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For a long, smooth route that both machines can complete, a wheeled delivery robot is the likely energy-efficiency choice. Wheels usually avoid the continuous joint-support work and repeated foot-ground impacts required by a quadruped. An electric robot dog can become the better operational option when stairs, curbs, loose ground, slippery surfaces or other obstacles make a wheeled route impossible or force a major detour.
There is no published, controlled head-to-head trial of a robot dog and a wheeled delivery robot carrying the same payload over the same long route. The right decision therefore depends on route feasibility, payload, usable battery energy, stops, charging and the service result—not on a single range number.
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The decision in one sentence
Choose wheels when the route is paved, continuous and accessible to both platforms; choose legs when access over obstacles changes whether the route can be completed at all. That is an operational conclusion, not a claim that every wheeled machine consumes less energy in every condition.
Efficiency should be specified before comparing designs. “Energy per kilometre,” “energy per parcel,” “energy per delivered kilogram-kilometre,” completion time, operating cost and life-cycle greenhouse-gas emissions are different measures. A platform can use little energy while moving but still deliver fewer parcels, wait longer at crossings or require more human intervention.
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Why wheels usually lead on smooth, shared routes
Rolling avoids continuous body-support work
A wheeled chassis supports its mass through the wheel-ground contact and uses motors mainly to overcome rolling resistance, aerodynamic drag, slopes and acceleration. Real systems also draw power for steering, sensors, computers, communications, refrigeration or other payload equipment. On a level, predictable surface, the rolling mechanism avoids repeatedly lifting and stabilizing a body segment.
Legs pay an unavoidable support and contact penalty
The Nature study of the RAIBO2 quadruped describes the distinction directly: “Unlike wheeled robots, quadrupeds continuously expend energy at their joints to support body weight and incur kinetic energy losses during intermittent foot–ground contact.” (Nature, 2026) Each step requires coordinated joint torques, foot placement and recovery of the robot’s motion. Efficient gait control can reduce those losses, but it does not turn a legged machine into a rolling chassis.
Why that does not prove a universal wheel advantage
A wheeled robot that cannot cross a staircase, curb or eroded path has no useful energy efficiency on that route. A detour can add distance, stops and battery demand, while a legged robot may take a direct line. The relevant comparison is therefore energy and service output for a feasible route, not propulsion energy on an identical flat test track.
What the strongest available evidence actually shows
The studies below illuminate separate parts of the decision. None supplies a matched wheel-versus-leg delivery test.
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| Source and evidence type | Finding relevant to the comparison | What it does not establish |
|---|---|---|
| Nature (2026), RAIBO2 quadruped study | RAIBO2 completed 42.195 km in 4 hours, 19 minutes and 52 seconds on one charge, with a reported total cost of transport (COT) of 0.25. The authors say its range per charge was more than three times that of existing quadrupeds. | It was a marathon demonstration, not a delivery trial and not a comparison with a wheeled delivery robot carrying the same load. |
| Energies (2022), wheeled-robot model | In synthetic driving cycles based on Helsinki food-delivery trips, a proposed 15 km/h design used more than 40% less modeled energy than the same design capped at 6 km/h. Auxiliary power represented more than half of modeled demand. | The result is a simulation comparing speed settings within one proposed wheeled design, not a field measurement or a legged-versus-wheeled result. |
| IEEE CASE (2025), legged-robot planning | The abstract describes selecting battery replacements, splitting payloads and reducing speed to minimize completion time on slopes that vary along the route. | It does not provide a universal range, payload limit or energy margin for all robot dogs. |
| Journal of Cleaner Production (2026) | A model of last-mile modes in 90 European pedestrian zones shows why city setting and route type matter when evaluating emerging delivery modes. | It does not identify one robot architecture as the winner on every long route. |
| Complex & Intelligent Systems (2026) | A modeled electric-van system carrying and coordinating autonomous delivery robots, with mobile charging and split delivery, reported a 33.84% average operating-cost reduction versus its modeled diesel-van baseline. | The percentage is a system cost result, not energy savings and not a quadruped-versus-wheeled comparison. |
| Environmental Science & Technology (2021) | Across studied automated last-mile parcel scenarios, the robot contributed 2–6% of modeled life-cycle greenhouse-gas emissions. | That percentage is scenario-specific life-cycle accounting, not propulsion energy per kilometre. |
RAIBO2’s result is still important: it demonstrates that a quadruped can achieve unusually low transport cost and long range relative to earlier quadrupeds. Nature’s report says the marathon course used varied terrain, but the measurement should not be presented as proof that robot dogs beat wheels for delivery logistics. (Nature Portfolio press coverage, 2026)
Route surface and access can reverse the practical choice
Pavement, warehouse floors and other continuous surfaces
On smooth pavement or a level indoor floor, a wheeled robot can maintain rolling motion with few gait transitions. If both platforms have the same delivery stops, compare their measured watt-hours for the route and the useful load delivered. Do not infer the result from the robot’s top speed or an unloaded demonstration.
