7 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…
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…
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…
Shared Autonomy for Proximal Teaching
Megha Srivastava, Reihaneh Iranmanesh, Yuchen Cui +6
Motor skill learning often requires experienced professionals who can provide personalized instruction. Unfortunately, the availability of high-quality training can be limited for…
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…