Dorsa Sadigh

Asking Easy Questions: A User-Friendly Approach to Active Reward Learning

2019 · arXiv (Cornell University)

Robots can learn the right reward function by querying a human expert. Existing approaches attempt to choose questions where the robot is most uncertain about the human's response; however, they do not consider how easy …

Learning Adaptive Language Interfaces through Decomposition

2020 · arXiv (Cornell University)

Our goal is to create an interactive natural language interface that efficiently and reliably learns from users to complete tasks in simulated robotics settings. We introduce a neural semantic parsing system that learns new high-level …

Transfer Reinforcement Learning across Homotopy Classes

2021 · arXiv (Cornell University)

The ability for robots to transfer their learned knowledge to new tasks -- where data is scarce -- is a fundamental challenge for successful robot learning. While fine-tuning has been well-studied as a simple but …

Influencing Towards Stable Multi-Agent Interactions

2021 · 5th Annual Conference on Robot Learning

Learning in multi-agent environments is difficult due to the non-stationarity introduced by an opponent's or partner's changing behaviors. Instead of reactively adapting to the other agent's (opponent or partner) behavior, we propose an algorithm to …

Data Quality in Imitation Learning

2023 · arXiv (Cornell University)

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 internet-scale data. However, in offline learning for robotics, we …

Data Retrieval with Importance Weights for Few-Shot Imitation Learning

2025 · arXiv (Cornell University)

While large-scale robot datasets have propelled recent progress in imitation learning, learning from smaller task specific datasets remains critical for deployment in new environments and unseen tasks. One such approach to few-shot imitation learning is …