Investment in humanoid robotics is accelerating, but moving from prototypes to widespread commercial deployment will require advances beyond AI models and mechanical design. Real-time perception, power efficiency and the ability to integrate increasingly sophisticated sensor systems are becoming critical challenges as developers work toward production-ready humanoid robots.
3D sensing is emerging as an important technology for enabling humanoids to perceive and interact with their surroundings. At the same time, robotics platforms need computing architectures that can deliver low-latency processing while remaining flexible enough to accommodate evolving AI models, sensor configurations and hardware requirements. FPGAs can play an important role in this environment by providing flexible, low-power computing for real-time 3D sensing and edge processing.
To explore the growing role of 3D sensing and FPGA technology in humanoid robotics, RoboticsBiz.com spoke with Karl Wachswender, Senior Principal System Architect at Lattice Semiconductor. Karl focuses on developing and advancing FPGA solutions for industrial applications, with expertise spanning industrial automation, edge AI, robotics and real-time processing.
In this interview, Karl discusses the role of 3D sensing in humanoid robots, the challenges involved in processing sensor data in real time, and how FPGA-based architectures can help developers build more flexible and efficient robotic systems.
Here is the complete interview:
1. As humanoid robots move from lab prototypes to commercial deployment, what are the biggest computing bottlenecks you’re seeing around real-time perception and power efficiency — and why do traditional processing architectures often fall short here?
Real-time robot perception demands sub-10ms closed-loop control at the joint level while simultaneously running higher-level scene understanding at 30-60Hz. Traditional CPU/GPU architectures are optimized for throughput — batching work to amortize overhead — which directly conflicts with the deterministic, low-latency responses a robot needs when it stumbles or encounters an unexpected obstacle. GPUs in particular impose unpredictable scheduling latency that is acceptable in data centers but problematic on a robot catching itself from a fall.
A humanoid integrates IMUs, proprioception from dozens of joints, stereo or depth cameras, tactile sensors, and often lidar or radar — all with different sample rates and data formats. Fusing this in real time requires tight temporal alignment and constant-bandwidth processing. General-purpose processors handle irregular, bursty workloads poorly; they’re designed around memory hierarchies optimized for reuse patterns that don’t match streaming sensor data well.
The “always-on” demands of humanoids present a significant bottleneck to progress. These devices must be ready to take action at any moment, while being powered by batteries with a limited power supply. They also operate with limited compute power, as centralized processors like GPUs can only take on so many edge workloads before they become overloaded.
These power and computing limits present a problem for traditional processing architectures, especially as increasingly powerful AI models are deployed at the edge. Constant high-volume processing can quickly drain the allocated power budget of a centralized chip, as well as introduce noticeable latency into the robot’s real-time responsiveness.
Low power processing options like field-programmable gate arrays (FPGAs) can help offset this challenge by offloading complex workloads from other edge processors. For real-time perception applications, that means helping with tasks such as sensor fusion, depth processing, object tracking, and disruption detection, reducing latency without significantly increasing power consumption. This ensures that humanoid robots are capable of real-time, precise response.
2. Where specifically do FPGAs outperform GPUs or fixed-function ASICs in humanoid robotics workloads, particularly for real-time 3D sensing and low-latency edge processing?
FPGAs really shine in high-priority processing scenarios that require real-time, deterministic responsiveness. Their low-power control capabilities enhance performance at the edge, with cycle-accurate timing guarantees allowing them to maintain always-on monitoring without draining the device’s limited battery power. Another significant strength comes from their reprogrammability, which allows developers to adjust an FPGA’s role after deployment to keep up with evolving needs and workloads.
It’s worth noting, though, that FPGAs aren’t necessarily best used as a replacement for GPUs. They are very good at what they’re designed to do and often serve even better as companion chips than full-on replacements. While ASICs can match FPGAs in some of the areas above, the latter’s flexibility is highly desirable, as it helps extend device lifespan.
A mid-task FPGA can partially reconfigure a region of fabric while the rest continues running — swapping in a different perception algorithm based on context (close-range manipulation vs. long-range navigation) without rebooting or stalling the control loop. No ASIC can do this; a GPU can context-switch software but pays latency and power costs
The realistic architecture is not FPGA or ASIC/GPU — it is FPGA as the real-time fabric sitting between sensors and a neural accelerator. The FPGA handles timestamping, protocol bridging, preprocessing, and hard-deadline control outputs; the GPU/NPU handles inference on pre-processed, time-aligned data. Boston Dynamics, Agility, and several others have used exactly this split. The FPGA absorbs the I/O chaos so the neural accelerator sees a clean, synchronous input stream.
3. Humanoid platforms are evolving quickly, with sensor configurations and AI models changing from one design cycle to the next. How does the reconfigurability of FPGAs help developers keep pace without redesigning hardware or blowing their power budgets?
Hardware development for humanoids currently runs on 12-24 month cycles, but AI model improvements and sensor upgrades happen every 3-6 months. That mismatch is the core problem. FPGAs decouple the firmware/algorithm lifecycle from the hardware lifecycle — the board ships, and the intelligence layer evolves independently via bitstream updates. This is essentially the same argument that made FPGAs dominant in base station radio: the air interface standards changed faster than silicon could be respun.
This is one of the key differentiators of FPGA hardware, especially at this stage in humanoid platform development. Things are changing fast, and operators cannot be expected to buy and install entirely new hardware every time they need to adjust or expand their system.
Reconfigurability turns a “rip and replace” scenario into a firmware update. FPGAs can also accommodate a wide range of workloads, allowing developers to shift “just in case” processing potential out of central compute, with the knowledge that the FPGA can take on more down the road. This lowers overall development costs, controls energy use amid changing demands, and helps ensure continued humanoid operations.
4. Can you walk us through a specific example — such as your work with partners like Airy3D — of how FPGA-accelerated depth processing frees up compute resources for higher-value AI tasks in a humanoid or robotic system?
Though digital depth processing has been possible for a while now, these solutions have relied on active technology (like structured light) or multicamera setups. Both work, but neither is ideal. Small changes in camera placement can impact accuracy in multicamera builds, and active options increase power needs in always-on contexts.
Our collaboration with Airy3D reimagines 3D visual perception to advance humanoid grasping capabilities. It places the vision sensor directly on a robotic hand to enable 3D vision at close range, using a single, passive camera module. Our CrossLink™-NX FPGA is the engine behind this advancement, running Airy3D’s DepthIQ software directly on the device to drive a real-time feedback loop to guide motion, while keeping central compute free to continue processing other workloads.
5. Looking ahead, as humanoid robots take on more complex, multi-sensor perception tasks, what capabilities do you think FPGA architectures will need to develop next to keep up with the pace of AI innovation?
The next phase of optimization will move beyond a single component’s capabilities, focusing more on holistic performance. As systems get more complex and operators’ expectations rise, the challenge will be keeping the growing volume of high-performance sensors in sync and operational, all without blowing power or compute budgets.
Hardware manufacturers across specialties will need to focus on progress that makes data movement and synchronization more reliable and deterministic. For FPGAs, this will mean advancing deterministic pipeline architectures: hardening the timing guarantees between sensor ingestion, preprocessing, and handoff to inference accelerators, so that latency is not merely low on average but bounded in the worst case.
Concretely, that requires tighter on-chip memory integration to eliminate off-chip round-trips, standardized IP for the sensor protocols humanoid platforms actually use, and partial reconfiguration capabilities mature enough to hot-swap perception pipelines without interrupting the real-time control loop. But we’ll also need to work closely with specialists in other humanoid technologies to ensure the solutions we identify come together to create seamless, efficient, and capable systems.
