What Is Mobile Manipulation?
Mobile manipulation refers to robots that combine locomotion (the ability to move through an environment) with manipulation (the ability to grasp, move, and interact with objects). This combination is what gives a robot human-like utility — the ability to navigate to where the work is, not just work on objects brought to a fixed location.
The combination is also what makes it hard. A fixed-base arm operates in a known, calibrated workspace. A mobile manipulator must grasp from an imprecisely-known base position, on a potentially moving platform, in an environment that may differ from any training configuration. Grasping from a non-fixed base increases effective grasp error by 3-5× compared to fixed-base manipulation.
Key Platforms in 2025
| Platform | Price | Type | Payload | Key Strength |
|---|---|---|---|---|
| Hello Robot Stretch 3 | $28,000 | Wheeled + telescoping arm | 1.5 kg | ROS2, elder care research, affordable |
| Spot + Arm (Boston Dynamics) | $100,000+ | Legged + arm | 4 kg | Rough terrain, enterprise support |
| Mobile ALOHA (Stanford) | $32K + base | Wheeled + bimanual | 3 kg each | Bimanual, open-source design |
| Unitree H1 | $90,000 | Humanoid (legged) | 5 kg | Humanoid form factor, growing ecosystem |
| Fetch Robotics (OTTO) | $150,000+ | Wheeled + arm | 6 kg | Warehouse logistics, enterprise AMR |
Key Challenges
Arm-base coordination: When should the base move and when should the arm extend? Whole-body control optimization for humanoid platforms (H1) treats base and arm as a unified kinematic chain. Wheeled platforms typically use decoupled planning — navigate base to within arm reach, then execute arm motion — which is simpler but suboptimal for dynamic tasks.
Dynamic stability during manipulation: Applying force through the arm creates reaction forces on the base. A legged robot applying 20N horizontal force through its arm during a constrained task must simultaneously adjust foot contacts to maintain stability. This whole-body force coordination is an active research problem.
Grasp pose estimation from mobile base: Perception from a moving base introduces localization uncertainty into grasp estimation. A 2cm base position error propagates to a grasp point error that may exceed grasp tolerance for precision tasks. Mobile manipulation systems need either high-precision base localization or grasp policies that are robust to base position uncertainty.
Algorithms for Mobile Manipulation
- Whole-body control (WBC): Treats the entire robot (base + arms) as a unified system. Computes joint torques that simultaneously satisfy base stability and arm trajectory constraints. State of the art for humanoids (H1, Atlas). Computationally expensive, requires accurate dynamics model.
- Decoupled arm/base planning: Plan base motion and arm motion independently, with the base moving to a pre-grasp pose before arm execution begins. Simpler implementation, works well for most practical tasks on wheeled platforms.
- Task-and-motion planning (TAMP): High-level planning that reasons over sequences of base moves and arm actions to achieve multi-step goals. Research frontier — teams at MIT, CMU, and Stanford have demonstrated impressive long-horizon mobile manipulation with TAMP, but real-time performance is still limited.
Real Deployment Examples
- Diligent Robotics Moxi: Wheeled mobile manipulator deployed in hospital wards for supply delivery and linen transport. Commercial deployment in 20+ US hospitals as of 2025.
- 6 River Systems (Shopify): AMR-based warehouse picking system that assists human workers. Handles structured bin picking, not full mobile manipulation.
- Hello Robot Stretch clinical trials: University of Washington and Georgia Tech trials of Stretch for in-home elder care assistance tasks (fetching objects, opening doors). Published results show 65-80% success on daily living tasks.
For teams building mobile manipulation solutions, SVRC's robotics solutions services include system design consulting and data collection for mobile manipulation tasks.