90 citations
- Honda Research Institute, USAUS9 papers
- Carnegie Mellon UniversityUS6 papers
- University of California, BerkeleyUS6 papers
- Boston UniversityUS4 papers
- Duke UniversityUS3 papers
- Honda (Germany)DE3 papers
- Honda (Japan)JP3 papers
- Brown UniversityUS2 papers
- Cornell UniversityUS2 papers
- Massachusetts Institute of TechnologyUS2 papers
- Purdue University West LafayetteUS2 papers
- University of DelawareUS2 papers
9 papers · 1 filter
Strategic Shaping of Human Prosociality: A Latent-State POMDP Framework
Zahra Zahedi, Xinyue Hu, Shashank Mehrotra +2
We propose a decision-theoretic framework in which a robot strategically can shape inferred human's prosocial state during repeated interactions. Modeling the human's prosociality…
Exploring The Impact Of Proactive Generative AI Agent Roles In Time-Sensitive Collaborative Problem-Solving Tasks
Anirban Mukhopadhyay, Kevin Salubre, Hifza Javed +2
Collaborative problem-solving under time pressure is common but difficult, as teams must generate ideas quickly, coordinate actions, and track progress. Generative AI offers new op…
Can we enhance prosocial behavior? Using post-ride feedback to improve micromobility interactions
Sidney T. Scott-Sharoni, Shashank Mehrotra, Kevin Salubre +3
Micromobility devices, such as e-scooters and delivery robots, hold promise for eco-friendly and cost-effective alternatives for future urban transportation. However, their lack of…
DualTake: Predicting Takeovers across Mobilities for Future Personalized Mobility Services
Zhaobo Zheng, Kumar Akash, Teruhisa Misu
A hybrid society is expected to emerge in the near future, with different mobilities interacting together, including cars, micro-mobilities, pedestrians, and robots. People may uti…
The Interaction Gap: A Step Toward Understanding Trust in Autonomous Vehicles Between Encounters
Jacob G. Hunter, Matthew Konishi, Neera Jain +4
Shared autonomous vehicles (SAVs) will be introduced in greater numbers over the coming decade. Due to rapid advances in shared mobility and the slower development of fully autonom…
Identification of Adaptive Driving Style Preference through Implicit Inputs in SAE L2 Vehicles
Zhaobo K. Zheng, Kumar Akash, Teruhisa Misu +4
A key factor to optimal acceptance and comfort of automated vehicle features is the driving style. Mismatches between the automated and the driver preferred driving styles can make…