57 citations · 159 across the 13 of their papers we have counts for
18 papers
Assistive Teaching of Motor Control Tasks to Humans
Megha Srivastava, Erdem Biyik, Suvir Mirchandani +2
Recent works on shared autonomy and assistive-AI technologies, such as assistive robot teleoperation, seek to model and help human users with limited ability in a fixed task. Howev…
Learning Preferences for Interactive Autonomy
Erdem Bıyık
When robots enter everyday human environments, they need to understand their tasks and how they should perform those tasks. To encode these, reward functions, which specify the obj…
How do people incorporate advice from artificial agents when making physical judgments?
Erik Brockbank, Haoliang Wang, Justin Yang +4
How do people build up trust with artificial agents? Here, we study a key component of interpersonal trust: people's ability to evaluate the competence of another agent across repe…
Leveraging Smooth Attention Prior for Multi-Agent Trajectory Prediction
Zhangjie Cao, Erdem Bıyık, Guy Rosman +1
Multi-agent interactions are important to model for forecasting other agents' behaviors and trajectories. At a certain time, to forecast a reasonable future trajectory, each agent…
Learning Multimodal Rewards from Rankings
Vivek Myers, Erdem Bıyık, Nima Anari +1
Learning from human feedback has shown to be a useful approach in acquiring robot reward functions. However, expert feedback is often assumed to be drawn from an underlying unimoda…
Learning Reward Functions from Scale Feedback
Nils Wilde, Erdem Bıyık, Dorsa Sadigh +1
Today's robots are increasingly interacting with people and need to efficiently learn inexperienced user's preferences. A common framework is to iteratively query the user about wh…