Automatic learning of pushing strategy for delivery of irregular-shaped objects

Lau, Manfred; Mitani, Jun; Igarashi, Takeo

doi:10.1109/icra.2011.5979740

Cited by 23 publications

(13 citation statements)

References 13 publications

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“…More recently, data-driven models [17,7,18,19,20] have been proposed as an alternative approach to analysis. Studies are still incipient and either do not offer sufficient generality, or do not address variability.…”

Section: Introductionmentioning

confidence: 99%

More than a million ways to be pushed. A high-fidelity experimental dataset of planar pushing

Bauzá

Fazeli

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

147

164

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Pushing is a motion primitive useful to handle objects that are too large, too heavy, or too cluttered to be grasped. It is at the core of much of robotic manipulation, in particular when physical interaction is involved. It seems reasonable then to wish for robots to understand how pushed objects move.In reality, however, robots often rely on approximations which yield models that are computable, but also restricted and inaccurate. Just how close are those models? How reasonable are the assumptions they are based on? To help answer these questions, and to get a better experimental understanding of pushing, we present a comprehensive and high-fidelity dataset of planar pushing experiments. The dataset contains timestamped poses of a circular pusher and a pushed object, as well as forces at the interaction. We vary the push interaction in 6 dimensions: surface material, shape of the pushed object, contact position, pushing direction, pushing speed, and pushing acceleration. An industrial robot automates the data capturing along precisely controlled position-velocity-acceleration trajectories of the pusher, which give dense samples of positions and forces of uniform quality.We finish the paper by characterizing the variability of friction, and evaluating the most common assumptions and simplifications made by models of frictional pushing in robotics.

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Section: Introductionmentioning

confidence: 99%

More than a million ways to be pushed. A high-fidelity experimental dataset of planar pushing

Bauzá

Fazeli

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

147

164

View full text Add to dashboard Cite

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“…Since pushing offers such a dramatic expansion of manipulation skills, there have been extensive research on the fundamental mechanics of pushing [14][15][16][17] and on the planning of pushing operations [17,18]. Recently, there has been interest in generating push trajectories using sampling based planners [19,20] and learning methods [21]. Most of the work described above employs pushing as an open-loop operation.…”

Section: Related Workmentioning

confidence: 99%

Pose estimation for contact manipulation with manifold particle filters

Koval

Doğar

Pollard

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-We investigate the problem of estimating the state of an object during manipulation. Contact sensors provide valuable information about the object state during actions which involve persistent contact, e.g. pushing. However, contact sensing is very discriminative by nature, and therefore the set of object states which contact a sensor constitutes a lowerdimensional manifold in the state space of the object. This causes stochastic state estimation methods such as particle filters to perform poorly when contact sensors are used. We propose a new algorithm, the manifold particle filter, which uses dual particles directly sampled from the contact manifold to avoid this problem. The algorithm adapts to the probability of contact by dynamically changing the number of dual particles sampled from the manifold. We compare our algorithm to the particle filter through extensive experiments and we show that our algorithm is both faster and better at estimating the state. Our algorithm's performance improves with increasing sensor accuracy and the filter's update rate. We implement the algorithm on a real robot using a force/torque sensor and strain gauges to track the pose of a pushed object.

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“…Over the years, several works have applied data-driven techniques to the problem of planar pushing [12,13,14,15,16]. Most recently, Zhou et al [16] presented a data-driven but physics-inspired model for planar friction.…”

Section: Related Workmentioning

confidence: 99%

A probabilistic data-driven model for planar pushing

Bauzá

Rodríguez

2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-This paper presents a data-driven approach to model planar pushing interaction to predict both the most likely outcome of a push and its expected variability. The learned models rely on a variation of Gaussian processes with inputdependent noise called Variational Heteroscedastic Gaussian processes (VHGP) [1] that capture the mean and variance of a stochastic function. We show that we can learn accurate models that outperform analytical models after less than 100 samples and saturate in performance with less than 1000 samples. We validate the results against a collected dataset of repeated trajectories, and use the learned models to study questions such as the nature of the variability in pushing, and the validity of the quasi-static assumption.

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Automatic learning of pushing strategy for delivery of irregular-shaped objects

Cited by 23 publications

References 13 publications

More than a million ways to be pushed. A high-fidelity experimental dataset of planar pushing

More than a million ways to be pushed. A high-fidelity experimental dataset of planar pushing

Pose estimation for contact manipulation with manifold particle filters

A probabilistic data-driven model for planar pushing

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