Yogesh Phalak

Yogesh Phalak, Wen Sin Lor, Apoorva Khairnar et al.

Physical Reservoir Computing (PRC) leverages the intrinsic nonlinear dynamics of physical substrates, mechanical, optical, spintronic, and beyond, as fixed computational reservoirs, offering a compelling paradigm for energy-efficient and embodied machine learning. However, the practical workflow for developing and evaluating PRC systems remains fragmented: existing tools typically address only isolated parts of the pipeline, such as substrate-specific simulation, digital reservoir benchmarking, or readout training. What is missing is a unified framework that can represent both high-fidelity simulated trajectories and real experimental measurements through the same data interface, enabling reproducible evaluation, analysis, and physics-aware optimization across substrates and data sources. We present OpenPRC, an open-source Python framework that fills this gap through a schema-driven physics-to-task pipeline built around five modules: a GPU-accelerated hybrid RK4-PBD physics engine (demlat), a video-based experimental ingestion layer (openprc.vision), a modular learning layer (reservoir), information-theoretic analysis and benchmarking tools (analysis), and physics-aware optimization (optimize). A universal HDF5 schema enforces reproducibility and interoperability, allowing GPU-simulated and experimentally acquired trajectories to enter the same downstream workflow without modification. Demonstrated capabilities include simulations of Origami tessellations, video-based trajectory extraction from a physical reservoir, and a common interface for standardized PRC benchmarking, correlation diagnostics, and capacity analysis. The longer-term vision is to serve as a standardizing layer for the PRC community, compatible with external physics engines including PyBullet, PyElastica, and MERLIN.

Yogesh Phalak

Since the past few decades, several designs and control methods have been developed for the Spherical Robots (SRs) with thoroughly analyzed mechanics on generalized 3D terrains. But the vertical motion and the steep inclination maneuver has been an unsolved problem with the existing SR's driving mechanisms. Also, the possibilities of wind-powered or air-propelled SRs have not been fully explored. This paper introduces the new Omnidirectional Spherical Robot mechanism named SPIROS: The Six Propeller and Intermeshed Rotor based Omnidirectional Spherical Robot. The SPIROS is driven by a novel octahedral arrangement of six intermeshed rotary air thrusters placed in the Goldberg Polyhedral shaped spherical gridshell. The advantage of the proposed design lies in its air-powered propulsion, which improves on the obstacle avoidance and the slope climbing abilities. The robot's dynamic models are derived using the existing kinematical models of the Continuous Rolling Spherical Robots (CR-SR), with subcategories, triple axes rolling (3R-SR), dual axes rolling (2R-SR) and rolling and turning (RT-SR) spherical robots. The path tracking control scheme based on the pure pursuit algorithm is presented. Simulations are carried out in MATLAB and Simulink to validate the developed models and the effectiveness of the proposed control schemes.

Aniket Gujarathi, Akshay Kulkarni, Unmesh Patil et al.

The field of autonomous robotics is growing at a rapid rate. The trend to use increasingly more sensors in vehicles is driven both by legislation and consumer demands for higher safety and reliable service. Nowadays, robots are found everywhere, ranging from homes, hospitals to industries, and military operations. Autonomous robots are developed to be robust enough to work beside humans and to carry out jobs efficiently. Humans have a natural sense of understanding of the physical forces acting around them like gravity, sense of motion, etc. which are not taught explicitly but are developed naturally. However, this is not the case with robots. To make the robot fully autonomous and competent to work with humans, the robot must be able to perceive the situation and devise a plan for smooth operation, considering all the adversities that may occur while carrying out the tasks. In this thesis, we present an autonomous mobile robot platform that delivers the package within the VNIT campus without any human intercommunication. From an initial user-supplied geographic target location, the system plans an optimized path and autonomously navigates through it. The entire pipeline of an autonomous robot working in outdoor environments is explained in detail in this thesis.