Why Traditional Robotics Data Collection Is Obsolete and What Replaces It

The Robot Report Podcast · 8 May 2026 · 1h 04m

What this episode covers

Eric Chan, chief scientist and co-founder of Rhoda AI, explains the company's Direct Video Action (DVA) model, which uses internet video data to train robots for zero-shot learning. Chan contrasts this approach with traditional Vision-Language-Action (VLA) models, arguing that predicting future video frames provides a more scalable and data-efficient prior for robot control. The discussion covers the technical challenges of low-latency inference, the use of force/tactile inputs, and the future of prompting robots via language or single-shot demonstrations.

Other shows on the same ground, closest first.

  • Robot Talk · 29m

    Episode 74: Teaching robots to grasp - Edward Johns

    Edward Johns, Director of the Robot Learning Lab at Imperial College London, discusses the challenges of robotic manipulation, arguing that the bottleneck is currently AI and control rather than hardware. He explains his lab's use of imitation learning, where robots learn by being physically guided, and details the Dali Bot project, which uses AI image generators to help robots visualize task goals. Johns outlines applications ranging from warehouse sorting to flexible manufacturing and long-term domestic robots, while noting the significant data scarcity in robotics compared to other AI fields.

  • The Manufacturing Executive · 27m

    Moving From Generative to Physical AI on the Manufacturing Floor w/ Etienne Lacroix

    Etienne Lacroix, Founder and CEO of Vention, discusses the transition from generative AI to 'physical AI' on the manufacturing floor, defining it as injecting AI code into embodied systems like robots to achieve autonomy. He identifies bin picking and vision-guided robotics as the only two task categories currently ready for industrial deployment, citing specific metrics like 99.9% first-pick success rates and sub-two-year payback periods for two-shift operations. Lacroix contrasts these mature applications with the current limitations of Vision-Language-Action (VLA) models, which require excessive post-training hours, and highlights the operational challenges of non-deterministic robot movements.

  • Robot Talk · 29m

    Episode 130: Robots learning from humans - Chad Jenkins

    Chad Jenkins, a Professor of Robotics at the University of Michigan, discusses his lab's work on teaching robots through demonstration rather than coding, focusing on dexterous mobile manipulation and perception. He explains the challenge of 'affordances'—understanding how objects can be used—and the trade-off between the speed of deep learning models and the robustness of search-based algorithms. Jenkins also highlights his 'distributed teaching collaboratives' initiative, which uses open-source course materials to scale robotics education, supported by funding from the Sloan Foundation, Amazon, and Toyota Research Institute.

  • The Next Byte Podcast · 23m

    177. Did A Shrimp Fry This Rice? No, A Robot Did

    Daniel and Forbode discuss Stanford University's 'Mobile Aloha' platform, a bimanual robot designed to learn household tasks through imitation learning rather than extensive simulation. They highlight the platform's ability to generalize from a static dataset of 825 operations and improve success rates by 90% after roughly 50 human-led demonstrations. The hosts emphasize the significance of reducing the cost of such robotic systems from hundreds of thousands to around $30,000, making general-purpose robotics more accessible for future commercial and domestic use.

  • Robot Talk · 30m

    Episode 132: Collaborating with industrial robots - Anthony Jules

    Anthony Jules, CEO and co-founder of Robust.AI, discusses the design and deployment of Carter, an AI-driven collaborative mobile robot for warehouses. Jules explains how Carter uses cameras instead of LIDAR to achieve semantic understanding, allowing it to navigate safely alongside humans and empower floor associates rather than replace them. He details the company's 'fleet learning' strategy, where data from individual robots is aggregated in the cloud to improve AI models offline, ensuring consistent productivity for clients like DHL without disrupting live operations.

The summary, chapters and topics are generated from the episode’s audio by this directory, not written by The Robot Report Podcast. The episode itself belongs to its publisher.