新闻与研究

With the advancement of humanoid robotics, human–robot collaboration has emerged as a prominent research focus. Ensuring the safety of both humanoid robots and humans remains a critical challenge. In this paper, we address conflict resolutions at the planning level and propose a safety-oriented motion planning (SOMP) algorithm for a wheeled humanoid robot operating in environments with unknown human motions. In the proposed SOMP algorithm, we employ Monte Carlo simulations to predict trajectories of stochastically moving humans and formulate both hard and soft constraints. A dynamic-quadrant stochastic sampling policy, integrated with a rapidly exploring random tree method, is proposed to generate diverse initial paths. Building upon this, we develop a constraint-fusion mechanism that combines hard constraints for safety guarantees and soft constraints for path optimization, thereby effectively resolving potential conflicts between wheeled humanoid robots and stochastically moving humans. We evaluate the proposed algorithm under different configurations of conflict numbers, task success rates, and path rewards. The proposed method outperforms A*, RRT, and MDP in terms of conflict numbers (−77.8%, −76.6%, and −71.4%) and task success rates (+168.0%, +109.4%, and +91.4%). Our simulation results prove the efficiency and robustness of our algorithm in safe motion planning with stochastically moving humans.

Humanoid robots must navigate, decide, and schedule efficiently to boost automated supply chain efficiency. Traditional rule-based techniques fail in dynamic situations, especially with task dependencies. A unique Puma Optimizer-mutated Twin-Stage Adaptive Twin-Delayed Deep Deterministic Policy Gradient (PO-TSATD3) method is used in this deep reinforcement learning system. Training datasets imitate real-world logistics situations with dynamic impediments, many robots, and varying workloads. Data preparation cleans and normalizes for quality. The Puma Optimizer optimises convergence and operating efficiency, while the PO-TSATD3 framework improves navigation and scheduling adaptive learning. Python simulations show considerable gains in navigation accuracy, collision reduction, and schedule optimization over conventional methods. The model's outstanding performance metrics proved its scalability and durability in complicated situations. This research validates the application of deep reinforcement learning, augmented by PO-TSATD3, as a powerful solution for intelligent humanoid robot operations in future supply chain systems.

Autonomous navigation in cluttered, unknown environments requires agents to balance local obstacle avoidance with global path optimality. This paper presents a novel hybrid approach for path planning in such environments by integrating Spiking Neural Networks, Artificial Potential Fields, and RRT* for episodic path optimization based on spatial memory. The proposed approach can be viewed as a lifelong learning paradigm where the intelligent agent creates a cognitive map based on the Spiking Neural Networks and continuously updates its spatial memory during episodes of navigation based on the Artificial Potential Fields approach. The spatial memory obtained from the Artificial Potential Fields approach can then be integrated into the RRT* path planning approach for path optimization during subsequent episodes of navigation, avoiding certain areas where obstacles are more probable based on the spatial memory. The performance of the proposed approach was evaluated on a grid environment with various obstacles, and the results show rapid convergence within six episodes with a success rate of 100% and a reduction in path loss of 89.8% for the normalized path length. The performance of the proposed approach was compared with the Artificial Potential Fields and RRT* path planning approaches, and the results show the proposed approach can significantly reduce the planning time and node expansion for path planning. The proposed approach can therefore be viewed as a novel approach for path planning based on spatial memory, which can be used for efficient path planning in unknown and dynamic environments.

Recent advances in learned control, large-scale simulation, and generative models have accelerated progress toward general-purpose robotic controllers, yet the field still lacks platforms suitable for safe, expressive, long-term deployment in human environments. Most existing humanoids are either closed industrial systems or academic prototypes that are difficult to deploy and operate around people, limiting progress in robotics. We introduce Sprout, a developer platform designed to address these limitations through an emphasis on safety, expressivity, and developer accessibility. Sprout adopts a lightweight form factor with compliant control, limited joint torques, and soft exteriors to support safe operation in shared human spaces. The platform integrates whole-body control, manipulation with integrated grippers, and virtual-reality-based teleoperation within a unified hardware-software stack. An expressive head further enables social interaction -- a domain that remains underexplored on most utilitarian humanoids. By lowering physical and technical barriers to deployment, Sprout expands access to capable humanoid platforms and provides a practical basis for developing embodied intelligence in real human environments.

