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Ajay Kumar Tanwani

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Publications associées (16)

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Small Variance Asymptotics for Non-Parametric Online Robot Learning

Sylvain Calinon, Ajay Kumar Tanwani

Small variance asymptotics is emerging as a useful technique for inference in large scale Bayesian non-parametric mixture models. This paper analyses the online learning of robot manipulation tasks with Bayesian non-parametric mixture models under small va ...

2019

Small-variance asymptotics for non-parametric online robot learning

Sylvain Calinon, Ajay Kumar Tanwani

Small-variance asymptotics is emerging as a useful technique for inference in large-scale Bayesian non-parametric mixture models. This paper analyzes the online learning of robot manipulation tasks with Bayesian non-parametric mixture models under small-va ...

SAGE PUBLICATIONS LTD2019

Generative Models for Learning Robot Manipulation Skills from Humans

Ajay Kumar Tanwani

A long standing goal in artificial intelligence is to make robots seamlessly interact with humans in performing everyday manipulation skills. Learning from demonstrations or imitation learning provides a promising route to bridge this gap. In contrast to d ...

EPFL2018

Generalizing Robot Imitation Learning with Invariant Hidden Semi-Markov Models

Sylvain Calinon, Ajay Kumar Tanwani, Swati Krishnan

Generalizing manipulation skills to new situations requires extracting invariant patterns from demonstrations. For example, the robot needs to understand the demonstrations at a higher level while being invariant to the appearance of the objects, geometric ...

2018

Underwater manipulation is a challenging problem. The state of the art is dominated by Remotely Operated Vehicles (ROV). ROV operations typically require an offshore crew consisting of at least an intendant, an operator, and a navigator. This crew often ha ...

2018

A Generative Model for Intention Recognition and Manipulation Assistance in Teleoperation

Sylvain Calinon, Ajay Kumar Tanwani

Performing remote manipulation tasks by teleoperation with limited bandwidth, communication delays and environmental differences is a challenging problem. In this paper, we learn a task-parameterized generative model from the teleoperator demonstrations us ...

2017

In this paper, we investigate the semi-tied Gaussian mixture models for robust learning and adaptation of robot manipulation tasks. We make use of the spatial and temporal correlation in the data by tying the covariance matrices of the mixture model with c ...

2016

Online Inference in Bayesian Non-Parametric Mixture Models under Small Variance Asymptotics

Sylvain Calinon, Ajay Kumar Tanwani

Adapting statistical learning models online with large scale streaming data is a challenging problem. Bayesian non-parametric mixture models provide flexibility in model selection, however, their widespread use is limited by the computational overhead of e ...

2016

Dexterous Undersea Interventions with Far Distance Onshore Supervision: the DexROV Project

Sylvain Calinon, Ajay Kumar Tanwani

The operation of a ROV requires significant off-shore dedicated manpower to handle and operate the robotic platform. In order to reduce the burden of operations, DexROV proposes to work out more cost effective and time efficient ROV operations, where manne ...

2016

DexROV: Dexterous Undersea Inspection and Maintenance in Presence of Communication Latencies

Sylvain Calinon, Ajay Kumar Tanwani

Underwater inspection and maintenance (e.g. in the oil & gas industry) are demanding and costly activities for which ROV based setups are often deployed in addition or in substitution to deep divers - contributing to operations risks and costs cutting. How ...

2015