Call for Papers (Download here😀)

Submission Dates

Important information about paper submission:

• Submission Deadline: June 15, 2026, 11:59pm AoE

• Notification of Acceptance: June 21, 2026

• Camera-ready submission: June 30, 2026, 11:59pm AoE

• Workshop Date: July 13, 2026

• Submission Portal: https://easychair.org/conferences/?conf=bridgingaiandrobotic0

• Paper Format: 2-4 pages (excluding references), single-blind RSS format 

• Location: The University of Technology Sydney and ICC, Darling Harbour, Sydney. 

Sample Topics

Sample discussion topics and research questions that would fit this workshop includes (the list is not exhaustive): 

• • Shared autonomy and human-robot Interactions in robotic surgery: How can shared autonomy interfaces, intent capture, haptics, and human-in-the-loop control, be designed and evaluated under clinical constraints for teleoperation and increasing autonomy?

  • Real-time sensing and state estimation: What real-time sensing and state-estimation methods are robust enough for clinical environments, and how should they be integrated into teleoperation and autonomous control loops?

  • Multimodal sensing and fusion: How can multimodal fusion (vision, force/torque, kinematics, physiological signals) improve robustness, reliability, and safety of healthcare robot perception and decision-making?

  • Simulators and digital twins: How can simulators/digital twins support reproducible validation pipelines from phantom → pre-clinical → clinical, and what benchmarks (logs, datasets, metrics, infrastructure) are needed for credible evaluation?

  • Goal-directed autonomy: How should planning–control integration and hierarchical decision-making be formulated for mixed-initiative, shared/adjustable autonomy and collaborative control in healthcare robotics?

  • Tool-using agents (LLM/VLM/VLA): What are effective and safe ways to develop and integrate tool-using agents (LLM/VLM/VLA) for healthcare robotics tasks such as assistance, monitoring, and procedure-level reasoning?

  • Safe robot learning: Which safe reinforcement/imitation learning, constrained optimization, and risk-sensitive/uncertainty-aware approaches are most promising for safety-critical healthcare robotics, and how can their safety claims be validated?

  • Representation learning: How can self-/weakly-supervised (and multimodal) representation learning improve medical robotic perception and decision-making, especially under limited labels and distribution shift?

Click here to submit.