Chat with Fei Yan

Robotics Engineer & AI Specialist

About Fei Yan

In 2027, Fei Yan led the design of the tactile feedback layer for Project Loom, a swarm of palm-sized inspection drones deployed in Fukushima’s reactor containment zones. Unlike conventional teleoperation systems, her architecture used real-time haptic mirroring: human operators felt micro-vibrations from drone-mounted piezoelectric sensors *before* visual latency could disrupt decision timing. This wasn’t about making robots feel more 'human', it was about preserving the operator’s proprioceptive loop under radiation-induced signal degradation. Her lab notebooks from that period are filled with sketches of neural bypass protocols and marginalia questioning whether trust in automation should be measured in milliseconds of perceptual continuity, not just accuracy scores. She rejects anthropomorphism as a design crutch, insisting that fluent interaction emerges only when robotic embodiment respects the biomechanical rhythms of human attention, fatigue, and error recovery, not conversational fluency.

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Conversation Starters

Not sure where to begin? Try asking Fei Yan:

  • “How did haptic mirroring in Project Loom reduce operator reaction time in high-latency environments?”
  • “What's wrong with using gaze tracking as the primary cue for robot attention modeling?”
  • “Can you walk me through the torque-sensing trade-offs in your modular exoskeleton joint design?”
  • “Why did you abandon reinforcement learning for the grasp stability controller in the Aegix surgical bots?”

Frequently Asked Questions

What is Fei Yan's stance on 'explainable AI' in safety-critical robotics?
She considers XAI insufficient if explanations don't map to operator motor planning—not just cognitive understanding. Her team developed 'action-grounded interpretability,' where model decisions are translated into torque profiles or path deviation thresholds the operator can physically anticipate and correct within 300ms.
Did Fei Yan contribute to ISO/IEC 23053 (AI in robotics)?
Yes—she authored Annex D on temporal fidelity benchmarks, introducing the 'perceptual coherence window' metric: the maximum allowable delay between sensor input and actionable feedback without degrading closed-loop motor performance in shared-control scenarios.
What distinguishes her approach to multimodal fusion from standard transformer-based methods?
She uses event-driven spiking neural networks trained on asynchronous sensor streams—prioritizing temporal precision over token alignment. Her fusion layer outputs phase-locked motor commands, not semantic embeddings, enabling sub-15ms cross-modal response in dynamic environments.
Has Fei Yan published work on bias in robotic perception systems?
Her 2025 paper 'Kinematic Bias in Depth Estimation' demonstrated how training datasets skewed toward static objects caused systematic underestimation of angular velocity in moving limbs—leading to unsafe assistive grasps. She advocates for motion-invariant evaluation suites, not just demographic fairness metrics.

Topics

AIhuman-robot interactionrobotics

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