Physical AI Lab

Design and test humanoid robot power architecture across battery BMS, DC bus, joint drives, auxiliary loads, regenerative braking and runtime.

Audit robot learning data quality with checks for time alignment, action frames, calibration, failures, intervention, duplicates, split leakage and lineage.

Choose AMR vs AGV using route flexibility, fleet control, safety, throughput, infrastructure, exception recovery and total lifecycle cost.

Compare stereo, ToF and structured-light robot depth cameras by range, surfaces, sunlight, motion, synchronization, calibration and task tests.

Choose robot CPU, GPU and NPU workloads from latency, memory bandwidth, power, thermal and profiling evidence instead of peak TOPS alone.

Secure ROS 2 and DDS robot commands with identities, keystores, enclaves, least privilege, network segmentation, certificate lifecycle and recovery tests.

Model robot joint friction with bidirectional sweeps, temperature and load tests, then validate feedforward, observers, reversal chatter and tracking.

Measure robot inference latency from camera exposure to actuator command, including queues, copies, preprocessing, model time, tail latency and data age.

Compare cogging torque and torque ripple in robot motors with no-current tests, angle and order analysis, current-loop checks and low-speed validation.

Learn robot functional safety PL and SIL targets, safety-function architecture, diagnostics, PFHd evidence, fault injection, stop testing and validation.