Search Results for author: Devesh Jha

Found 6 papers, 1 papers with code

Style-transfer based Speech and Audio-visual Scene Understanding for Robot Action Sequence Acquisition from Videos

no code implementations27 Jun 2023 Chiori Hori, Puyuan Peng, David Harwath, Xinyu Liu, Kei Ota, Siddarth Jain, Radu Corcodel, Devesh Jha, Diego Romeres, Jonathan Le Roux

This paper introduces a method for robot action sequence generation from instruction videos using (1) an audio-visual Transformer that converts audio-visual features and instruction speech to a sequence of robot actions called dynamic movement primitives (DMPs) and (2) style-transfer-based training that employs multi-task learning with video captioning and weakly-supervised learning with a semantic classifier to exploit unpaired video-action data.

Multi-Task Learning Scene Understanding +3

Learning robot motor skills with mixed reality

no code implementations21 Mar 2022 Eric Rosen, Sreehari Rammohan, Devesh Jha

Mixed Reality (MR) has recently shown great success as an intuitive interface for enabling end-users to teach robots.

Mixed Reality World Knowledge

Quasi-Newton Trust Region Policy Optimization

no code implementations26 Dec 2019 Devesh Jha, Arvind Raghunathan, Diego Romeres

The algorithm has achieved state-of-the-art performance when used in reinforcement learning across a wide range of tasks.

Continuous Control reinforcement-learning +1

Learning Deep Parameterized Skills from Demonstration for Re-targetable Visuomotor Control

2 code implementations23 Oct 2019 Jonathan Chang, Nishanth Kumar, Sean Hastings, Aaron Gokaslan, Diego Romeres, Devesh Jha, Daniel Nikovski, George Konidaris, Stefanie Tellex

We demonstrate that our model trained on 33% of the possible goals is able to generalize to more than 90% of the targets in the scene for both simulation and robot experiments.

Semiparametrical Gaussian Processes Learning of Forward Dynamical Models for Navigating in a Circular Maze

no code implementations13 Sep 2018 Diego Romeres, Devesh Jha, Alberto Dalla Libera, William Yerazunis, Daniel Nikovski

We propose the system presented in the paper as a benchmark problem for reinforcement and robot learning, for its interesting and challenging dynamics and its relative ease of reproducibility.

Friction Gaussian Processes +1

Sim-to-Real Transfer Learning using Robustified Controllers in Robotic Tasks involving Complex Dynamics

no code implementations13 Sep 2018 Jeroen van Baar, Alan Sullivan, Radu Cordorel, Devesh Jha, Diego Romeres, Daniel Nikovski

Another advantage when robots are involved, is that the amount of time a robot is occupied learning a task---rather than being productive---can be reduced by transferring the learned task to the real robot.

Friction Transfer Learning

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