49 citations · 105 across the 8 of their papers we have counts for
9 papers
Refuses in Chat, Complies with Arms: LLM Refusal Does Not Survive Robot Control
Edward Sun, Christopher Leet, Sravanthi Machcha +4
Frontier language models reliably refuse to explain how to cause physical harm. We ask whether this refusal survives when the same models control a robot. We introduce RoboHarm, a…
Data Quality in Imitation Learning
Suneel Belkhale, Yuchen Cui, Dorsa Sadigh
In supervised learning, the question of data quality and curation has been over-shadowed in recent years by increasingly more powerful and expressive models that can ingest interne…
HYDRA: Hybrid Robot Actions for Imitation Learning
Suneel Belkhale, Yuchen Cui, Dorsa Sadigh
Imitation Learning (IL) is a sample efficient paradigm for robot learning using expert demonstrations. However, policies learned through IL suffer from state distribution shift at…
"No, to the Right" -- Online Language Corrections for Robotic Manipulation via Shared Autonomy
Yuchen Cui, Siddharth Karamcheti, Raj Palleti +3
Systems for language-guided human-robot interaction must satisfy two key desiderata for broad adoption: adaptivity and learning efficiency. Unfortunately, existing instruction-foll…
Masked Imitation Learning: Discovering Environment-Invariant Modalities in Multimodal Demonstrations
Yilun Hao, Ruinan Wang, Zhangjie Cao +3
Multimodal demonstrations provide robots with an abundance of information to make sense of the world. However, such abundance may not always lead to good performance when it comes…
Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation?
Yuchen Cui, Scott Niekum, Abhinav Gupta +2
Task specification is at the core of programming autonomous robots. A low-effort modality for task specification is critical for engagement of non-expert end-users and ultimate ado…
The EMPATHIC Framework for Task Learning from Implicit Human Feedback
Yuchen Cui, Qiping Zhang, Alessandro Allievi +3
Reactions such as gestures, facial expressions, and vocalizations are an abundant, naturally occurring channel of information that humans provide during interactions. A robot or ot…
Uncertainty-Aware Data Aggregation for Deep Imitation Learning
Yuchen Cui, David Isele, Scott Niekum +1
Estimating statistical uncertainties allows autonomous agents to communicate their confidence during task execution and is important for applications in safety-critical domains suc…
Risk-Aware Active Inverse Reinforcement Learning
Daniel S. Brown, Yuchen Cui, Scott Niekum
Active learning from demonstration allows a robot to query a human for specific types of input to achieve efficient learning. Existing work has explored a variety of active query s…