Coming up timely, the workshop will focus on how learning-driven and agentic AI approaches can be engineered into collaborative surgical robotic systems. Fundamental technological developments and translational challenges of AI and medical robotics in surgery will be addressed through invited talks, panel discussion, and poster demo with spotlight presentation.Â
This workshop is designed to discuss the challenges hindering the large-scale integration of AI in medical robotics and adoption of intelligent healthcare robotics. We want to focus on addressing the fundamental technological developments and translational through invited talks, panel discussion, and poster demo with spotlight presentation. Thus, we invite submissions of extended abstract from research, engineering and developmental studies on mechanisms, methods, and systems on medical robotics for safer and smarter interventional procedures. Matured studies focusing pre-clinical and clinical validations, translation trials are also welcome. As a non-archival workshop, we also welcome recent published or submitted results on the relevant topics. The submissions will be reviewed, and if accepted, will be posted on the workshop website and presented at our poster session.Â
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 formatÂ
Location: The University of Technology Sydney and ICC, Darling Harbour, Sydney.Â
Sample discussion topics and research questions that would fit this workshop includes (the list is not exhaustive):Â
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.Â