Humanoid Robots

01Scene and object understanding from RGB cameras, depth cameras, 3D LiDAR, IMU.

02Calibration so sensor coordinates match robot coordinates.

03Uncertainty estimation for glare, dust, occlusion, reflections, poor lighting, smoke, water, or repeated geometry.

04Semantic perception when the robot must understand people, tools, shelves, surfaces, parts, terrain, crops, rooms, or assets.

01Odometry from wheels, joints, inertial sensors, visual motion, acoustic sensing, or external references.

02Maps may represent geometry, semantic objects, safety zones, inspection assets, crop rows, racks, or work cells.

03Robust systems detect when maps are stale or localization confidence is low.

04Fallback behavior is critical because a robot using the wrong map can become unsafe.

01Motion control for torque controlled electric joints, series elastic actuators, dexterous robotic hands, harmonic drives.

02Trajectory tracking with speed, acceleration, force, thermal, collision, and payload constraints.

03Recovery behaviors such as retry, reverse, replan, slow down, dock, ask for help, or safe stop.

04Human override and audit logs so operators can understand failures.

01Common sensors: RGB cameras, depth cameras, 3D LiDAR, IMU, joint encoders, force torque sensors.

02Movement and tools: torque controlled electric joints, series elastic actuators, dexterous robotic hands, harmonic drives, compliant feet.

03Compute: embedded CPUs, GPUs, microcontrollers, motor drivers, safety controllers, and networking.

04Mechanical design: stiffness, cable routing, ingress protection, cooling, service access, weight, and repairability.

05Power: batteries, charging docks, tethering, hot swap packs, or vehicle power depending on the environment.

01Start with one narrow workflow where success and failure are measurable.

02Map the environment, human handoffs, charging needs, cleaning needs, network coverage, and safe stop locations.

03Track uptime, task completion rate, manual interventions, maintenance time, and safety incidents.

04Expand only after the robot proves reliability over weeks, not after one impressive video.

01task success rate

02mean time between intervention

03safe stop frequency

04cycle time

05energy per task

06maintenance time

07operator workload

08total cost per useful task

01sensor occlusion or calibration drift

02unexpected object geometry

03battery or thermal limits

04network loss

05mechanical wear

06software edge cases

07operator confusion

01reliable perception in messy environments

02long duration autonomy

03safe contact with people and objects

04cost reduction without losing robustness

05data quality for robot learning

06integration with existing workflows