Intelligent Robots in 2026: Are We There Yet? with Nikita Rudin - #760

What It Covers

Nikita Rudin, CEO of Flexion Robotics, explains the gap between robotics demos and real-world deployment, covering simulation-to-reality challenges, reinforcement learning techniques, and why no humanoid robot generates actual economic value today in 2025.

Key Questions Answered

• •Sim-to-Real Gap: Closing the simulation-to-reality gap requires deep understanding of both worlds, mapping every software layer from high-level commands down to motor currents. Real-to-sim processes identify critical parameters like torque limits and delays by hanging robots and collecting motor data, then abstracting these into fast simulators.
• •Locomotion Progress: Blind locomotion (reactive only) is robust for quadrupeds, but perception-based locomotion remains challenging. Adding cameras increases the sim-to-real gap significantly because sensor noise and visual rendering must be accurately simulated. Switching robots to new hardware takes only days, but adapting to new tasks requires substantial engineering effort.
• •Robot Deployment Reality: Most robotics demos use either live teleoperation or policies trained on thousands of hours of collected teleoperation data. No humanoid robot currently generates economic value in warehouses or factories because they perform approximate tasks requiring more human handlers than the original workforce, making value negative.
• •Model Architecture: Production systems use three-tier hierarchies: large VLMs for abstract task planning (running off-board or in cloud), vision-language-action models for motion planning at ten hertz (onboard, compute-limited), and small whole-body trackers at fifty hertz on CPU for motor control. Diffusion models in VLAs create the biggest onboard compute bottleneck.
• •Training Approach: Combining reinforcement learning with imitation learning works by using human demonstrations to guide RL exploration, not as pure pretraining. Training separate primitive skills (walking, picking, placing) then distilling into one VLA shows early generalization signs. Industrial deployment focuses on repetitive tasks where abstract reasoning can be precomputed, unlike home environments.