Publications by Ajay Kumar Tanwani

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

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 ...

Ieee2014

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 ...

IEEE2014

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

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 ...

Ieee2013

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

Ajay Kumar Tanwani

Rewards-Driven Control of Robot Arm by Decoding EEG Signals

Rewards-Driven Control of Robot Arm by Decoding EEG Signals

Rewards-driven control of robot arm by decoding EEG signals

Transfer in Inverse Reinforcement Learning for Multiple Strategies

Transfer in Inverse Reinforcement Learning for Multiple Strategies

Autonomous reinforcement learning with experience replay

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Rewards-Driven Control of Robot Arm by Decoding EEG Signals

Rewards-Driven Control of Robot Arm by Decoding EEG Signals

Transfer in Inverse Reinforcement Learning for Multiple Strategies

Transfer in Inverse Reinforcement Learning for Multiple Strategies

Autonomous reinforcement learning with experience replay

Rewards-driven control of robot arm by decoding EEG signals