Robotics & Autonomous Systems
The Problem
The planned path violates joint limits that exist outside the token sequence. Trajectories assume instantaneous velocity changes because inertia is continuous physics that the model treats as discrete decisions. A loaded arm at full extension cannot reverse direction instantaneously, but "reverse direction" is a valid token.
How Ejentum Solves It
One API call forces your model to validate collision geometry, inertial limits, and boundary integrity at every planning step before the trajectory is finalized.
The Failures
- 01
The Pattern
Planned paths violate boundary integrity: walls, joint limits, and clearance constraints not enforced
Why It Happens
The model plans in token space where "move left 30 degrees" is always a valid sequence. Physical boundaries are external constraints that exist outside the generation process.
The Resolution
SP-001Topology ValidatorValidates every path segment against topological constraints: walls are impenetrable, floors support weight, and containment hierarchies are respected.
Supported bySP-002 Geometric Boundary Enforcer - 02
The Pattern
Trajectories assume instantaneous velocity changes, ignoring inertia and deceleration requirements under load
Why It Happens
Velocity changes are discrete decisions in the plan. Inertia and deceleration are continuous physics that must be computed, not predicted. The model generates the desired state without computing whether the transition is physically achievable.
The Resolution
SP-018Kinetic Momentum TrackerModels inertia, momentum, and force constraints for every movement. A robotic arm at full extension cannot reverse direction instantaneously.
Supported bySP-026 Stability Equilibrium Auditor - 03
The Pattern
Static obstacles accounted for but dynamic obstacles (moving humans, other robots, conveyor items) not predicted
Why It Happens
Static obstacle maps are part of the input context. Dynamic obstacles require future-state prediction over a time horizon, which is a simulation task the planning model does not perform.
The Resolution
SP-030Dynamic Scene ProjectorProjects the future positions of all dynamic entities in the scene, incorporating predicted trajectories into the planning constraint set.
Supported bySP-007 Trajectory Predictor
The Evidence
EjBench, 30 spatial tasks
Motion planning has strict physical constraints with single correct feasibility answers. A single scaffold that enforces intermediate state validation outperforms multi-ability injection. Spatial domain: Ki +7.4pp, Haki regressed -2.8pp.
Task tracked physical object positions through multiple state changes and perspective shifts. Baseline confused what the observer knew with where the object actually was. Haki enforced reference stability: each belief state anchored to direct observation only.
Run your next motion plan through the API. See how the scaffold catches the physics violation your planner generated as a valid token sequence.