Publications by Ajay Kumar Tanwani

Small Variance Asymptotics for Non-Parametric Online Robot Learning

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

Dexterous Underwater Manipulation from Distant Onshore Locations

Sylvain Calinon, Ajay Kumar Tanwani

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

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

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

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

Rewards-Driven Control of Robot Arm by Decoding EEG Signals

Aude Billard, José del Rocio Millán Ruiz, Ajay Kumar Tanwani

Decoding the user intention from non-invasive EEG signals is a challenging problem. In this paper, we study the feasibility of predicting the goal for controlling the robot arm in self-paced reaching movements, i.e., spontaneous movements that do not requi ...

2014

Autonomous reinforcement learning with experience replay

Ajay Kumar Tanwani

This paper considers the issues of efficiency and autonomy that are required to make reinforcement learning suitable for real-life control tasks. A real-time reinforcement learning algorithm is presented that repeatedly adjusts the control policy with the ...

Pergamon-Elsevier Science Ltd2013

Transfer in Inverse Reinforcement Learning for Multiple Strategies

Aude Billard, Ajay Kumar Tanwani

We consider the problem of incrementally learning different strategies of performing a complex sequential task from multiple demonstrations of an expert or a set of experts. While the task is the same, each expert differs in his/her way of performing it. W ...

2013