Latent Emission-Augmented Perspective-Taking (LEAPT) for Human-Robot Interaction
We construct a decomposed latent state space model for perspective-taking for human robot interaction.
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We construct a decomposed latent state space model for perspective-taking for human robot interaction.
We explore the potential of LLMs to act as zero-shot human models for HRI. We contribute an empirical study and case studies on a simulated table-clearing task and a new robot utensil-passing experiment.
We propose a sampling-based grasp synthesis framework, GraspFlow, to generate context-specific grasps. We test GraspFlow in a real-world table-top grasping task.
Congratulations Sreejith on successfully defending his thesis! Sreejith’s work was on value alignment in human-centric AI/robots. Check out his Neurips pap...
Harold has been awarded an Early Career Spotlight at the Robotics: Science and Systems 2023. The Early Career Spotlight “* acknowledges the outstanding acc...
We develop a family of stable continuous-time neural state space-models.
Xie Yaqi successfully defended her thesis and is now Dr. Xie. Congratulations Yaqi! You can find out more about Yaqi’s work on embedding symbolic knowledg...