LingBot‑VLA 2.0 tackles the biggest headache for robotics teams: a vision‑language‑action model that shines in the lab but fails when it hits the real world. The core issues are poor generalization across robot designs, a narrow set of actions the model can output, and a lack of foresight that leads to jerky or unsafe motions. By training on 60 000 hours of diverse robot and human video data, filtering out low‑quality trajectories with explicit jerk and smoothness checks, and unifying every embodiment into a fixed 55‑dimensional action vector, the model now works on everything from single‑arm manipulators to full humanoids without retraining.
The action space is expanded through a Mixture‑of‑Experts layer that activates only the needed experts per timestep, keeping compute bounded while giving the model the capacity to express fine‑grained grippers, wrist twists, waist bends, and mobile base commands. To move beyond pure reaction, two learnable queries are appended to the visual‑text stream: one targets the current frame, the other a future frame a few steps ahead. These queries are distilled from a depth‑prediction teacher (LingBot‑Depth) and a video‑understanding teacher (DINO‑Video), giving the policy explicit geometric cues and temporal semantics so it can anticipate contacts and plan smooth trajectories.
In practice, a user downloads the 6B checkpoint, runs the lightweight inference server (≈130 ms per step on an RTX 4090D), and feeds camera images plus a language instruction. The unified 55‑D output can be mapped directly to any robot’s joint space by padding unused dimensions. Fine‑tuning on a small LeRobot‑style dataset (e.g., RoboTwin 2.0) adapts the policy to a new task in hours rather than weeks.
Result: higher success rates on bimanual benchmarks, better handling of out‑of‑distribution poses, and fewer failures at the final placement step. Teams get a single, deployable policy that generalizes, acts richly, and predicts—cutting the gap between simulation and the factory floor.
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