Real-time collision avoidance is a critical challenge in visual servoing control for safe robot manipulation. This article presents a model-free framework that integrates reinforcement learning (RL) with real-time collision detection (RCD), enabling secure and smooth visual servoing in dynamic environments. The reward function in the RL framework is carefully designed to simultaneously minimize tracking errors, promote motion smoothness, and enforce collision avoidance. Beyond policy optimization through RL, a two-step real-time trajectory blending (RTB) scheme, consisting of a Butterworth filter (BF) and polynomial interpolation, is developed to further refine the generated trajectories and ensure smooth joint velocity profiles. Furthermore, the soft actor-critic (SAC) algorithm is employed for policy learning, and the performance is compared with two widely used methods, namely proximal policy optimization (PPO) and twin delayed deep deterministic policy gradient (TD3). Experimental results on a 6-degree-of-freedom (6-DoF) robot manipulator demonstrate the effectiveness of the proposed approach in achieving safe and smooth visual servoing in real time.

We showed recently that a novel prosthesis control, based on natural arm movement reconstructed from an Artificial Neural Network (ANN) that receives movement goal and stump motion as inputs, enables close-to-natural reaching control in Virtual Reality (VR). Here, we applied and tested this control in the physical world using first-person teleoperation of Reachy2, an open-source humanoid robot made by Pollen Robotics that ranked 2nd at ANA Avatar XPRIZE. Direct teleoperation reproducing all arm joints of non-disabled participants (n=14) elicited a 100% success rate for grasping objects at various locations, with best possible usability scores and small workload scores. High success rates (>92%) were also obtained when all distal joints from the elbow onward were operated with the novel prosthesis control, using a movement goal identified through gaze-guided computer vision. While usability and workload scores were slightly degraded when non-disabled participants used the prosthesis control as compared to direct teleoperation, both were scored similarly well by a sample from our target population (n=8 participants with transhumeral limb loss). This platform and control schemes offer broad perspectives for prosthesis control, but also for teleoperation robotics and human-robot interactions.

Quadruped robots are used for primary searches during the early stages of indoor fires. A typical primary search involves quickly and thoroughly looking for victims under hazardous conditions and monitoring flammable materials. However, situational awareness in complex indoor environments and rapid stair climbing across different staircases remain the main challenges for robot-assisted primary searches. In this project, we designed a two-stage end-to-end deep reinforcement learning (RL) approach to optimize both navigation and locomotion. In the first stage, the quadrupeds, Unitree Go2, were trained to climb stairs in Isaac Lab's pyramid-stair terrain. In the second stage, the quadrupeds were trained to climb various realistic indoor staircases in the Isaac Lab engine, with the learned policy transferred from the previous stage. These indoor staircases are straight, L-shaped, and spiral, to support climbing tasks in complex environments. This project explores how to balance navigation and locomotion and how end-to-end RL methods can enable quadrupeds to adapt to different stair shapes. Our main contributions are: (1) A two-stage end-to-end RL framework that transfers stair-climbing skills from abstract pyramid terrain to realistic indoor stair topologies. (2) A centerline-based navigation formulation that enables unified learning of navigation and locomotion without hierarchical planning. (3) Demonstration of policy generalization across diverse staircases using only local height-map perception. (4) An empirical analysis of success, efficiency, and failure modes under increasing stair difficulty.

