Adapting reinforcement learning (RL) policies to various robot body configurations is a significant challenge for creating flexible autonomous systems. This study presents a novel framework that integrates Reservoir Computing (RC) with the First-Order Reduced and Controlled Error (FORCE) learning rule to enhance policy adaptability in RL. The RC serves as a dynamic feature extractor, capturing temporal dependencies by pre-training on state transitions generated through random actions. This pre-training acts as regularization, reducing variance and preventing overfitting to specific configurations Subsequently, the control policy network is trained on a limited set of body variations using the enriched features from the RC. Experimental results across three distinct environments demonstrate that the proposed RC+FORCE framework significantly improves policy performance and adaptability to unseen robot configurations compared to traditional reinforcement learning through domain randomization. These findings highlight the effectiveness of combining RC-based feature extraction with FORCE-based training in developing robust RL agents.

In this paper, we propose a measurement-based modeling framework for hyper-elastic material identification and real-time haptic rendering. We build a custom data collection setup that captures shape deformation and response forces during compressive deformation of cylindrical material samples. We collected training and testing sets of data from four silicone objects having various material profiles. We design an objective function for material parameter identification by incorporating both shape deformation and reactive forces and utilize a genetic algorithm. We adopted an optimization-based Finite Element Method (FEM) for object deformation rendering. The numerical error of simulated forces was found to be perceptually negligible.

Symmetry is a salient visual pattern in images. Symmetrical structure attracts human eye more than other regions. Therefore, detecting symmetry in an image is one of the crucial tasks in pattern recognition and computer vision research. Sparse key point based symmetry detection methods have been proposed which are fast and robust to noise showing superior detection performance. However, such local appearance-based methods have difficulties in capturing structure based patterns mostly supported by edges and contours. In this paper, we propose a scale invariant structure feature which describes points on extremum curvature along edges. We propose to use a histogram of curvature responses at respective scale space for description. Experimental evaluation on public shape dataset and real world images show that our structure feature works better in detecting visually salient structure based symmetry patterns.