91 citations · 242 across the 32 of their papers we have counts for
11 papers · 1 filter
Quantifying Assistive Robustness Via the Natural-Adversarial Frontier
Jerry Zhi-Yang He, Zackory Erickson, Daniel S. Brown +1
Our ultimate goal is to build robust policies for robots that assist people. What makes this hard is that people can behave unexpectedly at test time, potentially interacting with…
Bootstrapping Adaptive Human-Machine Interfaces with Offline Reinforcement Learning
Jensen Gao, Siddharth Reddy, Glen Berseth +2
Adaptive interfaces can help users perform sequential decision-making tasks like robotic teleoperation given noisy, high-dimensional command signals (e.g., from a brain-computer in…
Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control
Vivek Myers, Andre He, Kuan Fang +7
Our goal is for robots to follow natural language instructions like "put the towel next to the microwave." But getting large amounts of labeled data, i.e. data that contains demons…
Learning to Model the World with Language
Jessy Lin, Yuqing Du, Olivia Watkins +4
To interact with humans and act in the world, agents need to understand the range of language that people use and relate it to the visual world. While current agents can learn to e…
Contextual Reliability: When Different Features Matter in Different Contexts
Gaurav Ghosal, Amrith Setlur, Daniel S. Brown +2
Deep neural networks often fail catastrophically by relying on spurious correlations. Most prior work assumes a clear dichotomy into spurious and reliable features; however, this i…
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Stephen Casper, Xander Davies, Claudia Shi +29
Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals. RLHF has emerged as the central method used to finetune state-of…