News & Research

We present a coverage framework that integrates Hilbert space-filling priors into decentralized multi-robot learning and execution. We augment DQN and PPO with Hilbert-based spatial indices to structure exploration and reduce redundancy in sparse-reward environments, and we evaluate scalability in multi-robot grid coverage. We further describe a waypoint interface that converts Hilbert orderings into curvature-bounded, time-parameterized SE(2) trajectories (planar (x, y, {\theta})), enabling onboard feasibility on resource-constrained robots. Experiments show improvements in coverage efficiency, redundancy, and convergence speed over DQN/PPO baselines. In addition, we validate the approach on a Boston Dynamics Spot legged robot, executing the generated trajectories in indoor environments and observing reliable coverage with low redundancy. These results indicate that geometric priors improve autonomy and scalability for swarm and legged robotics.

Aiming at dynamic jumping control problem of humanoid bipedal robots, parametric mechanism design, kinematic/dynamic model, numerical solution algorithm of dynamic parameters, motion simulation and prototype experiment of the underactuated takeoff process of humanoid bipedal robots are proposed. The underactuated takeoff process of the robot is divided into the stance phase before takeoff (dominated by the driving joint) and the underactuated takeoff phase (passively rotating around tiptoe). The kinematic model of underactuated takeoff process is established by using the D 'Alembert principle, and the kinematic criterion for the robot entering underactuated phase is derived. The motion critical conditions during takeoff process are analyzed, and trajectory planning method of the total centroid based on variable quartic polynomial interpolation is proposed. The dynamic constraints of toe rotation in underactuated phase are constructed. The dynamic analytical model of underactuated takeoff is established by using the Lagrange equation, and the closed-loop solution of joint spatial trajectories are achieved through inverse kinematic. Finally, a series of motion simulation and prototype experiment are carried out to verify effectiveness of the proposed mechanism design, kinematic/dynamic modeling, trajectory planning algorithm and motion control strategy.

Assistive robotics can be designed to support the physically disabled persons in numerous ways. They are trying to reinstate human capabilities which has been condensed or lost by accident, at old age or disease. But physically disabled persons facing some troubles in their moments with the existing assisted devices. To give the support for Old age and disabled persons by developing an intelligent Robotic wheel chair, to improve the complex navigation techniques in mobile robotics replacing the existing assisted devices like wheelchair with the automated one. A new algorithm is developed depend onbetter reality which replaces the existing system and improves effective localization with high accuracy.This technique is mainly developed by blob analysis, pattern and color matching. We designed the total prototype on national instruments Robotic starterkit2.0. It is important to design a robot in such a way which avoids obstacles automatically when collision takes place. These types of robotic wheel chairs are very efficient and enable the user to move around easily and also gives the independence to one who have motor disability can easily move without others help.

Autonomous aerial navigation in absolute darkness is crucial for post-disaster search and rescue operations, which often occur from disaster-zone power outages. Yet, due to resource constraints, tiny aerial robots, perfectly suited for these operations, are unable to navigate in the darkness to find survivors safely. In this letter, we present an autonomous aerial robot for navigation in the dark by combining an Infra-Red (IR) monocular camera with a large-aperture coded lens and structured light without external infrastructure like GPS or motion-capture. Our approach obtains depth-dependent defocus cues (each structured light point appears as a pattern that is depth dependent), which acts as a strong prior for our <italic>AsterNet</italic> deep depth estimation model. The model is trained in simulation by generating data using a simple optical model and transfers directly to the real world without any fine-tuning or retraining. <italic>AsterNet</italic> runs onboard the robot at 20 Hz on an NVIDIA Jetson Orin<inline-formula><tex-math notation="LaTeX">$^{\text{TM}}$</tex-math></inline-formula> Nano. Furthermore, our network is robust to changes in the structured light pattern and relative placement of the pattern emitter and IR camera, leading to simplified and cost-effective construction. We successfully evaluate and demonstrate our proposed depth navigation approach <italic>AsterNav</italic> using depth from <italic>AsterNet</italic> in many real-world experiments using only onboard sensing and computation, including dark matte obstacles and thin ropes (<inline-formula><tex-math notation="LaTeX">$\varnothing$</tex-math></inline-formula> 6.25 mm), achieving an overall success rate of <italic>95.5%</italic> with unknown object shapes, locations and materials. To the best of our knowledge, this is the first work on monocular, structured-light-based quadrotor navigation in absolute darkness.

