Active Perception for Deformable Linear Objects Stiffness Estimation

Abstract

Estimating the stiffness of Deformable Linear Objects (DLOs) is crucial for robust manipulation. Inferring this hidden property depends heavily on the physical interaction strategy. Through a 1D CNN-based analysis of predefined probing modes, we first demonstrate that boundary constraints and grasp locations drastically alter stiffness identifiability. While fixed-end setups yield highly informative responses, they are rarely practical in unconstrained tasks. Consequently, we move beyond manual heuristics and reframe DLO parameter identification as an active perception problem. We propose a Reinforcement Learning (RL) framework that autonomously learns informative interaction strategies for free cables. By coupling a Proximal Policy Optimization (PPO) agent with a trajectory-aware estimator, the system dynamically excites the DLO to extract stiffness from diverse, stochastic manipulation sequences. Achieving a Mean Absolute Error (MAE) of 0.0192, our approach provides a robust, active paradigm that overcomes the limitations of static probing in unconstrained environments.