95 citations · 378 across the 58 of their papers we have counts for
6 papers · 1 filter
Fix the Mind, Not the Move: Interpretable AI Assistance via Knowledge-Gap Localization
Ayano Hiranaka, Ya-Chuan Hsu, Stefanos Nikolaidis +2
AI assistants in human-AI collaboration often correct suboptimal human actions through behavioral feedback (e.g., alerts or steering-wheel nudges in assistive driving). Such interv…
Causally Robust Reward Learning from Reason-Augmented Preference Feedback
Minjune Hwang, Yigit Korkmaz, Daniel Seita +1
Preference-based reward learning is widely used for shaping agent behavior to match a user's preference, yet its sparse binary feedback makes it especially vulnerable to causal con…
RoboCLIP: One Demonstration is Enough to Learn Robot Policies
Sumedh A Sontakke, Jesse Zhang, Sébastien M. R. Arnold +5
Reward specification is a notoriously difficult problem in reinforcement learning, requiring extensive expert supervision to design robust reward functions. Imitation learning (IL)…
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…
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…
Multi-Agent Safe Planning with Gaussian Processes
Zheqing Zhu, Erdem Bıyık, Dorsa Sadigh
Multi-agent safe systems have become an increasingly important area of study as we can now easily have multiple AI-powered systems operating together. In such settings, we need to…