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Workshop Theme
The year 2024 has seen an explosion of interest in humanoid robots. However, recent systems for drone racing, playing table tennis, and others clearly demonstrate that the humanoid form-factor isn’t a requirement for human-level performance. In the 7th Robot Learning workshop, to be held at ICLR 2025, we will look beyond the humanoid embodiment and ask: how far are we from robots with human-level abilities? What do we need to improve about embodied learning, decision-making, perception, and data collection to train generally physically capable robots to robustly perform a wide range of activities such as cooking or tidying up a house – activities that people do without much thinking?
We believe many of the weaknesses of the current robotic systems to be a reflection of the shortcomings of general AI methods and models. As such, we seek diverse perspectives on the workshop theme from robotics-focused and robotics-orthogonal parts of the ICLR community alike, scientific contributions from academia and industry, as well as participants from a variety of backgrounds and career stages.
Invited Speakers
Call for Papers
Key details
- Submission page: Robot Learning Workshop on OpenReview
- Submission deadline: February 10, 2025 (Anywhere on Earth)
We welcome submissions of original research papers as well as systems papers accompanied by videos (see the submission format below) focusing on algorithmic innovations, theoretical advancements, system design, or practical applications relevant to the workshop theme.
Specific areas of interest include but are not limited to:
- Novel ML algorithms and model architectures for robot control: techniques integrating large multi-modal models, sim-to-real bridging, safe policy optimization, and data efficiency.
- Human-robot interaction and collaboration: socially aware motion planning, adaptive interfaces, and trust-building strategies for seamless teamwork. Hardware innovations and system integration: advanced sensing and actuation, high-degree-of-freedom controllers, energy-efficient designs, and cohesive robotics architectures.
- Simulation, benchmarking, and evaluation methodologies: realistic simulation environments, standardized task suites, robust metrics, and cross-domain validation protocols.
- Applications in unstructured and dynamic environments: household assistance, mobile manipulation, industrial automation, healthcare, disaster response, and other real-world domains.
Submission format and review process
We welcome submissions in three formats:
- Full Papers
- Recommended length: 4–10 pages (no strict upper limit) using the ICLR-2025 template.
- Expected to meet standards typical of workshop papers, including technical depth and novelty.
- Tiny Papers
- Adhering to the format described in the ICLR Call for Tiny Papers, focusing on concise and impactful ideas.
- Should align closely with the workshop theme, offering preliminary insights or novel perspectives.
- Systems Papers
- Recommended length: 4–10 pages (no strict upper limit) using the ICLR-2025 template.
- Expected to be about a system, at least one of whose key components critically relies on AI/ML.
- Must be submitted with a supplementary video showing the system operation.
- All accepted systems papers will be guaranteed an oral spotlight presentation.
IMPORTANT: For the camera ready submission of your workshop paper please use the updated template. Note it should read “Accepted as a workshop paper to the 7th Robot Learning Workshop at ICLR 2025” at the top of your camera ready paper.
Accepted submissions will be non-archival, though Tiny Papers will be subject to the non-workshop-specific rules in the ICLR Call for Tiny Papers.
Important dates
- Submission deadline: February 10, 2025 (Anywhere on Earth)
- Notification: February 27, 2025 (Anywhere on Earth)
- Camera-ready due: April 11, 2025 (Anywhere on Earth)
- Workshop: 27 April 2025
Schedule
TBD
Accepted Papers
TBD
Organizers
- Andrey Kolobov (Microsoft Research, Redmond, USA)
- Hamidreza Kasaei (University of Groningen, Netherlands)
- Alex Bewley (Google DeepMind, Zurich)
- Anqi Li (NVIDIA, Seattle, USA)
- Dhruv Shah (UC Berkeley)
- Georgia Chalvatzaki (TU Darmstadt, Germany)
- Feras Dayoub (University of Adelaide, Australia)
- Roberto Calandra (TU Dresden, Germany)
- Ted Xiao (Google DeepMind, Mountain View, USA)
- Rika Antonova (University of Cambridge, UK and Stanford University, USA)
- Nur Muhammad “Mahi” Shafiullah (New York University, USA)
- Masha Itkina (Toyota Research Institute, Los Altos, USA)
Advisors
- Markus Wulfmeier (Google DeepMind, London, USA)
- Jonathan Tompson (Google DeepMind, Mountain View, USA)
Contacts
For any further questions, you can contact us at iclr2025@robot-learning.ml