Figure shows Helix 02 cleaning an entire living room with one general-purpose humanoid control system

On March 9, 2026, Figure published a new Helix 02 demonstration focused on living-room cleanup. The company describes Helix 02 as a single neural system that controls the full body directly from pixels. In this example, the point is not one isolated manipulation trick but long-horizon autonomy across an entire room.

Figure argues that tidying a living room is difficult because the environment is open-ended and constantly changing. Unlike a tightly structured industrial task, the robot has to combine locomotion, dexterous manipulation, tool use and real-time planning while dealing with different objects, containers and layouts. According to the company, Helix 02 performs whole-body, end-to-end cleanup by moving through the room and continuously switching between these behaviors.

Behaviors Figure highlighted

Using a spray bottle and a towel together to clean surfaces

Handling a flexible towel by unhooking, repositioning and stowing it during motion

Picking up a bin with two hands and scooping blocks into it

Tucking a container under one arm to free both hands for other actions

Throwing a pillow back onto a couch and reorienting a remote to turn off a TV

Side-stepping through tight gaps while continuing manipulation

Figure says Helix 02 learned these new tasks by adding data rather than introducing new algorithms or special-case engineering. It also says the same general-purpose architecture used for earlier tasks was sufficient for this room-scale cleanup scenario. If that claim continues to hold, the implication is that capability growth could come more from data and scaling than from a growing stack of separate task controllers.

The reason this matters is that a living-room tidy is an unusually dense benchmark for humanoid robotics. It mixes sensing, walking, grasping, tool use and sequencing in a way that resembles everyday work much more than a single industrial pick-and-place motion. Figure is effectively arguing that it can push a single model toward broader household and workplace utility.

There is still a clear gap between a polished company demo and product-grade reliability. Important questions remain around speed, failure recovery and repeatability. Even so, the March 9 result is a notable signal that humanoid systems are being pushed beyond point demos toward room-scale generality.