166 citations · 441 across the 13 of their papers we have counts for
6 papers · 1 filter
Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning
Tuomas Haarnoja, Ben Moran, Guy Lever +25
We investigate whether Deep Reinforcement Learning (Deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be compo…
Nonverbal Robot Feedback for Human Teachers
Sandy H. Huang, Isabella Huang, Ravi Pandya +1
Robots can learn preferences from human demonstrations, but their success depends on how informative these demonstrations are. Being informative is unfortunately very challenging,…
Learning Gentle Object Manipulation with Curiosity-Driven Deep Reinforcement Learning
Sandy H. Huang, Martina Zambelli, Jackie Kay +4
Robots must know how to be gentle when they need to interact with fragile objects, or when the robot itself is prone to wear and tear. We propose an approach that enables deep rein…
Establishing Appropriate Trust via Critical States
Sandy H. Huang, Kush Bhatia, Pieter Abbeel +1
In order to effectively interact with or supervise a robot, humans need to have an accurate mental model of its capabilities and how it acts. Learned neural network policies make t…
Expressing Robot Incapability
Minae Kwon, Sandy H. Huang, Anca D. Dragan
Our goal is to enable robots to express their incapability, and to do so in a way that communicates both what they are trying to accomplish and why they are unable to accomplish it…
Enabling Robots to Communicate their Objectives
Sandy H. Huang, David Held, Pieter Abbeel +1
The overarching goal of this work is to efficiently enable end-users to correctly anticipate a robot's behavior in novel situations. Since a robot's behavior is often a direct resu…