MIT professor Sertac Karaman says small consumer drones are easier to make than self-driving cars, but scaling up delivery drones is much harder due to safety and regulation. He's positive on his own company Optimus Ride, which uses remote human monitors for multiple vehicles in closed areas like campuses, not full autonomy. He also thinks lidar (a laser-based sensor) may become so cheap that it's silly not to use it alongside cameras.
MIT professor and co-founder of Optimus Ride, Sertac Karaman, discussed the technical challenges of flying robots and driving robots on the Lex Fridman podcast. The core argument: current consumer-grade autonomous drones are easier to achieve than autonomous driving, but large-scale deployment of flying robots (e.g., logistics transport) is far more difficult than autonomous driving. Key conclusion: over the past 50 years, most deployed robots have operated in isolated environments or confined spaces; a truly large-scale, consumer-facing autonomous flight system has yet to emerge and is expected to be resolved only after large-scale deployment of autonomous driving. Karaman emphasizes that scaling is the core challenge, involving complex issues such as safety, regulation, and system reliability.
Karaman believes that autonomous flight is easier to achieve than autonomous driving in consumer drone scenarios (such as aerial photography), but once it involves large-scale logistics, transportation, and other applications, the difficulty will surpass that of autonomous driving.
> "We really haven't yet seen any kind of machine at massive scale, large scale being deployed and flown. And I think that's going to be after we kind of resolve some of the large scale deployments of autonomous driving."
Karaman points out that the core bottleneck of simulation technology has shifted from "simulating physics" to "simulating human behavior," with the latter being the most difficult challenge to overcome.
> "We're still missing what everybody else is going to do next. You want to know where you are, you want to know what everybody else is, and then you want to predict what other people are going to do. That last bit has been a real challenge."
Karaman emphasizes that the interaction between autonomous vehicles and humans is not only a technical issue but also a social and ethical one, involving a trade-off between "efficiency" and "sustainability (livability)."
> "If robots are going around being aggressive, you don't want to live in that environment. However, if you're not being aggressive, then you're probably taking up some delays in transportation. So you're always balancing that."
Karaman outlined Optimus Ride’s differentiated strategy: focusing on closed/semi-closed environments with “transportation scarcity” (such as campuses and naval shipyards), and achieving early deployment through “human-machine collaboration” (one person monitoring multiple vehicles) rather than full autonomy.
> “We want to go from that to 10 people operate 50 vehicles. How do we do that? The help shouldn't be for safety. Help should be for efficiency. Vehicles should be safe no matter what.”
Karaman is open to the notion that "LiDAR is a crutch," but believes the future is more likely to be dominated by "sensor fusion," and that LiDAR may become a "no-brainer" option as costs decline.
> "There will be a time when you can only use cameras and you'll be fine. At that time, it's very possible that you find the LiDAR system as another robustifier, or it's so affordable that it's stupid not to just put it there." (Meaning: There will come a time when cameras alone are sufficient. But by then, LiDAR may be so cheap that not adding it would be foolish.)
Karaman views drone racing as a testing ground for "high-throughput computing," a technology that could in the future enhance the safety of autonomous driving (e.g., by avoiding accidents).
> "We end up building systems that see things at a kilohertz, like a human eye would barely hit a hundred hertz. Imagine things that see stuff in slow motion, like 10X slow motion. That will be very useful."
| Position | Guest Sentiment | Key Data |
|---|---|---|
| Waymo | Neutral (viewed as a long-term research project) | No specific data provided |
| Tesla | Slightly positive (acknowledges its product and iteration strategy) | No specific data provided |
| Optimus Ride | Bullish (from the founder's perspective) | Target: 10 operators managing 50 vehicles; a 2-mile × 2-mile area could save tens of millions to billions of dollars in parking costs |
| NVIDIA | Neutral (viewed as a hardware partner) | No specific data provided |
| Lockheed Martin (AlphaPilot) | Neutral (viewed as a technology testing ground) | No specific data provided |
1. “Scalability is the true bottleneck for autonomous flight, not technical feasibility” (Karaman): Consumer drones are already viable, but large-scale logistics/transportation must first solve the scalability problem of autonomous driving, as ground environments are easier to “tame.”
2. “Simulating human behavior is far harder than simulating the physical world” (Karaman): Camera simulation is nearing an inflection point, but simulating human behavior (e.g., pedestrian gestures, driver intent) remains an “AI-complete” level challenge that cannot be solved through rule-based programming.
3. “The interaction between autonomous vehicles and humans is essentially a trade-off between ‘efficiency and livability’” (Karaman): Aggressive driving improves efficiency but reduces environmental livability; society must openly discuss this trade-off.
4. “Optimus Ride’s strategy is ‘human-machine collaboration’ rather than ‘full autonomy’” (Karaman): One human monitors multiple vehicles, with human intervention aimed at efficiency (not safety); the vehicle should remain safe in all circumstances.
5. “LiDAR is not a crutch; in the future, it may become a ‘why not use it’ option as costs decline” (Karaman): Pure camera systems are feasible, but LiDAR can reduce computational complexity; early deployments will use low-cost solid-state LiDAR.
6. “Drone racing is a testbed for ‘high-throughput computing,’ which could later be used to prevent traffic accidents” (Karaman): 1kHz perception combined with dedicated chips can take over a vehicle at the moment of an accident, with the technology originating from racing scenarios.
7. “The Bellman equation is the most beautiful equation in decision-making—it simultaneously reveals optimality and computational complexity” (Karaman): Theoretically optimal, but the “curse of dimensionality” can make its computation exceed the number of atoms in the universe in extreme cases; however, in practice, average cases are often feasible.
8. “Technology predictions suffer from a ‘time dilation’ effect—distant things are said to be near, and near things are said to be distant” (Karaman): People often “compress” all technological progress into the next three years, leading optimists to say “by year-end” and realists to say “in five years,” but the actual timeline is hard to predict.