Manipulation Orchestration#

Overview#

Manipulation orchestration provides a behavior tree-based framework for coordinating complex robotic manipulation tasks. Instead of writing rigid finite state machines, you compose reusable behavior components into hierarchical trees that automatically handle sequencing, error recovery, and parallel execution.

Why Behavior Trees?#

Traditional manipulation systems often use finite state machines (FSMs) to coordinate tasks, but behavior trees offer significant advantages for complex robotic systems [1] [2].

FSM vs Behavior Tree Comparison

Aspect

Finite State Machines

Behavior Trees

Scalability

State explosion: transitions often grow combinatorially [2]

Near-linear growth under common Behavior Tree designs [2]

Modularity

Tightly coupled states with explicit transitions make component reuse difficult [2]

Highly modular: reusable sub-trees can be independently developed and tested [1] [2]

Control Flow

Explicit state transitions must be manually defined for all paths

Implicit flow through Sequence, Selector, and Parallel nodes [1]

Error Handling

Recovery logic must be explicitly coded for each state transition

Systematic patterns via decorators (e.g., Inverter, Repeat/max-N-tries, Timeout/max-T-sec) applied consistently [1]

Reactivity

Requires manual implementation of interruption and preemption logic

Dynamic reactivity: trees re-evaluate from the root on each tick/input, enabling immediate responses [2]

Concurrency

Parallelism via hierarchical/parallel composition leads to combinatorial growth in states [2]

Natural concurrency: Parallel nodes with configurable success/failure policies [1]

Programming Effort

Significant code modification needed when adapting to new tasks [2]

Minimal modifications: compose existing sub-trees into new configurations [1] [2]

References

[1] “Behavior Trees in Robotics and AI: An Introduction” (2022). arXiv:1709.00084
[2] “Comparison between Behavior Trees and Finite State Machines” (2024). arXiv:2405.16137

Core Concepts#

Behavior Tree Structure

The orchestration system uses py_trees to implement:

  • Leaf Behaviors: Atomic actions (move robot, detect objects, grasp)

  • Composite Nodes: Control flow (Sequence, Selector, Parallel execution)

  • Decorator Nodes: Add robustness (Retry, Timeout, rate limiting)

  • Blackboard: Shared memory for inter-behavior communication

Implementation Resources#