Aim and Scope

Leveraging the foundation built in the prior workshops RoboNLP 2017, SpLU 2018, SpLU-RoboNLP 2019, and SpLU 2020, this workshop aims to realize the long-term goal of natural conversation with machines in our homes, workplaces, hospitals, and warehouses. It also highlights the importance of spatial semantics when it comes to communicating about the physical world and grounding language in perception. Human-robot dialogue often involves developing an understanding of grounded spatial descriptions. These capabilities invariably require understanding spatial semantics that relates to the physical environments where robots are embodied. The main goal of this joint workshop is to bring in the perspectives of researchers working on physical robot systems and with human users, and align spatial language understanding representation and learning approaches, datasets, and benchmarks with the goals and constraints encountered in HRI and robotics. Such constraints include high costs of real-robot experiments, human-in-the-loop training and evaluation settings, scarcity of embodied data, as well as non-verbal communication.

Topics of Interest

  1. Achieving Common Ground in Human-Robot Interaction
  2. Aligning and Translating Language to Situated Actions
  3. Cognitive and Linguistically Motivated Spatial Language Representations
  4. Evaluation Metrics for Language Grounding and Human-Robot Communication
  5. Human-Computer Interactions Through Natural or Structural Language
  6. Instruction Understanding and Spatial Reasoning Based on Multimodal Information for Navigation, Articulation, and Manipulation
  7. Interactive Situated Dialogue for Physical Tasks
  8. Language-based Game Playing for Grounding
  9. Reasoning over Spatial Language (e.g. Based on Qualitative and Quantitative Spatial Representation)
  10. Spatial Language and Skill Learning via Grounded Dialogue
  11. Spatial Information Extraction from Text (e.g. Locative Descriptions, Navigation Instruction)
  12. (Spatial) Language Generation for Embodied Tasks
  13. (Spatially) Grounded Knowledge Representations

Invited Speakers


Long Papers

Technical papers: ACL style, 8 pages excluding references

Short Papers

Position statements describing previously unpublished work or demos: ACL style, 4 pages excluding references

ACL Style files: Template

Submissions website: Softconf

Non-Archival option: ACL workshops are traditionally archival. To allow dual submission of work to SpLU-RoboNLP 2021 and other conferences/journals, we are also including a non-archival track. Space permitting, these submissions will still participate and present their work in the workshop, and will be hosted on the workshop website, but will not be included in the official proceedings. Please submit through softconf but indicate that this is a cross submission (non-archival) at the bottom of the submission form.

Important Dates

Organizing Committee

  • Malihe Alikhani
  • University of Pittsburgh
  • Valts Blukis
  • Cornell University
  • Parisa Kordjamshidi
  • Michigan State University
  • Aishwarya Padmakumar
  • Amazon Alexa AI
  • Hao Tan
  • University of North Carolina

    Program Committee

  • Jacob Arkin
  • University of Rochester
  • Jonathan Berant
  • Tel-Aviv University
  • Steven Bethard
  • University of Arizona
  • Johan Bos
  • University of Groningen
  • Volkan Cirik
  • Carnegie Mellon University
  • Guillem Collell
  • KU Leuven
  • Simon Dobnik
  • University of Gothenburg, Sweden
  • Fethiye Irmak Dogan
  • KTH Royal Institute of Technology
  • Frank Ferraro
  • University of Maryland, Baltimore County
  • Daniel Fried
  • University of California, Berkeley
  • Felix Gervits
  • Tufts University
  • Yicong Hong
  • Australian National University
  • Drew Arad Hudson
  • Stanford University
  • Xavier Hinaut
  • Inria
  • Gabriel Ilharco
  • University of Washington
  • Siddharth Karamcheti
  • Stanford University
  • Hyounghun Kim
  • UNC Chapel Hill
  • Jacob Krantz
  • Oregon State University
  • Stephanie Lukin
  • Army Research Laboratory
  • Lei Li
  • ByteDance AI Lab
  • Roshanak Mirzaee
  • Michigan State University
  • Ray Mooney
  • University of Texas, Austin
  • Mari Broman Olsen
  • Microsoft
  • Natalie Parde
  • University of Illinois, Chicago
  • Christopher Paxton
  • Roma Patel
  • Brown University
  • Nisha Pillai
  • University of Maryland, Baltimore County
  • Preeti Ramaraj
  • University of Michigan
  • Kirk Roberts
  • University of Texas, Houston
  • Anna Rohrbach
  • University of California, Berkeley
  • Mohit Shridhar
  • University of Washington
  • Ayush Shrivastava
  • Georgia Tech
  • Jivko Sinapov
  • Tufts University
  • Kristin Stock
  • Massey University of New Zealand
  • Alane Suhr
  • Cornell University
  • Rosario Scalise
  • University of Washington
  • Morgan Ulinski
  • Columbia University
  • Xin Wang
  • University of California, Santa Cruz
  • Shiqi Zhang
  • SUNY Binghamton
    *If you are interested to join the program committee and participate in reviewing submissions please Email the organizers at Please mention your prior reviewing experience and a link to your publication records in your Email.