Modular Robots That Switch: One Quad or Two Bipeds

A lot of mobile robots are trapped by a choice you make on day one: wheels for speed, legs for uneven ground, or something in between. That choice looks fine in a lab, then reality shows up—loading docks with broken pavement, hospitals with tight corners, warehouses that change layouts every quarter, and safety teams that won’t sign off on “one-size-fits-all” mobility.

That’s why the idea behind the modular D1 robot is so interesting: it isn’t just a quadruped with wheels-for-feet (which is already becoming common). It’s a platform that can operate as one quadruped or split into two self-balancing biped robots. And the part that actually matters for businesses isn’t the party trick—it’s what AI-enabled modular robotics makes possible: adaptive automation that can be redeployed without buying a second fleet.

This post sits in our AI in Robotics & Automation series, and I’m going to take a stance: modularity is quickly becoming the practical path to ROI in mobile robotics, because AI is finally good enough to manage the complexity that modular hardware introduces.

Why modular mobility is the next practical bet

Answer first: Modular robots reduce the “mobility mismatch” problem—when a robot is great in one zone and inefficient (or unsafe) in another—by letting the same platform reconfigure for different environments.

Most automation leaders don’t fail because they dislike innovation. They fail because they buy robots that are optimized for a single operating assumption:

• Floors are flat and consistent
• Pathways stay open
• Tasks don’t change much
• The building won’t be renovated

That’s not how real facilities behave. In Q4 (and it’s December 2025 as I’m writing this), many operations are in peak-mode: temporary staging areas, seasonal staffing patterns, extra pallets in aisles, pop-up overflow zones. This is exactly when rigid mobility choices become expensive.

A robot that can be a wheel-capable quadruped for long transits and then split into two bipeds for tighter, human-centric spaces suggests a new operating model: you don’t just schedule tasks—you schedule configurations.

The myth: “More modes = more headaches”

More modes can be more headaches—if your control stack can’t keep up. But AI changes the math.

A modular platform has more failure states (connections, calibration drift after reconfiguration, different balance constraints). The only way it becomes a business tool instead of a research project is if the robot can:

• Detect which configuration it’s in
• Recalibrate quickly
• Choose stable gaits and safe speeds automatically
• Keep performance predictable enough for operators and safety teams

That’s a software problem. And it’s exactly where modern robotics AI—especially learning-based perception and control—earns its keep.

What “one quad or two bipeds” actually enables

Answer first: The real value of a convertible quadruped/biped system is coverage: it can service both high-throughput corridors and human-scale spaces without swapping robots.

Let’s talk through what this kind of configuration switch means, beyond the novelty.

Quadruped mode: stability + payload tolerance

A quadruped form factor tends to win when the environment is inconsistent:

• Slight steps and thresholds
• Floor transitions (rubber mats, ramps, dock plates)
• Obstacles that require stepping around instead of rerouting

Add powered wheels at the feet and you get a hybrid that can roll when it’s efficient and step when it must.

Where it fits:

• Warehouse perimeter runs (between zones)
• Outdoor/indoor transitions at industrial sites
• Facilities where routes are long but not perfectly maintained

Two biped mode: human-space maneuvering and parallelism

Splitting into two self-balancing bipeds introduces a different advantage: parallel work.

Instead of one robot making one trip, you potentially have two units that can:

• Take separate tasks
• Work two sides of a hallway
• Stage items in two locations

Bipeds can also be better suited to human-designed constraints—narrow passages, doors, tight turn radii—if their balancing and perception are robust.

Where it fits:

• Hospitals and care facilities (tight corridors, frequent human interaction)
• Retail backrooms and stock areas
• Manufacturing lines where space near workcells is limited

Here’s the blunt truth: bipeds are harder—balance control, fall safety, recovery behaviors, and human proximity all raise the bar. That’s why AI (and strong safety engineering) is non-negotiable.

The AI stack that makes modular robots usable (not just impressive)

Answer first: Modular robots need AI for perception, state estimation, multi-modal control, and “task-to-mobility” decision-making—or operators will end up babysitting configuration choices.

If you’re evaluating modular robotics for logistics automation or service robotics, ask less about the chassis and more about the autonomy stack. These are the capabilities that separate a demo from deployment.

Perception: the robot must understand the environment and its own body

A convertible platform changes its dynamics and geometry. AI perception must handle:

• Scene understanding: people, carts, pallets, doors, thresholds
• Terrain classification: smooth floor vs ramp vs clutter
• Self-perception: confirmation that modules are latched, aligned, and calibrated

In practice, that’s a fusion problem: cameras, IMU, wheel odometry, joint encoders, possibly depth sensing. AI helps by improving robustness when sensors disagree (which happens constantly in busy facilities).

State estimation and balance: where bipeds live or die

Self-balancing bipeds require extremely reliable estimation of orientation, contact points, and center-of-mass behavior. Small errors compound quickly.

