Awesome lineup on MTS today: @ahall_research just discussed AGI politics @Ronalfa on the AI x Bio explosion happening @evan_wineland & @kaandogrusoz discussing their home robot, Isaac 1 @jared_western telling us about Western Chemicals @kmad sharing his thoughts on FDEs versus consultants @rdn_nikita on Flexion's approach to robotics @jasonhausenloy to discuss the frontier lab brain drain @StevenGlinert about Nvidia & geopolitics timeline review and coverage of important stories like government equity stakes in OpenAI, the return of Fable, and other breaking stories at the frontier of the singularity

We ran 300 fully autonomous live demonstrations over 3 days at ICRA 2026. The task: a humanoid navigating stairs, picking up a box from the floor and placing it on a table. Simple to describe, but hard to execute reliably when your robot is making every decision on its own at a conference with new surroundings and a crowd watching live. This is just a glimpse. We've been pushing our stack much further and we'll be sharing more very soon. More information in the thread. #HumanoidRobots #ICRA2026 #Flexion

Today, we're joined by @rdn_nikita, co-founder and CEO of @FlexionRobotics 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. 🗒️ For the full list of resources for this episode, visit the show notes page: twimlai.com/go/760. 📖 CHAPTERS =============================== 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 pla

We’ve raised $50 million in Series A funding from DST Global Partners, @nvidia (NVIDIA’s venture capital arm), @redalpine , @prosusventures , and @Moonfire_VC , following our $7.35M seed round from @frst_vc , @Moonfire_VC , and @redalpine just a few months earlier, to build the autonomy stack that makes humanoid robots adaptive, intelligent, and ready for real-world deployment at scale. In less than a year, our team has shown that long-horizon whole-body humanoid control can scale across hardware and tasks by leveraging the power of simulation and reinforcement learning. This funding will help us grow our team, scale our compute and robot fleets, and accelerate the commercialization of our autonomy stack with OEM partners globally. You can find more details in the links shared in the comments.

ANYmal just learned parkour in the wild! 9 expert skills → 1 foundation policy → RL fine-tuning → any terrain with a single end-to-end policy from cameras to motor control. Paper: arxiv.org/abs/2505.11164 Watch: youtu.be/QDU_FicBPDo @rdn_nikita Junzhe He Joshua Aurand

🔥Exciting news 🤖 Our latest research by @HoellerDavid, @rdn_nikita, @2nisi in @SciRobotics unlocks new achievements:  Unprecedented agility in quadrupedal robots, mastering locomotion, navigation, and perception through deep reinforcement learning! @NVIDIARobotics