dingxingdi Skill: symbolic-reactive-replanning-and-repair
1. Capability Definition & Real Case
- Professional Definition: The capability to perform closed-world symbolic reasoning to detect environmental deviations—spanning discrete object state changes to broader graph/topological revaluations—and dynamically generate a restorative or alternative action sequence to satisfy terminal goal predicates from the disrupted state. It incorporates a self-reflective loop consisting of pre-condition verification (logic checks before an action) and post-condition evaluation (verifying execution effects) to trigger recursive replanning or task retirement.
- Dimension Hierarchy: Closed-World Symbolic Planning->Dynamic and Adaptive Plan Orchestration->symbolic-reactive-replanning-and-repair
Real Case
[Case 1]
- Initial Environment: A mobile robot with a single gripper is standing in front of a microwave. A carrot is observed on the counter. The microwave door is currently closed.
- Real Question: Place the carrot into the microwave to heat it up.
- Real Trajectory: 1. [Initial Plan]: Pick up carrot -> Open microwave door -> Place carrot in microwave. 2. [Execution]: Robot picks up the carrot. 3. [Pre-condition Check]: Before 'Open microwave door', the robot checks its state. Pre-condition 'Hand-Empty' for the 'Open-Door' action is False because it is holding a carrot. 4. [Reflection & Repair]: The robot identifies the collision of constraints (holding object vs. operating door). It generates a repair: Put down carrot -> Open door -> Pick up carrot -> Place inside.
- Real Answer: put_down(carrot, counter) -> open(microwave_door) -> pick_up(carrot) -> place_in(carrot, microwave)
- Why this demonstrates the capability: This case demonstrates condition-check based reactive replanning. The agent performs a 'pre-condition' check that reveals its current symbolic state (holding an object) makes the next planned action (opening a door) impossible, forcing a dynamic insertion of restorative steps to satisfy the door's requirements.
[Case 2]
- Initial Environment: Two robots (Robot A and Robot B) are in a kitchen. Robot A is near the counter with a vegetable; Robot B is near the microwave. The microwave door is closed.
- Real Question: Coordinate a plan to put the vegetable in the microwave efficiently.
- Real Trajectory: 1. [Initial Plan]: Robot A picks up vegetable -> Robot A opens microwave -> Robot A places vegetable. 2. [Reflection]: Robot A realizes that opening the door while holding the vegetable is inefficient or blocked. 3. [Parallel Repair]: Robot A assigns 'Open microwave' to Robot B (who has empty hands) while Robot A performs 'Pick up vegetable'. 4. [Execution]: Robot B opens the door and Robot A simultaneously picks up the item. Robot A then places the item in the now-open microwave.
- Real Answer: Parallel([Robot A: pick_up(vegetable), Robot B: open(microwave)]) -> Robot A: place_in(vegetable, microwave)
- Why this demonstrates the capability: This demonstrates multi-agent belief-driven reactive repair. The planner identifies a physical bottleneck (one robot cannot hold and open simultaneously) and refines the plan by delegating enabling subtasks to a partner, maximizing parallelism and bypassing the single-agent state constraint.
[Case 3]
- Initial Environment: A symbolic workspace contains a Red block, a Blue block, and an Orange block. The Red block is clear; the Blue block is on the table; the Orange block is on top of the Blue block.
- Real Question: Your goal is to have the Orange block on top of the Red block. You started following a plan: 1. Unstack the Orange block; 2. Stack it on the Red block. However, after 'unstack', an event occurred: the Blue block was moved so that it is now on top of the Red block.
- Real Trajectory: 1. [Observation]: Detects that Red block is no longer clear (Blue is on it). 2. [Back-tracking]: Inferred that the 'Stack on Red' action's pre-condition (Clear Red) is now violated. 3. [Repair]: Unstack Blue from Red -> Put down Blue on table -> Stack Orange on Red.
- Real Answer: unstack(blue, red) -> putdown(blue) -> stack(orange, red)
- Why this demonstrates the capability: This case illustrates structural revaluation and repair. The agent detects an environmental change that invalidates the remaining steps of its original plan and must dynamically generate a new symbolic sequence to restore the required pre-conditions for its goal.
Pipeline Execution Instructions
To synthesize data for this capability, you must strictly follow a 3-phase pipeline. Do not hallucinate steps. Read the corresponding reference file for each phase sequentially:
Phase 1: Environment Exploration Read the exploration guidelines to discover raw knowledge seeds:
references/EXPLORATION.mdPhase 2: Trajectory Selection Once Phase 1 is complete, read the selection criteria to evaluate the trajectory:
references/SELECTION.mdPhase 3: Data Synthesis Once a trajectory passes Phase 2, read the synthesis instructions to generate the final data:
references/SYNTHESIS.md