Sandy H. Huang — accepted AI-conference papers

2024

Mastering Stacking of Diverse Shapes with Large-Scale Iterative Reinforcement Learning on Real Robots

Thomas Lampe, Abbas Abdolmaleki, Sarah Bechtle, Sandy H. Huang, Jost Tobias Springenberg, Michael Bloesch +9

ICRA 2024poster

Reinforcement learning solely from an agent’s self-generated data is often believed to be infeasible for learning on real robots, due to the amount of data needed. However, if done right, agents learning from real data can be surprisingly efficient through re-using previously collected sub-optimal d…

Cited by 6 Source ↗Scholar ↗

2019

Nonverbal Robot Feedback for Human Teachers

Sandy H. Huang, Isabella Huang, Ravi Pandya, Anca D. Dragan

CoRL 2019

Robots can learn preferences from human demonstrations, but their success depends on how informative these demonstrations are. Being informative is unfortunately very challenging, because during teaching, people typically get no transparency into what the robot already knows or has learned so far. I

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2018

Establishing Appropriate Trust via Critical States

Sandy H. Huang, Kush Bhatia, Pieter Abbeel, Anca D. Dragan

IROS 2018poster

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 that particularly challenging. We propose an approach for helping end-users build a mental model of such policies. Our ke…

Cited by 160 Source ↗Scholar ↗

2015

Leveraging appearance priors in non-rigid registration, with application to manipulation of deformable objects

Sandy H. Huang, Jia Pan, George Mulcaire, Pieter Abbeel

IROS 2015poster

Manipulation of deformable objects is a widely applicable but challenging task in robotics. One promising nonparametric approach for this problem is trajectory transfer, in which a non-rigid registration is computed between the starting scene of the demonstration and the scene at test time. This reg…

Cited by 56 Source ↗Scholar ↗