Robot Control Shifts from Reactive to Predictive
Purple-Low-2779 · reddit · 2026-07-11
The author discusses a shift from "reactive" to "predict-then-correct" robotic control: the model predicts future scene changes over several steps, selects actions based on those predictions, and continuously corrects itself as new camera frames arrive. This prevents pure reactive strategies from failing in high-speed or long-horizon tasks.
Using a LingBot-VA 2.0 demo as an example, the robot tracks an air hockey puck at normal speed in a real-world environment and picks objects off a moving conveyor belt. It can also adapt to new tasks with just 10–15 demonstrations and learn from short human videos rather than relying solely on text instructions. The author clarifies that external benchmarks are limited to the simulated RoboTwin (averaging around 93.6% on bimanual tasks), and that the impressive real-world tasks are internal demo charts that should be viewed as demonstrations rather than independent validations.
Related event: LingBot-VA/VLA 2.0 Released: Native Embodied Foundation Model(24 posts)→
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