6 papers · 1 filter
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…
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…
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…
SimCoachCorpus: A naturalistic dataset with language and trajectories for embodied teaching
Emily Sumner, Deepak E. Gopinath, Laporsha Dees +9
High-quality curated datasets are essential for training and evaluating AI approaches, but are often lacking in embodied interactive domains where language and physical action are…
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…