Search Results for author: Henry Zhu

Found 8 papers, 4 papers with code

Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner

no code implementations18 May 2022 Danilo Ribeiro, Shen Wang, Xiaofei Ma, Rui Dong, Xiaokai Wei, Henry Zhu, Xinchi Chen, Zhiheng Huang, Peng Xu, Andrew Arnold, Dan Roth

Our model is able to explain a given hypothesis by systematically generating a step-by-step explanation from textual premises.

Question Answering

Learning to be Fair: A Consequentialist Approach to Equitable Decision-Making

1 code implementation18 Sep 2021 Alex Chohlas-Wood, Madison Coots, Henry Zhu, Emma Brunskill, Sharad Goel

In the dominant paradigm for designing equitable machine learning systems, one works to ensure that model predictions satisfy various fairness criteria, such as parity in error rates across race, gender, and other legally protected traits.

Decision Making Fairness

The Ingredients of Real World Robotic Reinforcement Learning

no code implementations ICLR 2020 Henry Zhu, Justin Yu, Abhishek Gupta, Dhruv Shah, Kristian Hartikainen, Avi Singh, Vikash Kumar, Sergey Levine

The success of reinforcement learning in the real world has been limited to instrumented laboratory scenarios, often requiring arduous human supervision to enable continuous learning.

reinforcement-learning

The Ingredients of Real-World Robotic Reinforcement Learning

no code implementations27 Apr 2020 Henry Zhu, Justin Yu, Abhishek Gupta, Dhruv Shah, Kristian Hartikainen, Avi Singh, Vikash Kumar, Sergey Levine

In this work, we discuss the elements that are needed for a robotic learning system that can continually and autonomously improve with data collected in the real world.

reinforcement-learning

ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots

1 code implementation25 Sep 2019 Michael Ahn, Henry Zhu, Kristian Hartikainen, Hugo Ponte, Abhishek Gupta, Sergey Levine, Vikash Kumar

ROBEL introduces two robots, each aimed to accelerate reinforcement learning research in different task domains: D'Claw is a three-fingered hand robot that facilitates learning dexterous manipulation tasks, and D'Kitty is a four-legged robot that facilitates learning agile legged locomotion tasks.

Continuous Control reinforcement-learning

Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost

no code implementations14 Oct 2018 Henry Zhu, Abhishek Gupta, Aravind Rajeswaran, Sergey Levine, Vikash Kumar

Dexterous multi-fingered robotic hands can perform a wide range of manipulation skills, making them an appealing component for general-purpose robotic manipulators.

reinforcement-learning

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