Search Results for author: Huihan Liu

Found 10 papers, 1 papers with code

Interactive Robot Learning from Verbal Correction

no code implementations26 Oct 2023 Huihan Liu, Alice Chen, Yuke Zhu, Adith Swaminathan, Andrey Kolobov, Ching-An Cheng

A key feature of OLAF is its ability to update the robot's visuomotor neural policy based on the verbal feedback to avoid repeating mistakes in the future.

Language Modelling Large Language Model

Model-Based Runtime Monitoring with Interactive Imitation Learning

no code implementations26 Oct 2023 Huihan Liu, Shivin Dass, Roberto Martín-Martín, Yuke Zhu

Unlike prior work that cannot foresee future failures or requires failure experiences for training, our method learns a latent-space dynamics model and a failure classifier, enabling our method to simulate future action outcomes and detect out-of-distribution and high-risk states preemptively.

Imitation Learning

Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment

no code implementations15 Nov 2022 Huihan Liu, Soroush Nasiriany, Lance Zhang, Zhiyao Bao, Yuke Zhu

To harness the capabilities of state-of-the-art robot learning models while embracing their imperfections, we present Sirius, a principled framework for humans and robots to collaborate through a division of work.

Decision Making

Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation Tasks

1 code implementation7 Oct 2021 Soroush Nasiriany, Huihan Liu, Yuke Zhu

Realistic manipulation tasks require a robot to interact with an environment with a prolonged sequence of motor actions.

reinforcement-learning Reinforcement Learning (RL)

Parrot: Data-Driven Behavioral Priors for Reinforcement Learning

no code implementations ICLR 2021 Avi Singh, Huihan Liu, Gaoyue Zhou, Albert Yu, Nicholas Rhinehart, Sergey Levine

Reinforcement learning provides a general framework for flexible decision making and control, but requires extensive data collection for each new task that an agent needs to learn.

Decision Making reinforcement-learning +1

Design, Benchmarking and Explainability Analysis of a Game-Theoretic Framework towards Energy Efficiency in Smart Infrastructure

no code implementations16 Oct 2019 Ioannis C. Konstantakopoulos, Hari Prasanna Das, Andrew R. Barkan, Shiying He, Tanya Veeravalli, Huihan Liu, Aummul Baneen Manasawala, Yu-Wen Lin, Costas J. Spanos

In this paper, we propose a gamification approach as a novel framework for smart building infrastructure with the goal of motivating human occupants to reconsider personal energy usage and to have positive effects on their environment.

Benchmarking Decision Making

A Novel Graphical Lasso based approach towards Segmentation Analysis in Energy Game-Theoretic Frameworks

no code implementations5 Oct 2019 Hari Prasanna Das, Ioannis C. Konstantakopoulos, Aummul Baneen Manasawala, Tanya Veeravalli, Huihan Liu, Costas J. Spanos

A number of such frameworks have been introduced over the years which formulate the energy saving process as a competitive game with appropriate incentives for energy efficient players.

Decision Making Segmentation

Segmentation Analysis in Human Centric Cyber-Physical Systems using Graphical Lasso

no code implementations24 Oct 2018 Hari Prasanna Das, Ioannis C. Konstantakopoulos, Aummul Baneen Manasawala, Tanya Veeravalli, Huihan Liu, Costas J. Spanos

A generalized gamification framework is introduced as a form of smart infrastructure with potential to improve sustainability and energy efficiency by leveraging humans-in-the-loop strategy.

Decision Making Segmentation

A Deep Learning and Gamification Approach to Energy Conservation at Nanyang Technological University

no code implementations13 Sep 2018 Ioannis C. Konstantakopoulos, Andrew R. Barkan, Shiying He, Tanya Veeravalli, Huihan Liu, Costas Spanos

We propose the design and implementation of a large-scale network game with the goal of improving the energy efficiency of a building through the utilization of cutting-edge Internet of Things (IoT) sensors and cyber-physical systems sensing/actuation platforms.

Discrete Choice Models

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