Search Results for author: Avinash Ummadisingu

Found 6 papers, 2 papers with code

SAID-NeRF: Segmentation-AIDed NeRF for Depth Completion of Transparent Objects

no code implementations28 Mar 2024 Avinash Ummadisingu, Jongkeum Choi, Koki Yamane, Shimpei Masuda, Naoki Fukaya, Kuniyuki Takahashi

Acquiring accurate depth information of transparent objects using off-the-shelf RGB-D cameras is a well-known challenge in Computer Vision and Robotics.

Depth Completion Depth Estimation +3

Cluttered Food Grasping with Adaptive Fingers and Synthetic-Data Trained Object Detection

no code implementations10 Mar 2022 Avinash Ummadisingu, Kuniyuki Takahashi, Naoki Fukaya

To address this problem, we propose a method that trains purely on synthetic data and successfully transfers to the real world using sim2real methods by creating datasets of filled food trays using high-quality 3d models of real pieces of food for the training instance segmentation models.

Instance Segmentation object-detection +2

The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors

no code implementations26 Jan 2021 William H. Guss, Mario Ynocente Castro, Sam Devlin, Brandon Houghton, Noboru Sean Kuno, Crissman Loomis, Stephanie Milani, Sharada Mohanty, Keisuke Nakata, Ruslan Salakhutdinov, John Schulman, Shinya Shiroshita, Nicholay Topin, Avinash Ummadisingu, Oriol Vinyals

Although deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples, affording only a shrinking segment of the AI community access to their development.

Decision Making Efficient Exploration +3

Distributed Reinforcement Learning of Targeted Grasping with Active Vision for Mobile Manipulators

no code implementations16 Jul 2020 Yasuhiro Fujita, Kota Uenishi, Avinash Ummadisingu, Prabhat Nagarajan, Shimpei Masuda, Mario Ynocente Castro

Developing personal robots that can perform a diverse range of manipulation tasks in unstructured environments necessitates solving several challenges for robotic grasping systems.

reinforcement-learning Reinforcement Learning (RL) +1

Hindsight policy gradients

1 code implementation ICLR 2019 Paulo Rauber, Avinash Ummadisingu, Filipe Mutz, Juergen Schmidhuber

A reinforcement learning agent that needs to pursue different goals across episodes requires a goal-conditional policy.

Policy Gradient Methods reinforcement-learning +2

ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation

1 code implementation13 Nov 2017 Ritabrata Dutta, Marcel Schoengens, Lorenzo Pacchiardi, Avinash Ummadisingu, Nicole Widmer, Jukka-Pekka Onnela, Antonietta Mira

Further, ABCpy enables ABC experts to easily develop new inference schemes and evaluate them in a standardized environment and to extend the library with new algorithms.

Computation

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