Foci Policy: Focus on Object-Centric Interactions for Relational Manipulation Policies

CoRL 2026  ·  ICRA 2026 Beyond Teleoperation Workshop

KU Leuven

Highlights

Main Takeaway: Object-centric interaction modeling enables one-shot relational manipulation.

Key Insight

Relational manipulation can be simplified through two complementary abstractions:

1. Spatial abstraction reduces trajectory variability.

Spatial abstraction of relational manipulation

2. Temporal abstraction isolates the only part that matters.

Temporal abstraction of relational manipulation

Method Overview

FOCI POLICY method overview

Foci Policy predicts relative SE(3) trajectories between task-relevant entities instead of end-effector trajectories.

FOCI POLICY method overview

Interaction intervals are automatically detected from demonstrations using change-point detection over kinematic and geometric signals.

Foci Policy in RLBench

Only 1 demonstration is provided for each task.

Meat on Grill

Phone on Base

Books on Shelf

Plate in Rack

Toilet Roll on Stand

Umbrella in Stand

Reach and Drag

Screw Nail

Slide Block

Stack Blocks

Stack Wine

Turn Tap

Foci Policy in Real-World

One-Shot Learning

Foci Policy learns each manipulation task from a single demonstration and generalizes across diverse object configurations.

Insert Tube

Open Drawer

Pour Cup

Scale Grape

Sweep Dust

Cross-Gripper and Scene Transfer

The same policy transfers from the original gripper to a Robotiq gripper and adapts to new backgrounds easily, without retraining the model.

Scale Grape

Insert Tube

Open Drawer

Robust Execution in Complex Scenes

By separating task-critical interactions from unconstrained transport motion, Foci Policy can combine learned interaction trajectories with motion planning for obstacle avoidance and reliable execution in cluttered scenes.

Obstacle Avoidance

Cluttered Scene

BibTeX

@misc{fu2026focipolicy,
      title={FOCI Policy: Focus on Object-Centric Interactions for Relational Manipulation Policies}, 
      author={Ze Fu and Pinhao Song and Yutong Hu and Renaud Detry},
      year={2026},
      eprint={2609.08743},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2609.08743}, 
}

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