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Explore Perceptive Dynamic Manipulation with Spot with 50,204 views, 410 likes, and 43 comments. Experience the impact of this video content that has captured audience attention.

Robotic manipulation is moving past rigid, single-purpose automation toward adaptive, real-time physical interaction. Previously, we demonstrated Spot uprighting and rolling a 15 kg tire. Because this weight exceeds the payload limits of Spot's manipulator arm alone (11 kg peak, 5 kg continuous), the control architecture coordinated ground reaction forces across its legs and torso in tandem with arm trajectories, synthesizing reinforcement learning with sampling-based trajectory optimization. However, this early setup remained tethered to the lab, reliant on an external motion-capture array, off-board desktop computation, and explicit state-machine scripting to sequence behaviors. Our latest milestone transitions this system to full autonomy and untethered outdoor execution. By replacing external motion capture with onboard depth-camera perception and running all policy evaluation locally, the robot can now perceive, locate, and manipulate objects in unmapped, real-world environments. The core of this advance lies in visual policy learning that directly processes noisy sensor streams. As a result, the platform dynamically sequences and adjusts behaviors like rolling, uprighting, dragging, and recovering from physical interferences, allowing the robot to adapt to whatever is around it. Learn more about this control stack, how these policies were trained, and what comes next: https://rai-inst.com/resources/blog/whole-body-manipulation-outside-the-lab/

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