Curbs, stairs and broken paths
Wheels may need ramps, a different sidewalk, a human lift or a detour. A quadruped can place feet independently and negotiate some discrete obstacles, although capability depends on its locomotion controller, foot traction, body clearance and payload stability. The available studies do not quantify a delivery-specific obstacle break-even distance; measure the actual route.
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Legged access can preserve a direct route where a small wheel sinks, slips or loses contact. It can also increase control effort and reduce speed. A wheel design with appropriate tires, suspension or larger diameter may perform better than a generic wheeled assumption, so the platform’s actual hardware and weather envelope matter.
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Crowds, crossings and operating restrictions
Safety policies can force a delivery robot to travel at walking speed, wait at crossings or stop for people. The 2022 wheeled-robot model warns that longer trip times add idling and stops, and that traffic and pedestrian density affect how speed translates into energy. A robot that is fast in a laboratory may not complete an urban route quickly.
Payload, battery and range are coupled
Useful load is the number that matters
Compare payload mass and volume in the intended compartment, not the robot’s empty mass. Food, parcels, insulation, locks and packaging all consume capacity. As payload rises, acceleration, climbing and support work generally require more energy, and a heavier machine may need different tires, gait parameters or stopping distances.
Usable battery energy is not the nameplate capacity
Plan with the battery energy available inside the manufacturer’s operating limits, then reserve energy for acceleration, slopes, weather, idle time, communications and a safe return or charging stop. A quoted “range” without payload, speed, terrain, temperature, traffic and reserve conditions cannot be transferred to another route.
Service range is more useful than travel range
For a delivery operation, calculate how many successful drops fit into one battery cycle, including loading, unlocking, customer handoff, waiting, intervention and the return leg. A robot with longer empty-travel range may serve fewer customers if it carries less or spends more time stopped.
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Battery replacement and charging change the route plan
The IEEE CASE work explicitly treats battery replacement, payload splitting and speed reduction as planning decisions for legged deliveries. (IEEE CASE, 2025) A swap can reduce turnaround time but requires charged inventory, staff or automation. Charging in place may be simpler but can create a long service interruption. Include those minutes and the energy used by the charging infrastructure in the route model.
Speed is not the same as efficiency
The Energies study modeled a proposed high-speed wheeled delivery robot with synthetic cycles derived from food-delivery trips in Helsinki. Its 15 km/h maximum-speed case used more than 40% less modeled energy than its 6 km/h case. The authors also found that auxiliary power accounted for more than half of demand. (Vepsäläinen, Energies, 2022)
That counterintuitive result comes from the model’s longer exposure to auxiliary loads, idling and stops at the lower speed. It is not evidence that every robot should travel faster. A higher speed can increase aerodynamic and traction losses, braking energy, collision risk and regulatory constraints. Use the speed that minimizes energy per completed delivery under the route’s safety rules, rather than optimizing cruise speed alone.
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Use the right efficiency metric
| Metric | What it answers | What to include |
|---|---|---|
| Energy per kilometre (Wh/km) | How much electricity the robot uses to move a route distance | Drive motors, steering, sensors, compute, communications, idle and stop energy. |
| Energy per delivered kilogram-kilometre | How efficiently useful payload is moved over distance | Delivered payload mass, route distance and all route energy. |
| Energy per parcel or customer | How much electricity produces one successful delivery | Stops, failed attempts, loading, handoff and return or recharge trips. |
| Route completion time | How quickly the service is finished | Cruising, crossings, waiting, charging or swapping, interventions and loading. |
| Operating cost | What the operation spends | Energy, labor, supervision, maintenance, support vehicles, charging and asset use. |
| Life-cycle greenhouse-gas emissions | Climate impact across the modeled life cycle | Manufacture, electricity carbon intensity, vehicle use, maintenance and delivery density. |
Keep these measures separate. The 33.84% operating-cost reduction in the electric-van study is against a diesel-van baseline for a coordinated system, not a statement about robot energy consumption. (Complex & Intelligent Systems, 2026) Similarly, the 2–6% robot share in the life-cycle study is not a propulsion-energy share. (Environmental Science & Technology, 2021)
A practical comparison procedure
- Map the real route. Record surface type, gradients, stairs, curbs, crossings, narrow sections, crowd density, weather exposure and no-go areas. Measure the route actually available to each platform, including any wheeled detour.
