Targeted Topics

We especially welcome work that investigates how semantic structure can improve the data efficiency, generalization, adaptability, safety, and interpretability of learned robot policies:

• Semantic representations for generalizable robot learning, including object-centric representations, affordances, skill abstractions, geometric task-space primitives, pre-/post-conditions, constraints, and action tokenization
• Vision-language-action models, robot foundation models, and generalist policies that connect semantic understanding with embodied control
• Hybrid neural-symbolic architectures that combine learned representations with structured scene representations, knowledge graphs, scene graphs, task graphs, or procedural reasoning
• World models and dynamics models for robot learning, including predictive simulation, synthetic data generation, policy training, planning, evaluation, and sim-to-real transfer
• Cross-embodiment robot learning, including shared action spaces, embodiment-invariant task representations, morphology-aware policies, and transfer across robot platforms
• Test-time adaptation, policy steering, online policy guidance, and failure recovery for robots operating in uncertain or changing environments
• Human-in-the-loop robot learning, including online policy supervision, preference feedback, corrective demonstrations, interactive teaching, and behavior repair
• Semantic memory mechanisms for storing, retrieving, and reusing experience across tasks, environments, and interaction histories
• Metacognitive and self-reflective mechanisms for robot learning, including uncertainty estimation, calibration, self-critique, chain-of-thought-style reasoning, agentic reasoning structures, and confidence-aware control
• Safety-aware semantic reasoning for learned robot policies, including constraint satisfaction, risk-aware decision-making, verification, monitoring, and intervention mechanisms
• Data-efficient and scalable robot learning methods that exploit semantic structure, task decomposition, compositionality, or foundation-model priors
• Benchmarks, datasets, and evaluation protocols for semantic generalization, long-horizon behavior, cross-task transfer, and real-world robustness

Submission Guidelines

RSS SemRob 2026 suggests 4+N or 8+N paper length formats — i.e., 4 or 8 pages of main content with unlimited additional pages for references, appendices, etc.

Submission Site https://openreview.net/group?id=roboticsfoundation.org/RSS/2026/Workshop/SemRob

We will accept the official LaTeX or Word paper templates, provided by RSS 2026. Authors should upload their submission as a PDF file to OpenReview.

Our review process will be double-blind, following the RSS paper submission policy for Science/Systems papers.

All accepted papers will be invited for poster presentations; the highest-rated papers, according to the Technical Program Committee, will be given spotlight presentations. Accepted papers will be made available online on this workshop website as non-archival reports, allowing authors to also submit their works to future conferences or journals. We will highlight the Best Paper Award during the closing remarks at the workshop event.

Important Dates

• Submission deadline (extended): 8 June 2026

  18 June 2026

  , 23:59 AOE.
• Author Notifications: 25 June 2026.
• Camera Ready: 1 July 2026, 23:59 AOE (firm).
• Workshop: 17 July 2026.