation that we use

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