Dr Andrew Singletary, CEO and Co-Founder of 3Laws.

What are the most significant challenges that AMRs face in real-world logistics environments, and how is 3Laws addressing them?

The key challenges are:

• Dynamic obstacle avoidance & unpredictability: Warehouses are bustling with forklifts, pedestrians, shifting inventory, and ad hoc human movements.
• Conservative safety margins: Traditional safety systems often simply stop or slow, reducing throughput.
• Heterogeneous fleets & sensor fusion: Different robots, sensor modalities, and compute architectures make integration complex.
• Perception in uncontrolled environments: Robots struggle to perceive the world in less structured environments, causing significant safety hazards and roadblocks to unsupervised autonomy.

The 3Laws approach:

• Optimal, real‑time constraint enforcement: Our safety layer sits between the autonomy planner and the robot controller, modifying commands only when necessary to guarantee collision‑free motion – without forcing a full stop.
• Model‑based guarantees: By leveraging a rigorous, mathematically proven safety‑critical control framework (originating in our Caltech research), we ensure no violations of workspace constraints, even in highly dynamic scenes.
• Sensor‑agnostic & modular integration: We support 3D lidars, cameras, and other common logistics sensors, and can drop in on top of existing autonomy stacks – minimising system redesign.
• Throughput uplift: By replacing overly conservative ‘stop‑and‑wait’ policies with smooth reactive adjustments, we increase average operating speed and reduce human interventions.

How do you see AMRs evolving beyond simple transport tasks to more complex operational roles in the warehouse?

• Higher levels of autonomy: Robots will be able to receive and execute on more open-ended tasks, requiring less pre-programmed behaviour and more intelligence.
• Mobile manipulation: AMRs will pick and stow individual items, collaborating with robotic arms or mobile manipulators to handle mixed pallets.
• Predictive maintenance & health monitoring: By playing dual roles as transporters and mobile sensing platforms, AMRs will gather data on equipment health, floor conditions, and environmental factors.
• Human-robot teaming: Rather than replacing humans, robots will become mobile "tool pallets," autonomously bringing specialised tooling or parts to human operators, dynamically adjusting to human pace and preferences.

How does 3Laws ensure safety and adaptability when AMRs operate in dynamic, human‑centric environments like distribution centers, with the addition of the intelligent layer?

• Formal safety guarantees: Our intelligent layer is built on well‑understood control‑theoretic constructs (barrier functions, reachability analysis) that mathematically enforce collision‑avoidance without relying on heuristics.
• Graceful intervention: Instead of hard stops, we adjust velocity vectors in real time—ensuring smooth, predictable robot behaviour that feels natural to nearby humans.
• Scenario‑based testing & validation: We run extensive hardware‑in‑the‑loop simulations against digital twins of each facility layout, augmented by live pilot programs, to tune performance and validate edge cases.
• Continuous learning & adaptation: Feedback from on‑site operations flows back into our parameter‑tuning pipeline, allowing the safety layer to adapt to new traffic patterns or facility modifications.

In your view, what has been the biggest game‑changer AI has brought to logistics and warehouse management over the past five years?

• Deep perception for unstructured environments: Advances in 3D vision and point‑cloud segmentation have empowered robots to "see" pallets, racks, and even loose items with unprecedented accuracy.
• End‑to‑end learning pipelines: From route planning to demand forecasting, AI models have shifted from rule‑based systems to data‑driven approaches, enabling more robust performance under variability.
• Reinforcement‑learning‑driven motion primitives: AI‑optimised motion policies that balance speed and safety have begun to replace hand‑tuned motion planners, improving cycle times.
• Cloud‑native orchestration platforms: AI‑enabled optimisation at the fleet level – for dynamic task allocation and real‑time rescheduling – has unlocked higher utilisation rates across heterogeneous fleets.

What role do you see AI playing in orchestrating multi‑robot coordination or fleet management within a warehouse?

• Global task allocation: AI schedulers will dynamically assign pick‑and‑place, charging, and maintenance tasks based on predicted workload and robot health.
• Decentralised coordination: Through improved robot interoperability, robots will negotiate passage and handoff in real time – reducing bottlenecks at chokepoints.
• Predictive congestion control: AI models trained on historical traffic patterns will preemptively reroute robots to avoid future slowdowns.
• Adaptive fleet scaling: By forecasting demand surges (e.g., seasonal peaks), AI can recommend when to deploy additional robots or reassign resources across multiple facilities.

With increasing concerns about data privacy and cyber‑resilience, how does 3Laws approach the secure use of AI and other software in mission‑critical logistics systems?

• On‑premise inference: All safety‑critical decision logic runs locally on embedded, local compute; no raw sensor data is streamed offsite in real time.
• Encrypted communication: We use industry‑standard TLS for all metadata and health‑status reporting, and ensure each robot enforces mutual authentication.
• Secure boot & code signing: Our software stack is locked down from the moment of power‑up, with cryptographic signature checks at every layer.
• Regular penetration testing & audits: We partner with external security firms to run quarterly vulnerability assessments, ensuring resilience against emerging threats.
• 3Laws is a redundant control layer: 3Laws ensures proper robotic behaviour outside of the core control system, adding an additional layer of redundancy, safety, and security.

As a CEO and technologist, where do you see the convergence of robotics and AI making the biggest impact in supply chains over the next decade?

• End‑to‑end autonomous fulfillment: From inbound receiving to final shipping, intelligent robotic systems will orchestrate every touchpoint – minimising manual handoffs.
• Bring manufacturing back to the US: As factories are reshoring, autonomy plays a critical role for our national security. 3Laws makes the absorption of autonomy possible and easy.
• Increase economic productivity: Robotic autonomy requires a considerable investment. 3Laws makes it possible to get more out of the investment by increasing throughput at every juncture where robots are used. Either more work gets done with the same number of robots or fewer robots will be needed to get the job done.
• Self‑optimising networks: AI will tie together warehouse operations, transportation logistics, and last‑mile delivery into a fluid, responsive network that adapts to disruptions in real time.
• Human‑centered automation: By combining safe, adaptive robotics with natural‑language interfaces and wearable AR, we'll create genuinely collaborative workcells that amplify human skills rather than replace them.
• Sustainability‑driven efficiency: Robots guided by AI will optimise energy usage, reduce idle time, and minimise waste – supporting greener supply‑chain strategies.

Dr Andrew Singletary is the CEO and co-founder of 3Laws, a cutting-edge robotics software company specializing in safety software for autonomous vehicles, aircraft, and mobile robots. With a PhD on safety-critical control from Caltech, Andrew's research has significantly advanced the understanding of how autonomous systems can be made safer and more reliable, preventing collisions and constraint violations in highly dynamic environments, earning him a spot on Forbes' 30 Under 30 list. At 3Laws, Andrew continues to push the boundaries of what's possible in autonomous mobility, ensuring that safety remains at the forefront of innovation.