Towards Robust Execution of Long-Horizon Whole-Body Control Tasks

RSS 2026 Workshop
July 13, Morning, Sydney, Australia

Introduction

Recent progress in robot learning has significantly advanced robotic capabilities on short-horizon skills and well-defined tasks. However, despite these advances, robots, such as humanoids and robotic arms, continue to struggle when deployed on long-horizon, complex tasks in the real world. Topics of interest of this workshop include on-policy progress estimation for multi-stage tasks, run-time failure recovery, hierarchical and memory-augmented policies trained under long-horizon rollouts, sim-to-real transfer with execution-time fine-tuning, and leveraging foundation models for task planning, perception, and feedback within closed-loop, on-policy execution.

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Call for Contributions

We invite researchers to share their work with the community through submissions to the workshop in a variety of formats beyond traditional papers, including reports, demos, video, and etc. Submissions may include research papers or reports, but we equally welcome alternative formats such as videos demonstrating systems in action, demos, interactive artifacts, or other creative presentations of research ideas. We particularly welcome ongoing, preliminary, or exploratory work.

Topics of Interest

We welcome works on a wide range of topics, including but not limited to:

Long-horizon robot learning and multi-stage task execution.
Whole-body control for high-dimensional, multi-contact robotic systems.
Loco-manipulation and mobile manipulation in unstructured environments.
On-policy progress estimation under partial observability.
Task decomposition and sub-goal discovery for complex behaviors.
Run-time failure detection, recovery, and robustness in real-world deployment.
Closed-loop decision-making with feedback-driven adaptation.
Integration of learning, planning, and control for sequential decision-making.
Data efficiency in robot learning, including on-policy data collection and hybrid offline-online training.
Metrics and evaluation protocols for long-horizon performance, robustness, and generalization.

Guidelines

All contributions must be submitted through OpenReview.
Manuscripts are required to use the LaTex or Word template.
No strict page length requirements on submissions.
To facilitate double-blind review, all submissions must be fully anonymized.
All accepted contributions will be made available online on this workshop website as non-archival reports.
Selected contributions will be invited for presentations.