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. 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: 29 June 2026, 23:59 AOE.
-
Camera Ready: 5 July 2026, 23:59 AOE
(firm).
-
Workshop: 17 July 2026.
Dual Submission Policy
We welcome new, under-review, or recently published work. Accepted papers will be posted on
this website but are
non-archival and won't be included in any official proceedings.