- Define the delivery job. Specify payload mass and volume, number of stops, delivery windows, handoff method, return requirement and acceptable intervention rate.
- Set comparable battery rules. Use the same reserve policy, ambient conditions and charging accounting. Record usable watt-hours rather than only nominal battery capacity.
- Instrument every load. Log traction energy, onboard computers, sensors, communications, refrigeration or other auxiliary equipment, idle time and charging losses. The auxiliary share can dominate, as the 2022 model illustrates.
- Run repeated route trials. Test loaded and unloaded cases, normal and adverse surfaces, realistic stop spacing and the required safety speed. Report measured results separately from simulations.
- Record service output. Count completed deliveries, failed attempts, human interventions, stops, battery changes and total elapsed time.
- Calculate normalized results. Report Wh/km, Wh per parcel, Wh per delivered kilogram-kilometre and deliveries per charge, each with payload and route conditions.
- Stress-test the plan. Recalculate for heavier loads, colder weather, slower pedestrian-zone operation, a blocked segment and one missed delivery. A design that wins only under ideal conditions is not necessarily the efficient operational choice.
When a mothership is more efficient than either robot alone
Long-distance logistics often work better as a system. An electric van or another carrier can move smaller autonomous robots near a service area, provide mobile charging and collect completed units. The 2026 electric-van location-routing model includes mobile charging and split delivery, while accounting for battery-limited range, walking-speed operation for safety and payload capacity. Its reported 33.84% average operating-cost reduction is relative to that study’s diesel-van baseline. (Complex & Intelligent Systems, 2026)
This architecture can shorten the robot’s unproductive travel and avoid asking either a dog or a wheeled unit to cover an entire metropolitan distance. Evaluate the van, robot, chargers, battery inventory, driver or supervisor time and loading delays together. A small robot’s low energy per kilometre does not automatically make a full-distance deployment efficient if a support vehicle must shadow it.
Scenario-based choices
Long, flat campus or industrial park
If paths are continuous and both machines can carry the required load, start with a wheeled platform. Measure auxiliary and idle energy carefully, because those loads can outweigh propulsion energy during long waits.
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First test whether a wheeled route can use ramps without an excessive detour. If not, a quadruped may deliver more efficiently at the system level by preserving direct access, even if its instantaneous energy per metre is higher.
Mixed route with a short obstacle section
Consider a hybrid plan: use wheels for the long smooth segment and transfer the parcel to a legged unit only where access requires it, or use a mothership to stage the appropriate robot. Include transfer labor, synchronization and extra battery capacity before assuming a gain.
Very long urban route with charging support
Compare a full-distance robot mission with a carrier-supported plan. Charging or swapping locations, walking-speed restrictions, crowd delays and the number of customers per sortie may dominate the propulsion choice.
Common mistakes that produce misleading answers
- Using a marathon as a delivery range claim. RAIBO2’s 42.195 km demonstration and COT of 0.25 describe a quadruped performance event, not parcel service.
- Calling the 40% figure a wheel-versus-leg result. It compares 15 km/h and 6 km/h cases in a modeled wheeled design.
- Comparing empty robots. Payload changes energy, handling and the number of customers served.
- Ignoring auxiliary power. In the 2022 proposed wheeled design, auxiliary demand exceeded half of modeled demand.
- Equating range with productivity. Stops, waiting, charging, interventions and failed deliveries determine service output.
- Calling operating-cost savings energy savings. The 33.84% figure belongs to a coordinated electric-van system against a diesel-van baseline.
- Turning life-cycle percentages into motor efficiency. The 2–6% robot emissions share applies to particular last-mile scenarios.
- Assuming a universal commercial specification. Battery, payload, weather tolerance, speed and terrain capability vary by model; no single class-wide range is established.
Bottom line
For long, smooth routes where both designs are genuinely feasible, wheels are the defensible starting point for energy efficiency. For routes with stairs, curbs, loose or slippery ground, or obstacles that create a substantial wheeled detour, a robot dog can be the better operational choice because access changes the route itself. Make the decision with measured energy per completed delivery, payload, route time and support requirements—not with an isolated range claim or a cross-category headline number.
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