Posterior-driven Heuristic Support Adaptation in a Probabilistic Treatment of Real2Sim2Real for Vision-Driven Deformable Linear Object Manipulation
cs.RO, cs.LG
Submitted: 2025-10-30
Updated: 2026-09-09
Comments: 17 pages, 23 figures
License: http://creativecommons.org/licenses/by/4.0/
The gist: Likelihood-free inference (LFI) enables system identification in complex tasks via black-box modelling, abstracting nonlinearity and stochasticity, and infers a domain distribution for adapting
Terminology
Abstract
Likelihood-free inference (LFI) enables system identification in complex tasks via black-box modelling, abstracting nonlinearity and stochasticity, and infers a domain distribution for adapting agents to parametric deployment conditions. LFI assumes an arbitrary support for sampling, which remains fixed as the initial generic prior is refined to increasingly descriptive posteriors. Misspecified support can therefore yield suboptimal yet overconfident posteriors. We address this issue by using the posterior of an inference step to guide the adaptation of the support using three illustrative heuristics: EDGE, MODE, and CENTRE. Each heuristic interprets the updated belief and enables support adaptation alongside posterior inference. For illustrative purposes, we first study misspecified support in LFI and evaluate the utility of our heuristics using stochastic dynamical benchmarks. We then evaluate posterior-driven heuristic support adaptation for parameter inference and policy learning in a dynamic deformable linear object (DLO) manipulation task. Inference results in a finer length and stiffness classification for a parametric set of DLOs. When the resulting posteriors are used as domain distributions for sim-based policy learning, they lead to more robust object-centric agent performance.
Sources
- ASID: Active Exploration for System Identification in Robotic Manipulation
- Sim-and-Real Co-Training: A Simple Recipe for Vision-Based Robotic Manipulation
- Perspectives on Sim2Real Transfer for Robotics: A Summary of the R:SS 2020 Workshop
- Solving Rubik's Cube with a Robot Hand
- Proximal Policy Optimization Algorithms
- Gymnasium: A Standard Interface for Reinforcement Learning Environments
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