High levels of electric vehicle (EV) penetration and load uncertainty pose significant challenges to traditional voltage regulation in distribution systems (DS), particularly concerning voltage drops. This study investigates the potential of a dual-service mode (V2V/V2G) for mobile charging using multiple charging robots, cooperating with conventional DS voltage regulation methods to enhance performance. Specifically, we propose a multi-robot adaptive charging network (MRACN) embedded in a three-level voltage regulation framework to address voltage violations. At the low level, we introduce a voltage security rule-based dispatch strategy for MRACN to mitigate EV-induced voltage drops. At the mid-level, we optimize day-ahead scheduling of on-load tap changers and capacitor banks via optimal power flow for long-term voltage regulation. At the high level, we employ an unscented Kalman filter-based deep reinforcement learning (UKF-DRL) approach to integrate MRACN into DSs for real-time voltage control. Experimental results demonstrate that MRACN effectively reduces voltage violations and enhances the flexibility of conventional voltage control systems. Moreover, the UKF-DRL method accelerates DRL training by nearly an order of magnitude compared to full AC power-flow solutions, with the overwhelming majority of state estimates deviating by no more than 2%.

This paper presents an Agentic AI-based framework for automated safety analysis of humanoid and service robots using their CAD designs. The framework integrates multiple specialized AI agents and tools that collaboratively import and analyze CAD models to detect and segment hazardous features such as sharp edges, gaps, holes, pinch points, and exposed moving parts (e.g., limbs, wheels, gears, and motors). By combining vision-language models (VLMs), geometric analysis, and semantic segmentation, the system evaluates compliance with international safety standards. Identified semantic features such as gaps, holes, accessible areas, sharp edges, slots, and pinch points are correlated with ISO defined safety thresholds, enabling the AI agents to assess compliance, estimate the likelihood of injury or entrapment, and determine whether the robot's CAD-based form factors meet safety requirements. This work defines key Agentic AI use cases, outlines the design and workflow of collaborating agents and tools, and proposes an integrated design-support system for early-stage robot development, enabling proactive hazard analysis & risk assessment throughout product design life cycle.

This paper presents NAVBOT25, a labelled dataset aimed at strengthening the overall security of autonomous robots against network-based cyber threats within the Robot Operating System (ROS) platform. NAVBOT25 offers a comprehensive, labelled dataset that captures both normal operational behaviour and a variety of attack scenarios relevant to ROS-based systems. This dataset was generated by deploying a TurtleBot3 running ROS Noetic in controlled laboratory setting, where real-world attack vectors were executed —including SSH brute-force attempts, reverse shells, port scans, and ROS-specific attacks such as unauthorized publishing actions and topic flooding. Network traffic was captured using tcpdump, and 83 flow-level features were extracted using CICFlowMeter, resulting in a series of CSV files. Designed to support the development of AI-assisted intrusion detection systems, NAVBOT25 addresses existing gaps in robotic cybersecurity research by providing a richer and more diverse dataset for evaluating threat detection in networked robotic systems.

Diffusion-based reinforcement learning dem effectiveness of the proposed algorithm is validated through simulations on the MuJoCo platform and experiments on a physical robotic platform. The relevant code for this study is publicly available on GitHub: https://github.com/zhn-ya/diffusion-RL.

The rapid growth of robotics and embedded system technologies has increased the need for intelligent and userfriendly robotic systems. Conventional robot control methods such as remote controls, keyboards, and programmed instructions often make human-robot interaction difficult and less efficient. To overcome these limitations, this project proposes the design and development of a Voice Controlled Humanoid Robot that can understand human voice commands and perform corresponding actions.The proposed system uses speech recognition technology to convert voice commands into digital signals. These commands are transmitted through Bluetooth communication to a microcontroller such as Arduino or ESP32, which processes the commands and controls servo motors through a motor driver circuit. Based on the received instructions, the humanoid robot performs movements such as walking, turning, hand movements, and other predefined actions.The main objective of this project is to create a simple, low-cost, and efficient robotic system that improves natural communication between humans and robots. The system provides hands-free robot control, making it suitable for educational purposes, assistive technologies, automation systems, and future smart robotic applications. The project demonstrates the practical implementation of embedded systems, wireless communication, and voice recognition technologies in humanoid robotics.