Predicting Slippage and Learning Manipulation Affordances through Gaussian Process Regression
Paper i proceeding, 2013

Object grasping is commonly followed by some form of object manipulation - either when using the grasped object as a tool or actively changing its position in the hand through in-hand manipulation to afford further interaction. In this process, slippage may occur due to inappropriate contact forces, various types of noise and/or due to the unexpected interaction or collision with the environment. In this paper, we study the problem of identifying continuous bounds on the forces and torques that can be applied on a grasped object before slippage occurs. We model the problem as kinesthetic rather than cutaneous learning given that the measurements originate from a wrist mounted force-torque sensor. Given the continuous output, this regression problem is solved using a Gaussian Process approach. We demonstrate a dual armed humanoid robot that can autonomously learn force and torque bounds and use these to execute actions on objects such as sliding and pushing. We show that the model can be used not only for the detection of maximum allowable forces and torques but also for potentially identifying what types of tasks, denoted as manipulation affordances, a specific grasp configuration allows. The latter can then be used to either avoid specific motions or as a simple step of achieving in-hand manipulation of objects through interaction with the environment.


Robot sensing systems





Francisco E B Vina

Kungliga Tekniska Högskolan (KTH)

Yasemin Bekiroglu

Kungliga Tekniska Högskolan (KTH)

Christian Smith

Kungliga Tekniska Högskolan (KTH)

Yiannis Karayiannidis

Kungliga Tekniska Högskolan (KTH)

Danica Kragic

Kungliga Tekniska Högskolan (KTH)

IEEE-RAS International Conference on Humanoid Robots

2164-0572 (ISSN) 2164-0580 (eISSN)


IEEE-RAS International Conference on Humanoid Robots
Atlanta, USA,


Annan data- och informationsvetenskap

Robotteknik och automation

Datorseende och robotik (autonoma system)

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