Background: Coordinating differential-drive mobile robots for landmark coverage is challenging due to non-holonomic dynamics, clutter, and sparse rewards. Standard multi-agent RL pipelines often show unstable learning and inconsistent completion in this setting. Methodology: We adopt a centralized-training, decentralized-execution actor-critic without inter-agent commu nication. Our replay-centric design combines a tagged buffer that up-samples goal-reaching transitions and an offline replay initialization that seeds early learning with curated trajectories. Dynamic task assignment uses the Hungarian algorithm during training and evaluation, and we benchmark against uniform replay and established variants. Results: In a cluttered six-robot arena, the approach improves training stability relative to uniform replay. Convergence is faster and requires fewer updates to reach consistent success. Coverage efficiency increases as landmarks are reached earlier across runs. Collisions per episode decrease without adding communication or architectural changes. Multi seed evaluations show gains that persist with narrow confidence intervals. Train-evaluation gaps shrink on unseen maps, indicating improved generalization. Ablations attribute complementary ben efits to the tagged and offline components. Performance remains competitive with prioritized and hindsight replay baselines under matched budgets. Computational overhead is small because sam pling logic changes while network sizes do not. Conclusions: Focus ing on replay design substantially stabilizes multi-agent learning for differential-drive coordination. The pipeline integrates cleanly with standard CTDE implementations and supports practical deployment in coverage tasks.

Autism Spectrum Disorder (ASD) is defined by ongoing difficulties in social communication, flexibility in behavior, and adaptive learning skills. Interventions that utilize robots have demonstrated potential in providing organized training for children with ASD; however, there is a lack of controlled studies that specifically examine the effects of reinforcement strategies. This research introduces a systematic interaction policy based on reinforcement, founded on the principles of Applied Behavior Analysis (ABA), and assesses its effectiveness through a randomized controlled experimental design with observation. The humanoid robot NAO was used in two different interaction scenarios, one involving a reinforcement condition (RC) and the other a non-reinforcement condition (RC), ensuring that the instructional material and environment were maintained, while only the availability of contingent positive feedback was altered. A total of 50 participants diagnosed with ASD Level 2 engaged in structured word-learning sessions. Learning outcomes were assessed using institutional performance criteria, average response time, and emotion analysis derived from a CNN-based facial expression model. Independent samples t-tests revealed statistically significant improvements in both performance scores (t(48) = 3.779, p < 0.05) and response times (t(48) = 3.758, p < 0.05) in the reinforcement condition compared to the non-reinforcement condition. The findings demonstrate that structured ABA-based reinforcement within robotic interaction significantly enhances learning efficiency and task engagement, contributing methodologically rigorous evidence to robot-assisted ASD intervention research.

Behavior Cloning (BC) has emerged as a highly effective paradigm for robot learning. However, BC lacks a self-guided mechanism for online improvement after demonstrations have been collected. Existing offline-to-online learning methods often cause policies to replace previously learned good actions due to a distribution mismatch between offline data and online learning. In this work, we propose Q2RL, Q-Estimation and Q-Gating from BC for Reinforcement Learning, an algorithm for efficient offline-to-online learning. Our method consists of two parts: (1) Q-Estimation extracts a Q-function from a BC policy using a few interaction steps with the environment, followed by online RL with (2) Q-Gating, which switches between BC and RL policy actions based on their respective Q-values to collect samples for RL policy training. Across manipulation tasks from D4RL and robomimic benchmarks, Q2RL outperforms SOTA offline-to-online learning baselines on success rate and time to convergence. Q2RL is efficient enough to be applied in an on-robot RL setting, learning robust policies for contact-rich and high precision manipulation tasks such as pipe assembly and kitting, in 1-2 hours of online interaction, achieving success rates of up to 100% and up to 3.75x improvement against the original BC policy. Code and video are available at https://pages.rai-inst.com/q2rl_website/

This paper presents the mechanical design of a human-ridable giant humanoid robot that enables intuitive pilot-in-the-loop whole-body control. Despite the steady development of humanoid robots, humanoids continue to be constrained by human-scale strength and size, with poor scalability of transmissions and structures at large sizes, and teleoperation limits in latency and situational awareness. To overcome such limitations, we introduce the mechanical design of a human-ridable giant humanoid with a cockpit-style human-machine-interface (HMI): an Internal Robot (IR) with gravity-compensated zero-torque joint stream angles to the External Robot (ER) arms, while the ER lower body provides bipedal stabilization and locomotion. This approach preserves situational awareness, mitigates teleoperation latency and viewpoint issues, and couples human decision making with robotic strength, reach, and endurance. We define the objectives for rideability, biped operation in human environments, and protective, high-tech packaging realized via seated posture, nonwearable manipulator HMI, height-first co-design, rapid low-cost fabrication, and vertical integration. In addition, we detail the subsystem design and demonstrated stable walking and whole-body tasks.

Labor shortages and aging populations are straining hospital systems globally, prompting interest in humanoid robots as a viable technology solution for healthcare support. This paper presents a proof-of-concept deployment of a Unitree G1 humanoid robot using advanced SLAM with multi-modal sensors (LiDAR, depth cameras) for reliable navigation in dynamic hospital-like environments. Unlike conventional approaches that model humans solely as obstacles to avoid, our system implements human-aware navigation. Respecting proxemics, social context, and malleable spatial boundaries to treat people as collaborative partners, enabling safe, legible co-navigation in crowded wards and corridors. By demonstrating autonomous, purposeful movement for logistics and patient-support tasks, this research illustrates a pathway to augment healthcare delivery, worker support, and patient care outcomes through adaptable, human-centered robotics.

