Contact-Aware Probabilistic Reconstruction for Contact-Rich Manipulation

Aditya Kamireddypalli, Joao Moura, Russell Buchanan, Matias Mattamala, Sethu Vijayakumar, Subramanian Ramamoorthy

Keywords: Perception for Manipulation, Contact-aware manipulation

Abstract:

Robotic assembly tasks such as peg-in-hole inser-
tion require precise geometric reasoning, yet visual sensing noise
in real-world systems often exceeds the tight tolerances required
for successful insertion. In this work, we propose ContactFusion,
a probabilistic mapping framework that fuses depth sensing
and force–torque measurements to estimate the geometry of
insertion targets. Our method builds a Stochastic Poisson
Surface Map (SPSMap), an uncertainty-aware implicit surface
representation constructed using Stochastic Poisson Surface
Reconstruction (SPSR). To incorporate contact information,
we introduce a sampling based contact location estimator that
converts force–torque measurements into spatial hypotheses
over candidate contact locations on the robot end-effector.
These hypotheses are fused with depth observations within
a sequential reconstruction framework, enabling the map to
be refined through both visual and contact interactions. We
evaluate ContactFusion in simulation and on a real robotic
system in a peg-in-hole setting. Our results show that SPSMap
produces more accurate and geometrically consistent recon-
structions, improving reconstruction F-score by up to 30–
35%, while providing uncertainty estimates that enable active
reconstruction strategies.

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Submission Number: 5