hc-robot-learning 2026
Submit Paper
In conjunction with CoRL 2026 · Austin, TX

Human-Centered Robot Learning and Interaction

hc-robot-learning 2026
Toward Reliable and Scalable Autonomy in Human Environments

  • November 9, 2026
  • Austin, Texas, USA
  • In-person
Call for Papers View Schedule
Robots operating in human environments — an office, a home, a hospital, a sidewalk, and a warehouse.
Overview

Robots must work in human environments

Robot learning has made rapid progress on isolated capabilities such as dexterous manipulation, locomotion, navigation, physical reasoning, and increasingly general vision-language-action models. Yet a central premise of real-world deployment is that robots must operate in human environments and around humans: homes, hospitals, offices, warehouses, and public spaces in which people are not only end-users, but also collaborators, supervisors, demonstrators, and bystanders. These humans may introduce ambiguous instructions, changing preferences, social expectations, physical constraints, and safety-critical interruptions that current robot learning systems rarely handle in a reliable and scalable way. Translating today's lab-scale demonstrations into real-world systems that are reliable enough to be trusted, and scalable enough to be deployed across diverse users, tasks, and settings, remains an open challenge that cuts across policy learning, perception, interaction, adaptation, and safety.

Core challenges

Six open problems

1

Scalable data from people

Interaction data at scale and quality, respecting privacy, consent, and effort.

2

Evaluation beyond task success

Benchmarks for safety, social appropriateness, and recovery with a human in the loop.

3

Human-aware foundation models

Grounding VLA models in physical contact, social norms, and human attention.

4

Robustness to human-induced shift

Stable policies under clutter, interruptions, ambiguity, and untrained users.

5

Long-horizon interaction

Turn-taking, intent inference, and repair over minutes-to-hours of shared activity.

6

Continual human feedback

Learning online from preferences and corrections without forgetting or unsafe exploration.

Invited speakers

Speakers

Aude Billard

Aude Billard

EPFL

Full Professor at EPFL, where she heads the Learning Algorithms and Systems Laboratory (LASA). A pioneer of robot learning from demonstration, she develops methods that let robots acquire dexterous manipulation and reactive control skills from human guidance and adapt them safely during close physical interaction with people.

Harold Soh

Harold Soh

National University of Singapore

Associate Professor at the National University of Singapore, where he leads the Collaborative, Learning, and Adaptive Robots (CLeAR) group. His work builds trustworthy interactive robots, spanning trust-aware human-robot interaction, tactile perception, and learning robot policies from human supervision and feedback.

Andreea Bobu

Andreea Bobu

Massachusetts Institute of Technology

Assistant Professor at MIT (AeroAstro and CSAIL). Her research studies how robots can learn the right representations of human intent, combining robot learning, algorithmic human-robot interaction, and learning from human feedback to keep autonomous systems aligned with people's preferences.

Moritz Bächer

Moritz Bächer

Disney Research Zurich

Lab Director for Robotics at Disney Research Zurich. He bridges computational design, differentiable simulation, and learning-based control to create believable robotic characters that move expressively and operate safely alongside people.

Format & schedule

Schedule

0:00–0:15

Opening & live challenge poll

0:15–2:00

Invited talks, clustered

Four 20-min talks + Q&A in two themed clusters with cross-talk.

2:00–2:40

Poster & live-demo session

2:40–3:15

Fishbowl discussion

3:15–3:45

Contributed spotlight talks

3:45–4:00

Closing synthesis & roadmap

Call for papers

Contribute your work

Extended abstracts up to 4 pages (excl. references) in the CoRL format, via OpenReview (single-blind). Non-archival. All accepted papers are presented as posters; 3–4 are selected for spotlight talks. We welcome in-progress work, negative results, system and demo papers, and cross-disciplinary contributions.

Submit Paper
Important dates

Timeline

~6 weeks beforeSubmission deadlineDeadline
~3 weeks beforeDecisions & spotlight notificationsReview
~1 week beforeCamera-ready posters & slidesFinal
November 9, 2026Workshop day @ CoRL 2026, AustinEvent
Organizers

Organizing committee