Connectionists: [Meetings] Call for Competition Proposals: CIS-RAM 2026 Competition Track

[CIS-RAM 2026]

We are pleased to announce the inaugural Competition Track at the Conference on Intelligent Systems for Robotics and Manufacturing (CIS-RAM) 2026. Through this call, we invite proposals for competitions on topics relevant to the CIS-RAM community.

We particularly encourage submissions that address clear scientific questions and have a positive societal impact, especially those leveraging embodied AI to support underserved communities.

Proposals focusing on the evaluation of embodied AI models and agentic systems are also welcome. Beyond societal benefit, we seek competitions in areas where embodied AI can advance other scientific, technological, or business domains pertinent to CIS-RAM. Our goal is to attract a diverse, interdisciplinary audience to the CIS-RAM 2026 Competition Track.

Each accepted competition will feature a dedicated in-person workshop during the conference, where participants and organizers can present and discuss results.

All submissions must adhere to the CIS-RAM Code of Conduct and Code of Ethics. The Competition Track follows the CIS-RAM Main Track policies (including those on plagiarism, use of AI agents/models, and anti-collusion), except where specific Competition Track rules (e.g., page limits, review process) are stated.

Proposal Submission

Competition proposals must be submitted via CIS-RAM_competitions at outlook.com<mailto:CIS-RAM_competitions at outlook.com>

The main text is limited to eight (8) content pages, including all figures and tables. Additional pages for references and team biographies are allowed.

Review and Selection Process

A dedicated program committee of experts in embodied AI and challenge organization will review proposals. Evaluation criteria include:

•Scientific Relevance and Tasks:​ Competitions should be driven by clear scientific questions. We will assess impact, originality, and relevance to CIS-RAM. Tasks with humanitarian/societal impact or focused on evaluating embodied AI/agentic systems are highly encouraged. For humanitarian projects, involvement of the relevant community is strongly advised, and "parachute science" is discouraged. Ethical considerations and potential risks must be addressed.

•Evaluation Protocol:​ We will evaluate task feasibility, availability of baselines, soundness of evaluation criteria, and clarity/fairness of rules. The evaluation should align with real-world performance and the core scientific question. Proposals should outline measures (e.g., standardized environments) to ensure fairness, especially for less-resourced teams.

•Data:​ For data-driven competitions, proposals must describe the dataset(s) in detail—including quality, size, representativeness, and suitability for training/testing. Potential data leakage must be addressed. Data must be freely available; open data is encouraged.

•Logistics:​ The competition schedule, plans for attracting participants (including groups underrepresented at CIS-RAM), and the organizers' experience and diversity will be considered.

The review process is single-blind; submissions should include organizer names and affiliations.

Proceedings
Accepted competitions in 2026 have the following option for publishing post-competition analyses:

•Submission to a dedicated CIS-RAM proceedings

Important Data


  *
Competition Proposal Submission Deadline:​ June 30, 2026
  *
Acceptance Notification:​ July 6, 2026
  *   CIS-RAM 2026 Conference (Competition Track):​ August 5-8, 2026


Additional Information

•Organizers should propose a detailed timeline, allowing participants sufficient time to develop high-quality entries. It is recommended to launch competitions by June 2026 and conclude by the end of July 2026 at the latest.
•Organizers are expected to provide brief, regular updates to the Competition Chairs.
•For assistance or suggestions regarding competition platforms, please contact the Competition Chairs.

Competition Chairs

Dongyan Huang, Tsinghua University, CN
Jinwu Gao, Jilin University, CN
Shan Luo, King's College London, UK
Jing Xiong, Shenzhen Institute of Advanced Technology, CAS，CN

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