Search Results for author: Sehoon Ha

Found 17 papers, 2 papers with code

Safe Reinforcement Learning for Legged Locomotion

no code implementations5 Mar 2022 Tsung-Yen Yang, Tingnan Zhang, Linda Luu, Sehoon Ha, Jie Tan, Wenhao Yu

In this paper, we propose a safe reinforcement learning framework that switches between a safe recovery policy that prevents the robot from entering unsafe states, and a learner policy that is optimized to complete the task.

reinforcement-learning Safe Reinforcement Learning

Improving Safety in Deep Reinforcement Learning using Unsupervised Action Planning

no code implementations29 Sep 2021 Hao-Lun Hsu, Qiuhua Huang, Sehoon Ha

One of the key challenges to deep reinforcement learning (deep RL) is to ensure safety at both training and testing phases.

Continuous Control reinforcement-learning

Finite State Machine Policies Modulating Trajectory Generator

no code implementations26 Sep 2021 Ren Liu, Nitish Sontakke, Sehoon Ha

Deep reinforcement learning (deep RL) has emerged as an effective tool for developing controllers for legged robots.

Legged Robots

Benchmarking Augmentation Methods for Learning Robust Navigation Agents: the Winning Entry of the 2021 iGibson Challenge

no code implementations22 Sep 2021 Naoki Yokoyama, Qian Luo, Dhruv Batra, Sehoon Ha

Recent advances in deep reinforcement learning and scalable photorealistic simulation have led to increasingly mature embodied AI for various visual tasks, including navigation.

Image Augmentation PointGoal Navigation +1

Success Weighted by Completion Time: A Dynamics-Aware Evaluation Criteria for Embodied Navigation

no code implementations14 Mar 2021 Naoki Yokoyama, Sehoon Ha, Dhruv Batra

Several related works on navigation have used Success weighted by Path Length (SPL) as the primary method of evaluating the path an agent makes to a goal location, but SPL is limited in its ability to properly evaluate agents with complex dynamics.

Error-Aware Policy Learning: Zero-Shot Generalization in Partially Observable Dynamic Environments

no code implementations13 Mar 2021 Visak Kumar, Sehoon Ha, C. Karen Liu

An EAP takes as input the predicted future state error in the target environment, which is provided by an error-prediction function, simultaneously trained with the EAP.

PODS: Policy Optimization via Differentiable Simulation

no code implementations1 Jan 2021 Miguel Angel Zamora Mora, Momchil Peychev, Sehoon Ha, Martin Vechev, Stelian Coros

Current reinforcement learning (RL) methods use simulation models as simple black-box oracles.

A Few Shot Adaptation of Visual Navigation Skills to New Observations using Meta-Learning

no code implementations6 Nov 2020 Qian Luo, Maks Sorokin, Sehoon Ha

Therefore, learning a navigation policy for a new robot with a new sensor configuration or a new target still remains a challenging problem.

Meta-Learning Visual Navigation

Observation Space Matters: Benchmark and Optimization Algorithm

no code implementations2 Nov 2020 Joanne Taery Kim, Sehoon Ha

Recent advances in deep reinforcement learning (deep RL) enable researchers to solve challenging control problems, from simulated environments to real-world robotic tasks.

reinforcement-learning

Learning to be Safe: Deep RL with a Safety Critic

no code implementations27 Oct 2020 Krishnan Srinivasan, Benjamin Eysenbach, Sehoon Ha, Jie Tan, Chelsea Finn

Safety is an essential component for deploying reinforcement learning (RL) algorithms in real-world scenarios, and is critical during the learning process itself.

Transfer Learning

Learning to Walk in the Real World with Minimal Human Effort

no code implementations20 Feb 2020 Sehoon Ha, Peng Xu, Zhenyu Tan, Sergey Levine, Jie Tan

In this paper, we develop a system for learning legged locomotion policies with deep RL in the real world with minimal human effort.

Legged Robots Multi-Task Learning +1

Learning Fast Adaptation with Meta Strategy Optimization

1 code implementation28 Sep 2019 Wenhao Yu, Jie Tan, Yunfei Bai, Erwin Coumans, Sehoon Ha

The key idea behind MSO is to expose the same adaptation process, Strategy Optimization (SO), to both the training and testing phases.

Legged Robots Meta-Learning

Zero-shot Imitation Learning from Demonstrations for Legged Robot Visual Navigation

no code implementations27 Sep 2019 Xinlei Pan, Tingnan Zhang, Brian Ichter, Aleksandra Faust, Jie Tan, Sehoon Ha

Here, we propose a zero-shot imitation learning approach for training a visual navigation policy on legged robots from human (third-person perspective) demonstrations, enabling high-quality navigation and cost-effective data collection.

Disentanglement Imitation Learning +2

Estimating Mass Distribution of Articulated Objects using Non-prehensile Manipulation

no code implementations9 Jul 2019 K. Niranjan Kumar, Irfan Essa, Sehoon Ha, C. Karen Liu

Using our method, we train a robotic arm to estimate the mass distribution of an object with moving parts (e. g. an articulated rigid body system) by pushing it on a surface with unknown friction properties.

Learning to Walk via Deep Reinforcement Learning

no code implementations26 Dec 2018 Tuomas Haarnoja, Sehoon Ha, Aurick Zhou, Jie Tan, George Tucker, Sergey Levine

In this paper, we propose a sample-efficient deep RL algorithm based on maximum entropy RL that requires minimal per-task tuning and only a modest number of trials to learn neural network policies.

Legged Robots reinforcement-learning

Learning a Unified Control Policy for Safe Falling

no code implementations8 Mar 2017 Visak CV Kumar, Sehoon Ha, C. Karen Liu

With this mixture of actor-critic architecture, the discrete contact sequence planning is solved through the selection of the best critics while the continuous control problem is solved by the optimization of actors.

Continuous Control

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