Reinforcement learning from human feedback (RLHF) is a technique for tra...
Robot policies need to adapt to human preferences and/or new environment...
Recent works on shared autonomy and assistive-AI technologies, such as
a...
When robots enter everyday human environments, they need to understand t...
How do people build up trust with artificial agents? Here, we study a ke...
Multi-agent interactions are important to model for forecasting other ag...
When humans collaborate with each other, they often make decisions by
ob...
Today's robots are increasingly interacting with people and need to
effi...
Learning from human feedback has shown to be a useful approach in acquir...
Reward learning is a fundamental problem in robotics to have robots that...
Coordination is often critical to forming prosocial behaviors – behavior...
Traffic congestion has large economic and social costs. The introduction...
Preference-based learning of reward functions, where the reward function...
The COVID-19 pandemic has severely affected many aspects of people's dai...
Characterizing what types of exoskeleton gaits are comfortable for users...
Multi-agent safe systems have become an increasingly important area of s...
Autonomous driving has achieved significant progress in recent years, bu...
Reward functions are a common way to specify the objective of a robot. A...
Designing reward functions is a challenging problem in AI and robotics.
...
In order to collaborate safely and efficiently, robots need to anticipat...
Robots can learn the right reward function by querying a human expert.
E...
Road congestion induces significant costs across the world, and road net...
Data collection and labeling is one of the main challenges in employing
...
Autonomous vehicles have the potential to increase the capacity of roads...
We propose a safe exploration algorithm for deterministic Markov Decisio...
Traffic congestion has large economic and social costs. The introduction...
Data generation and labeling are usually an expensive part of learning f...