This workshop focuses on the "Human-to-Robot" (H2R) challenge, bringing together researchers to tackle the core obstacles hindering effective learning from human data. We structure these challenges around four interconnected themes:

• Sensorizing Humans: To learn from humans, we must first capture the richness of their embodied experience. This requires advancements in unobtrusive, multimodal sensing technologies (beyond standard vision/audio to include touch, force, haptics, motion, gaze, and physiological signals) and methods for large-scale, long-term data acquisition in naturalistic settings. This pillar focuses on creating the comprehensive data foundation necessary for H2R.

• Modeling Human Behavior: To effectively leverage rich, in-the-wild human data for robot learning, we must first develop a deeper understanding of the humans generating it. Raw sensor data alone has thus far proven insufficient because existing learning algorithms often struggle to capture the nuances, underlying intent, long-term goals, and complex decision-making processes inherent in natural human behavior observed outside constrained lab settings.

• Robot Learning from Human Data: With rich data and human models, the next step is enabling robots to effectively learn skills. This requires robust algorithms for imitation learning, learning from observation, affordance grounding, and policy transfer that can handle the variability and scale of real-world human data. The focus is on translating the understanding gleaned from human data into tangible robot skills, particularly for complex, long-horizon tasks.

• Leveraging Human Understanding for Better Human-Robot Interaction: Beyond offline skill learning, a deeper understanding of human state, intent, attention, and non-verbal cues (derived from advanced sensing and modeling) is critical for robots designed to work alongside people. This theme explores how insights from human data can enable robots to interact more safely, intuitively, predictably, and collaboratively in shared environments.

Best Paper Awards

The workshop best paper awards are generously sponsored by Meta (Project Aria).

Speakers

Organizers