Section 42.7: Mobile Manipulation: Base, Arm, Perception, And Recovery

"A mobile manipulator is a negotiation between reachability and route planning."

A Whole-Body Systems Notebook

This section explains how base position, arm kinematics, visibility, and recovery policies interact in whole-body manipulation. Good mobile manipulators plan for navigation and grasping together, not as isolated modules.

It synthesizes earlier material on navigation, mapping, manipulation, and recovery into one application-grade system loop with explicit evidence artifacts.

Theory

The central object is a coupled cost over base pose, arm configuration, visibility, collision risk, and task progress. Local arm planning cannot be optimal if the base pose destroys reachability or line of sight.

This coupling is why mobile manipulation is a natural benchmark for embodied AI. It forces a policy or planner to reason across spatial scales and across multiple failure channels in one episode.

$$ (q_b^\star, q_a^\star) = \arg\min_{q_b, q_a} C_{\text{route}}(q_b)+C_{\text{reach}}(q_b,q_a)+C_{\text{view}}(q_b)+C_{\text{risk}}(q_b,q_a) $$

Worked Example

# Score candidate base poses for reachability, view, and risk.
candidates = [
    {"pose": "b1", "reach": 0.92, "view": 0.55, "risk": 0.20},
    {"pose": "b2", "reach": 0.80, "view": 0.90, "risk": 0.10},
    {"pose": "b3", "reach": 0.95, "view": 0.30, "risk": 0.35},
]

scores = []
for c in candidates:
    score = round(0.5 * c["reach"] + 0.4 * c["view"] - 0.6 * c["risk"], 3)
    scores.append((c["pose"], score))

scores.sort(key=lambda row: row[1], reverse=True)
print(scores)

Expected output: The expected ranking prefers the slightly less reachable pose with much better visibility and lower risk. That is often the right whole-body decision in homes, warehouses, and service settings.

Practical Recipe

1. Score base poses with visibility and retreat feasibility, not just arm reachability.
2. Refresh local perception after arriving at the base pose because small route errors matter near contact.
3. Reserve space for retreat and human-safe recovery before the arm starts moving.
4. Log route, base pose, arm plan, and failure branch in one artifact.
5. Benchmark on tasks where the first base pose is intentionally suboptimal so recovery is exercised.

Mobile manipulation is a clean example of multi-timescale reasoning. Global route planning runs over meters and seconds, while contact control runs over centimeters and milliseconds. Whole-body success depends on passing the right abstractions between those scales.

It is also a useful place to teach coupled evaluation. A navigation benchmark and a grasp benchmark can both look strong while the combined system fails because the interfaces between them were never optimized together.

When a task fails, ask whether the route, the base pose, the local perception refresh, the arm plan, or the retreat branch first violated its contract. Mobile manipulation only becomes debuggable once those labels stay separate.