This paper deals with a problem of multitask agents' navigation in urban environment with obstacles using different algorithmic approaches: neural network models with a system of rewards and penalties, neural networks oriented on distance minimization, as well as models that use direct movement vectors and rectangular route structures. The novelty of the presented method is the creation of a hybrid algorithm that incorporates strict rules for movement in an initially unknown environment and, when obstacles appear, switches to a neural network for obstacle avoidance. This approach combines deterministic movement, which can be adapted to artificial intelligence for safe and reliable navigation in uncharted territory. Experiments showed that thirty neural argents trained purely using rewards and penalties in movement exhibits slow learning and cyclical movement as clusters, which is due to the fact that they optimize the current state without global planning. Models trained solely on distance optimization perform slightly better, but still create clusters of movement toward the goal. Meanwhile, the use of strict linearity rules and rectangular routes allows for effective obstacle avoidance and consistent target achievement with less time under conditions of limited graph information. The results show that rule-based learning yields better results in agent movement than fully autonomous agents. Consequently, the combined path generation method yields the best results in agent navigation along a known path.

With the rapid development of Internet technology and multimedia applications, and the continuous expansion of interactive scenes of humanoid robot education, the number of digital resources of ethnic music has reached an unprecedented scale and continues to grow. This study mainly focuses on the construction of a national music retrieval system that integrates multimodal attention mechanisms and its application implementation in humanoid robot education interaction. Firstly, a method for automatic annotation of ethnic music adapted to multimodal data was studied to meet the human-computer interaction needs of robot education interaction. Secondly, a label conditional random field music automatic annotation method is proposed, and a multi-modal attention mechanism deep neural network model for ethnic music annotation is constructed to enhance the accurate matching of music features and retrieval requirements in human-computer interaction. Finally, integrate the constructed ethnic music retrieval system into the humanoid robot education interactive platform, conduct application analysis based on actual educational interactive scenarios, verify the effectiveness of the system in the human-computer interaction process, and complete performance evaluation. The results indicate that in the Glu module, Glu blocks perform better in ethnic music annotation tasks. The annotation results of various indicators in music hierarchy sequence modeling are superior to traditional models, ensuring the accuracy of ethnic music annotation in human-computer interaction scenarios. Compared with other algorithms, the AUC label score of this system is the highest, reaching 0.913. It can more efficiently model the mapping relationship between multimodal music features input during human-computer interaction and text retrieval requirements. It shows better performance in all evaluation indicators and can effectively support ethnic music retrieval services in humanoid robot education interaction, enhancing the immersion and fun of educational interaction.

Social robot navigation presents a significant and complex challenge within indoor environments characterized by numerous obstacles and dynamic changes. Reinforcement learning (RL) serves as a potent approach for addressing this issue. However, the primary attributes of such environments—their diversity and complexity—pose substantial challenges to the generalization capabilities of RL-based social robot navigation systems. In this article, we propose a meta-reinforcement learning framework for social robot navigation that empowers robots to adapt to a variety of environments. Furthermore, we introduce the concept of utilizing social norms to guide social navigation for the first time. We employ field modeling to represent interactions among the environment, pedestrians, and robots. Historical pedestrian motion data are utilized as training input for physical information neural networks, which generate pedestrian movement vector fields within the environment. We then incorporate the similarity between the robot’s predicted motion state and its actual motion state into the reward function, guiding the robot’s learning process in alignment with social norms. Simulation results demonstrate that our proposed method enhances the robot’s social navigation capabilities.

Efficient representation and querying of railway networks are crucial for autonomous railway systems and digital infrastructure management. This paper introduces VEctorial Railway NEtwork (VERNE), an interpretable data structure and algorithm that integrates vector-based spatial partitioning with a railway-specific topological framework to enhance network representation and navigation. VERNE is designed to optimize query efficiency, reduce memory footprint, and ensure scalability for real-time applications. Its internal mechanism results from a comparative performance analysis between a $k$-d tree, an STRtree and two custom algorithms, highlighting trade-offs in computational efficiency and memory overhead. The proposed approach is validated using datasets from both the French and Swedish railway networks, demonstrating its effectiveness in real-world scenarios. The results indicate that VERNE provides a robust and scalable solution for railway infrastructure modeling, offering improvements in localization speed and computational efficiency. Another advantage is that it inherently manipulates atomic elements which can contain any information relevant to directly perform navigation onboard an autonomous robot. This work contributes to the advancement of railway digitalization by providing a structured methodology for spatial data processing in autonomous railway systems.