A deployable system needs:

• Fast control loops
• Slip detection
• Recovery behaviors (stumbles, minor impacts)
• Conservative policies around humans

Learning-based controllers can be helpful, but only when they’re wrapped in guardrails—think constraint-aware control and “fallback” behaviors.

Decision-making: when should it be one quad vs two bipeds?

This is the part most companies underestimate.

You don’t want operators deciding configuration all day. You want an AI policy that considers:

• Route length and congestion
• Doorways and pinch points
• Task urgency and batching opportunities
• Battery state and charging availability
• Safety constraints (crowded corridors vs empty runs)

A simple but effective approach is a cost model: estimate time, energy, and risk for each mode, then pick the lowest-cost option within safety constraints.

Snippet-worthy rule: A modular robot needs an AI “mobility planner” that optimizes time, energy, and safety—not just a navigation stack.

Fleet coordination: two bipeds means two traffic participants

If one robot becomes two, your fleet manager now has to:

• Allocate tasks across two agents
• Prevent bottlenecks at doors and elevators
• Reserve zones for safe reconfiguration
• Track maintenance and health independently

This is where AI-driven fleet orchestration (prediction of congestion, dynamic task allocation) pays off.

Real deployment scenarios in manufacturing, logistics, and healthcare

Answer first: Modular robots make the most sense where you have mixed environments and changing workflows—especially when redeployment costs are high.

Below are concrete ways a “one quad or two bipeds” robot could create value.

Logistics: peak season reconfiguration without adding headcount

During peak periods, warehouses often create temporary zones. A modular robot could:

• Run long replenishment routes as a quadruped (fast, stable)
• Split into two bipeds near picking or packing zones
• Handle smaller, frequent moves (totes, small carts) in tighter aisles

The lead-gen reality: if you’re an ops leader, you’re not buying “a robot.” You’re buying throughput under variability. Modularity directly targets variability.

Manufacturing: one platform across line-side delivery and plant transit

Manufacturing sites often have both:

• Long internal corridors between storage and cells
• Tight line-side spaces where people and robots mix

A convertible robot can function as a plant runner in quad mode, then switch to biped mode to service line-side tasks without widening aisles or redesigning workcells.

Healthcare: mobility that respects people, not just maps

Hospitals are full of edge cases: beds protruding into hallways, visitors stopping abruptly, equipment parked in “temporary” spots for hours.

A biped form can be attractive for maneuvering, but only if it behaves conservatively.

The best use case here is not flashy humanoid interaction. It’s boring and valuable:

• Specimen runs
• Supply restocking
• Linen and waste routes

And because healthcare environments change constantly, the ability to reconfigure and reassign roles is practical.

What to evaluate before you buy (a checklist that saves budget)

Answer first: If you’re considering modular robot platforms, evaluate reconfiguration time, autonomy maturity, safety behaviors, and maintenance impact—not just specs.

Here’s a field-tested evaluation checklist I’d use.

Technical evaluation

1. Reconfiguration time and repeatability

   • How long does switching modes take?
   • Does it require tools or human intervention?
   • How often does it need recalibration?

2. Mode-aware autonomy

   • Does navigation performance change dramatically by mode?
   • Are there “no-go” environments for biped mode?

3. Failure behavior

   • What happens if a module doesn’t latch?
   • How does it detect drift or misalignment?
   • Does it fail safe (stop, alert, recover)?

4. Floor and obstacle performance

   • Thresholds, ramps, mats, small debris
   • Slip detection and recovery

Operational evaluation

• Training burden: Can a non-expert supervise it?
• Maintenance model: Are you doubling your maintenance by splitting into two units?
• Spare strategy: Can you hot-swap modules to reduce downtime?
• Fleet integration: Does it work with your task system, elevators/doors, and safety requirements?

Snippet-worthy stance: If modularity increases your operator workload, you didn’t buy flexibility—you bought complexity.

Where this is heading in 2026: modular robots as “physical apps”

Answer first: The next step is treating robot configurations like deployable capabilities—AI selects the form factor the way software selects a compute instance.

As AI in robotics matures, we’re trending toward an idea I like a lot: hardware as a substrate, software as the differentiator.

In 2026, I expect more vendors to pitch modularity as a way to:

• Extend robot lifespan through upgrades (swap modules instead of replacing the robot)
• Add specialized attachments (sensors, grippers, compliance modules)
• Expand from mobility to manipulation without a new platform

A “one quad or two bipeds” robot fits that trajectory. It’s a statement that mobility shouldn’t be a fixed identity.

If you’re building an automation roadmap, the question isn’t “Do we want a quadruped or a biped?” It’s: Do we want a platform that can adapt as our facility changes—without resetting our whole robotics program?

If you’re exploring AI-enabled modular robotics for logistics automation, manufacturing, or healthcare, the right next step is a structured pilot: define two environments with conflicting mobility needs, then measure mode-switching impact on throughput, safety incidents, and operator time.

What would you change in your facility first if your robots could reconfigure themselves on the fly—routes, tasks, or the way teams collaborate with automation?