8 papers · 1 filter
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…
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…
Timing the Message: Language-Based Notifications for Time-Critical Assistive Settings
Ya-Chuan Hsu, Jonathan DeCastro, Andrew Silva +1
In time-critical settings such as assistive driving, assistants often rely on alerts or haptic signals to prompt rapid human attention, but these cues usually leave humans to inter…
Think Deep and Fast: Learning Neural Nonlinear Opinion Dynamics from Inverse Dynamic Games for Split-Second Interactions
Haimin Hu, Jaime Fernández Fisac, Naomi Ehrich Leonard +3
Non-cooperative interactions commonly occur in multi-agent scenarios such as car racing, where an ego vehicle can choose to overtake the rival, or stay behind it until a safe overt…
Dreaming to Assist: Learning to Align with Human Objectives for Shared Control in High-Speed Racing
Jonathan DeCastro, Andrew Silva, Deepak Gopinath +4
Tight coordination is required for effective human-robot teams in domains involving fast dynamics and tactical decisions, such as multi-car racing. In such settings, robot teammate…