robot simulation implementation

Existing robotic simulation modeling relies heavily on hand-crafted kinematic/dynamic models and large-scale real-world data, limiting autonomy and generalizability. Method: We propose an end-to-end framework for autonomous construction of differentiable simulators, enabling robots to jointly identify their morphology, kinematics, and dynamics solely through active interaction and multimodal perception (vision and proprioception). Our approach integrates deep reinforcement learning, neural radiance fields (NeRF) for geometric representation, differentiable physics engines, and online system identification—without requiring prior dynamical knowledge or manual modeling. Contribution/Results: To our knowledge, this is the first method enabling closed-loop self-identification and policy pre-execution in simulation. Evaluated on a physical robotic arm, it achieves significantly higher dynamic fidelity: 47% reduction in action prediction error and a 3.2× improvement in task planning success rate—breaking the paradigm of manual modeling and extensive real-data dependency.

Bridging the domain gap between real-world RGB-D images and robot simulation environments remains challenging for digital twin task generation. Method: This paper proposes a simulation-task alignment framework leveraging vision-language models (VLMs) and an iterative routing mechanism to generate executable simulation tasks end-to-end from single-frame RGB-D input. The method integrates SAM2 for precise object segmentation, VLM-driven semantic understanding, dynamic matching against a simulation asset library, and automated generation of self-validating test suites—forming a closed-loop “perceive–match–generate–verify” optimization pipeline. Contribution/Results: It achieves the first high-fidelity geometric-semantic alignment between real-scene objects and simulation assets while ensuring physical feasibility and executability within physics engines. Evaluated on multiple real-world benchmarks, the approach significantly improves object correspondence accuracy (+23.6%), task success rate (+31.4%), and cross-scene generalization.

To address the lack of flexible, composable simulation platforms and standardized evaluation protocols in robot learning, this paper introduces robosuite v1.0—the first modular robot learning simulation framework built on MuJoCo. Its core innovation is a novel modular task construction paradigm that decouples task specification, physics simulation, and reinforcement learning training via standardized APIs, reproducible benchmark environments, and ROS-compatible design. The framework provides over 12 benchmark tasks spanning bimanual manipulation, dexterous hand control, and multi-object interaction, supporting native Python invocation and cross-platform deployment. Since its release, robosuite v1.0 has been adopted in over 100 research studies, significantly advancing experimental reproducibility, comparability, and standardization in robot learning.

A significant gap exists between academic research and industrial practice in robot runtime software reconfiguration: while the literature predominantly focuses on structural reconfiguration (e.g., component loading/unloading), industry widely adopts lightweight, low-intrusion parameter-based reconfiguration. Method: We conduct a mixed-methods study comprising a systematic literature review of 78 papers, source-code and documentation analysis of four mainstream robotic frameworks, and empirical evaluation across 48 subsystems plus real-world case studies. Contribution/Results: This work is the first to systematically expose this practice-research divide and establishes the first comprehensive design space for robot reconfiguration. It confirms parameter-level reconfiguration as the sole widely adopted paradigm in practice; identifies critical research gaps and emerging technical trends; and proposes actionable, engineering-oriented improvement pathways. Our findings provide empirically grounded guidance for practitioners in tool selection, framework design, and industry–academia collaboration.

Embodied AI and robotics research lacks high-fidelity, scalable testbeds for evaluating humanoid robots. Method: This work introduces PR2—a physically accurate, photorealistic, full-scale humanoid robot simulation platform—integrating a high-precision rigid-body dynamics engine with a real-time rendering framework to support multi-sensor simulation, large language model (LLM) interfaces, closed-loop motion planning, and control. It proposes the first comprehensive benchmark suite covering gait generation, dexterous locomotion-manipulation (loco-manipulation), and language-guided navigation. Contribution/Results: The benchmark was deployed in a national undergraduate humanoid robotics competition, enabling 137 university teams to evaluate performance on walking, loco-manipulation, and language-driven search tasks. Empirical results validate the platform’s efficacy across diverse embodied intelligence capabilities. Following open-sourcing, PR2 has significantly accelerated pedagogical adoption and algorithmic development in embodied AI and robotics.

This work addresses the challenge that traditional model-based testing is ill-suited for distributed robotic systems due to their high nondeterminism, dynamic reconfiguration, and inherent complexity. To overcome this limitation, the paper proposes the Scenario Specification Language (SCSL), which enables the construction of system-level tests by composing basic scenarios. The approach integrates runtime online test generation and execution with mechanisms for dynamic component joining/leaving and interface reconnection, thereby supporting automated testing and dynamic reconfiguration. The syntax and semantics of SCSL are validated through a robotic salvage mission case study, where automatically generated tests effectively demonstrate the feasibility and advantages of the proposed method.

This study addresses the absence of a unified and widely accepted formalism for specifying robotic tasks, which hinders non-experts from defining single- or multi-robot missions in complex, dynamic environments. For the first time, it systematically compares four prominent task specification paradigms—Behavior Trees, Finite State Machines, Hierarchical Task Networks (HTN), and Business Process Model and Notation (BPMN)—from the perspective of task-level description. The evaluation focuses on expressiveness, control structures, tooling support, and integration with human workflows. Through expert validation, the work clarifies the strengths, limitations, and suitable application contexts of each approach, offering researchers and practitioners a principled basis for method selection to enhance the robustness and adaptability of robotic task systems.

This study addresses the limitations of current robotic system validation, which relies heavily on manual selection of test scenarios, thereby hindering scalability and compromising reproducibility and reliability of conclusions. To overcome these challenges, this work proposes a compositional, scenario-based modeling approach that integrates declarative test specifications, plugin-driven scenario generation, containerized parallel simulation, and unified result analysis to establish the first modular and scalable automated verification framework. The framework enables systematic parameter variation across multiple dimensions and facilitates robust identification of systemic faults versus stochastic anomalies. Evaluated across 5,480 distinct scenario configurations with over 100,000 simulation runs, the approach accumulated 1,800 hours of simulated operation and 1,873 virtual kilometers, demonstrating its efficacy in discerning consistent system deficiencies from random irregularities.

This work addresses the reliability challenges inherent in deploying autonomous mobile robots from simulation to real-world environments by proposing and implementing an end-to-end development and validation framework. Building upon an existing mechatronic platform, the system integrates onboard sensing and computing units to achieve self-localization and autonomous navigation. The complete architecture is first developed and rigorously validated in a high-fidelity simulation environment before being seamlessly transferred to a physical robot. Experimental results demonstrate that the real-world system successfully replicates the performance observed in simulation, thereby confirming the effectiveness and robustness of the proposed approach. These findings substantiate the use of high-fidelity simulation as a trustworthy foundation for robotic development, significantly enhancing deployment efficiency and system credibility.

This study addresses the limited interoperability of existing traffic agent models across autonomous driving simulation platforms due to the absence of a unified integration standard, which compromises the consistency and reliability of evaluation results. To overcome this, the authors propose a modular simulation integration architecture based on open standards, uniquely combining the Open Simulation Interface (OSI) and the Functional Mock-up Interface (FMI). This framework establishes a generic, reusable specification for agent model encapsulation, clearly defining interfaces, data mappings, and execution semantics. The approach enables seamless deployment of the same agent model across three major platforms—OpenPASS, CARLA, and CarMaker—with consistent behavioral performance, thereby demonstrating strong cross-platform interoperability and modularity. A reference implementation has been open-sourced to advance standardization in simulation ecosystems.