10 papers
Spatio-Temporal Retrieval-based Priors for Adaptive Computational Teaching in Driving
Deepak Edakkattil Gopinath, Xiongyi Cui, Jonathan DeCastro +2
Learning-based automated coaching systems for complex motor tasks such as high-performance driving remain limited in the ability to be adaptive by their reliance only on local, con…
Proximal State Nudging: Reducing Skill Atrophy from AI Assistance
Megha Srivastava, Jonathan Ouyang, Eric Zhou +6
Skill atrophy, the gradual decline of human capability under AI assistance, poses a safety risk in shared-control of semi-autonomous systems, where operators may be unable to disti…
Learning to Plan, Planning to Learn: Adaptive Hierarchical RL-MPC for Sample-Efficient Decision Making
Toshiaki Hori, Jonathan DeCastro, Deepak Gopinath +2
We propose a new approach for solving planning problems with a hierarchical structure, fusing reinforcement learning and MPC planning. Our formulation tightly and elegantly couples…
On the Strengths and Weaknesses of Data for Open-set Embodied Assistance
Pradyumna Tambwekar, Andrew Silva, Deepak Gopinath +3
Embodied foundation models are increasingly performant in real-world domains such as robotics or autonomous driving. These models are often deployed in interactive or assistive set…
Safety with Agency: Human-Centered Safety Filter with Application to AI-Assisted Motorsports
Donggeon David Oh, Justin Lidard, Haimin Hu +8
We propose a human-centered safety filter (HCSF) for shared autonomy that significantly enhances system safety without compromising human agency. Our HCSF is built on a neural safe…
Estimating cognitive biases with attention-aware inverse planning
Sounak Banerjee, Daphne Cornelisse, Deepak Gopinath +5
People's goal-directed behaviors are influenced by their cognitive biases, and autonomous systems that interact with people should be aware of this. For example, people's attention…