Roadmap to Sample-Efficient Real-World Reinforcement Learning Workshop (R2RL)

What makes this workshop unique?

1. ONLY Early-career speakers (students + scientists): The people actually running real-robot RL who are familiar with its bottlenecks through heavy experimentation.
2. A crowdsourced problem board: Placed at the entrance, where attendees propose and vote on problems precluding sample-efficient real-world RL.
3. Breakout sessions: Top-voted, crowdsourced problems formed into interactive audience discussion where a promising solution is discussed and presented by each breakout group.
4. Interactive panel discussion: Both speakers and audience members that led each breakout session discuss each problem and solution.
5. A roadmap whitepaper co-produced during the workshop: The post-workshop paper is constructed from organizers scribing talks, breakout sessions, and the resulting panel discussion. We release this paper for the community to read and learn from the workshop's findings.

Workshop Schedule (Tentative)

Location: TBD, Austin, TX

Date: Monday, November 12, 2026

8:30 - 8:40	Opening, Central Question, and Problem-board Framing
8:40 - 9:50	Keynote Talks
9:50 - 10:00	Problem-board Voting
10:00 - 10:30	Oral Spotlights of Key Contributed Papers
10:30 - 11:00	Poster Session + Coffee Break
11:00 - 11:50	Breakout Sessions on Top-voted Problems
11:50 - 12:20	Panel Discussion with Speakers and Audience Volunteers
12:20 - 12:30	Summary, Whitepaper Next Steps, (Tentative) Workshop Best Paper Prize

Call for Papers

We welcome submissions on sample-efficient real-world RL, especially work that identifies practical bottlenecks, reports lessons learned from real-robot experiments, or proposes methods that reduce the cost of learning on hardware. Topics of interest include, but are not limited to:

Example Topics

Supervision and Reward Cost: Methods for obtaining useful learning signals cheaply, including learned reward models, human feedback, human-in-the-loop interventions, and strategies for avoiding supervision as the bottleneck.
Exploration and Safety on Hardware: Algorithms and systems for efficient exploration under safety constraints, reset-free or autonomous RL, safe online adaptation, and approaches that improve wall-clock efficiency during real-world training.
Leveraging Priors for Real-World RL: Approaches that use pretrained policies, VLAs, offline datasets, world models, or sim-to-real transfer to reduce the number of required on-robot interactions.
Post-Deployment Adaptation: Methods for finetuning deployed policies, improving robustness to distribution shift, learning new behaviors from experience, and adapting generalist policies without catastrophic forgetting.
Long-Horizon and Contact-Rich RL: Techniques for improving sample efficiency in long-horizon manipulation, dexterous control, contact-rich tasks, and settings with delayed rewards, compounding errors, or difficult exploration.
Benchmarks, Metrics, and Lessons Learned: Benchmarks, evaluation protocols, shared platforms, negative results, system-level insights, and analysis of what worked, what failed, and why in real-world RL experiments.

Submission Guidelines

Submission portal: all papers must be submitted through our OpenReview portal.
Page limit: submissions should be 4–8 pages, excluding references and appendix.
Format: submissions must follow the official CoRL paper template and style, and must be properly anonymized for double-blind review.
Dual submission policy: we disallow work already accepted to the main CoRL 2026 conference. Accepted workshop papers will be listed on the website but are non-archival and will not appear in formal proceedings.

Spotlight Talks

All accepted papers will be presented at an in-person poster session. A small number of selected papers will additionally give a 5-minute spotlight talk. Camera-ready versions of all accepted papers will be made available on the workshop website.