Plan a task, bounded by the safety plane

The challenge

Task planning benefits from inference, but nothing about physical limits can depend on a model being right.

The scenario

A cell needs a new pick-and-place sequence, and the team needs confidence it cannot exceed configured limits.

What goes in

Goal

What the robot needs to accomplish.

Envelope

Configured velocity, force and workspace limits.

What you can ask

Illustrative prompts. Results depend on your own data and are not deterministic.

• “Plan a pick-and-place sequence for this cell.”Copy
• “Which planned motions were rejected, and why?”Copy
• “What does this plan assume about the fixture?”Copy

How Clearception approaches it

How the work is structured. Each step shows what its output is grounded in.

1. 1

   Describe the goal

   Source

   State the task and the environment.

2. 2

   Plan

   AI-inferred

   Produce a candidate motion plan.

3. 3

   Check the envelope

   Source

   The deterministic safety plane accepts or rejects each motion.

4. 4

   Report rejections

   Source

   Say which motions were refused and against which limit.

What you get

The shape of what comes back — not a promised result.

• Accepted plan

  Source

  A motion plan with every element checked against configured limits.

Why it matters

Inference stays out of safety

Limits are enforced by rules, independent of the planner.