Intelligent Robots in 2026: Are We There Yet? [Nikita Rudin] – 760

Today, we’re joined by Nikita Rudin, co-founder and CEO of Flexion Robotics to discuss the gap between current robotic capabilities and what’s required to deploy fully autonomous robots in the real world. Nikita explains how reinforcement learning and simulation have driven rapid progress in robot locomotion—and why locomotion is still far from “solved.” We dig into the sim2real gap, and how adding visual inputs introduces noise and significantly complicates sim-to-real transfer. We also explore the debate between end-to-end models and modular approaches, and why separating locomotion, planning, and semantics remains a pragmatic approach today. Nikita also introduces the concept of "real-to-sim", which uses real-world data to refine simulation parameters for higher fidelity training, discusses how reinforcement learning, imitation learning, and teleoperation data are combined to train robust policies for both quadruped and humanoid robots, and introduces Flexion’s hierarchical approach that utilizes pre-trained Vision-Language Models (VLMs) for high-level task orchestration with Vision-Language-Action (VLA) models and low-level whole-body trackers. Finally, Nikita shares the behind-the-scenes in humanoid robot demos, his take on reinforcement learning in simulation versus the real world, the nuances of reward tuning, and offers practical advice for researchers and practitioners looking to get started in robotics today.

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📖 CHAPTERS
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00:00 – Introduction
04:07 – Is robot locomotion solved?
06:04 – Sim-to-real gap
08:58 – Adding semantics to policies
09:42 – Modular vs end-to-end architectures
10:29 – Planner model
12:21 – Adapting RL techniques from quadrupeds to humanoids
15:39 – Behind robot demos
18:09 – Humanoid robots in home environments
22:03 – Training approach
23:56 – VLA models
27:59 – Closing the sim-to-real gap
32:55 – Task orchestration using VLMs
36:38 – Tool use
38:10 – Model hierarchy
43:37 – Simulator versus simulation environment
44:57 – Combining imitation learning and reinforcement learning
46:42 – RL in real world versus RL in simulation
52:58 – Reward tuning and value functions in robotics
56:38 – Predictions
1:00:10 – Humanoids, quadropeds, and wheeled platforms
1:02:45 – Advice, recommended robot kits, and community platforms

🔗 LINKS & RESOURCES
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Flexion Robotics – https://flexion.ai/
Flexion Robotics (Tweet) – https://x.com/FlexionRobotics/status/1991897997068956092
Reinforcement Learning for Industrial AI with Pieter Abbeel – #476 – https://twimlai.com/podcast/twimlai/reinforcement-learning-industrial-ai-pieter-abbeel/

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