Workshop on Reinforcement Learning in the Era of Imitation Learning
Overview
Imitation learning (IL) has rapidly become the dominant paradigm for training robot policies, powering recent advances in dexterous manipulation, household tasks, and large-scale foundation models. Despite this success, IL methods face persistent challenges in data efficiency, robustness, and generalization to unseen conditions.
Reinforcement learning (RL), with its emphasis on exploration and reward optimization, offers complementary strengths that could help address these limitations—but its integration with IL remains an open problem.
How can reinforcement learning improve the real-world performance of robot policies, especially when complementing imitation learning?
This workshop aims to convene leading researchers from academia and industry to examine challenges and opportunities at this intersection, including efficient fine-tuning of pretrained policies, uncertainty-aware and human-in-the-loop adaptation, the use of real-to-sim digital twins for continuous policy improvement, and benchmarks for real-world evaluation.
Important Dates
- Submission Opens: Feb 10, 2026
- Paper Submission Deadline: Mar 20, 2026
- Notification of Acceptance: TBD
- Camera Ready: TBD
- Workshop Date: June 1, 2026
Schedule
| Time | Session |
|---|---|
| 08:45–09:00 | Opening remarks (organizers) |
| 09:00–09:30 | Invited Talk: Sergey Levine |
| 09:30–10:00 | Invited Talk: Chelsea Finn |
| 10:00–10:30 | Lightning Talks I for Accepted Papers (3–5 minutes each) |
| 10:30–11:00 | Coffee break + Posters I |
| 11:00–11:30 | Invited Talk: Robert Platt |
| 11:30–12:00 | Invited Talk: Pulkit Agrawal |
| 12:00–12:30 | Panel Discussion: "Reinforce or Imitate: Practical Challenges in RL with Real World Data" |
| 12:30–14:00 | Lunch |
| 14:00–14:30 | Invited Talk: Jason Ma |
| 14:30–15:00 | Invited Talk: Georgia Chalvatzaki |
| 15:00–15:30 | Invited Talk: David Held |
| 15:30–16:00 | Lightning Talks II for Accepted Papers (3–5 minutes each) |
| 16:00–16:30 | Coffee break + Posters II |
| 16:30–17:00 | Award Announcement and Closing Remarks |
Call for Papers
We invite submissions of research works in the following domains, including but not limited to:
- Reinforcement Learning Fine-tuning
- Reward learning / Preference Learning
- Imitation Learning Based Policy Fine-tuning
- Real2Sim for Continuous Policy Improvement
- Benchmark Design for Real-World Evaluation
Submission Requirements
- Format: Papers must follow the IEEE conference format. Use the official ICRA template available at https://www.ieee.org/conferences/publishing/templates.html
- Page Limit: Submissions should not exceed 6 pages, excluding references
- Review Process: All submissions will undergo a double-blind peer review process
- Submission Portal: Papers should be submitted via OpenReview (link below)
Accepted papers will be presented through lightning talks and poster sessions, with opportunities for in-depth technical discussions.