Search Results for author: Kei Ota

Found 12 papers, 2 papers with code

Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?

1 code implementation ICML 2020 Kei Ota, Tomoaki Oiki, Devesh K. Jha, Toshisada Mariyama, Daniel Nikovski

We believe that stronger feature propagation together with larger networks (and thus larger search space) allows RL agents to learn more complex functions of states and thus improves the sample efficiency.

Decision Making reinforcement-learning +1

Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learning

no code implementations13 Mar 2019 Kei Ota, Devesh K. Jha, Tomoaki Oiki, Mamoru Miura, Takashi Nammoto, Daniel Nikovski, Toshisada Mariyama

Our experiments show that our RL agent trained with a reference path outperformed a model-free PID controller of the type commonly used on many robotic platforms for trajectory tracking.

Motion Planning reinforcement-learning +1

Efficient Exploration in Constrained Environments with Goal-Oriented Reference Path

no code implementations3 Mar 2020 Kei Ota, Yoko SASAKI, Devesh K. Jha, Yusuke Yoshiyasu, Asako Kanezaki

Specifically, we train a deep convolutional network that can predict collision-free paths based on a map of the environment-- this is then used by a reinforcement learning algorithm to learn to closely follow the path.

Efficient Exploration Navigate +2

Deep Reactive Planning in Dynamic Environments

no code implementations31 Oct 2020 Kei Ota, Devesh K. Jha, Tadashi Onishi, Asako Kanezaki, Yusuke Yoshiyasu, Yoko SASAKI, Toshisada Mariyama, Daniel Nikovski

The main novelty of the proposed approach is that it allows a robot to learn an end-to-end policy which can adapt to changes in the environment during execution.

Training Larger Networks for Deep Reinforcement Learning

no code implementations16 Feb 2021 Kei Ota, Devesh K. Jha, Asako Kanezaki

Previous work has shown that this is mostly due to instability during training of deep RL agents when using larger networks.

reinforcement-learning Reinforcement Learning (RL) +1

Object Memory Transformer for Object Goal Navigation

no code implementations24 Mar 2022 Rui Fukushima, Kei Ota, Asako Kanezaki, Yoko SASAKI, Yusuke Yoshiyasu

This paper presents a reinforcement learning method for object goal navigation (ObjNav) where an agent navigates in 3D indoor environments to reach a target object based on long-term observations of objects and scenes.

Navigate Object

H-SAUR: Hypothesize, Simulate, Act, Update, and Repeat for Understanding Object Articulations from Interactions

no code implementations22 Oct 2022 Kei Ota, Hsiao-Yu Tung, Kevin A. Smith, Anoop Cherian, Tim K. Marks, Alan Sullivan, Asako Kanezaki, Joshua B. Tenenbaum

The world is filled with articulated objects that are difficult to determine how to use from vision alone, e. g., a door might open inwards or outwards.

Tactile-Filter: Interactive Tactile Perception for Part Mating

no code implementations10 Mar 2023 Kei Ota, Devesh K. Jha, Hsiao-Yu Tung, Joshua B. Tenenbaum

We evaluate our method on several part-mating tasks with novel objects using a robot equipped with a vision-based tactile sensor.

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

Tactile Estimation of Extrinsic Contact Patch for Stable Placement

no code implementations25 Sep 2023 Kei Ota, Devesh K. Jha, Krishna Murthy Jatavallabhula, Asako Kanezaki, Joshua B. Tenenbaum

In particular, we estimate the contact patch between a grasped object and its environment using force and tactile observations to estimate the stability of the object during a contact formation.

Object

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