5 papers
Asking Smarter Questions: Targeted Active Preference Learning for Underspecified Rewards
Helena Merker, Nick Walker, Andreea Bobu
Reward learning typically assumes the human's input covers everything that matters for the task. In practice, humans can only attend to so much at once, so their teaching naturally…
A Few Words Go a Long Way: Language Guided Robot Policy Synthesis
Daphne Chen, Archit Ritesh Jain, Eric Goossen +4
While vision-language-action models have demonstrated impressive zero-shot manipulation capabilities, they remain fundamentally black box policies that are difficult to interpret,…
Robots That Know What to Ask: Recovering Misaligned Rewards through Targeted Explanations
Helena Merker, Nick Walker, Andreea Bobu
Learning reward functions from demonstrations assumes that demonstrations provide adequate supervision over all features -- or task-relevant aspects of behavior. In practice, demon…
Can Large Language Models Help Developers with Robotic Finite State Machine Modification?
Xiangyu Robin Gan, Yuxin Ray Song, Nick Walker +1
Finite state machines (FSMs) are widely used to manage robot behavior logic, particularly in real-world applications that require a high degree of reliability and structure. Howeve…
Fast Explicit-Input Assistance for Teleoperation in Clutter
Nick Walker, Xuning Yang, Animesh Garg +3
The performance of prediction-based assistance for robot teleoperation degrades in unseen or goal-rich environments due to incorrect or quickly-changing intent inferences. Poor pre…