News & Research

arXiv:2605.23241v1 Announce Type: new Abstract: Relational databases (RDBs) remain the cornerstone of modern data systems and support diverse predictive tasks. Recent relational deep learning (RDL) methods enable end-to-end prediction by converting RDBs into graphs, where rows are represented as nodes and inter-table interactions are represented as edges, and then applying graph-based models for representation learning. Despite the strong capability of RDL, effective self-supervised pre-training for RDBs remains non-trivial. RDB tasks often require multi-faceted information across different perspectives and granularities. For example, user churn classification may rely more on interaction patterns, whereas consumption value prediction requires both user-item behaviors and intrinsic user attributes for fine-grained regression. Such heterogeneous needs challenge RDB representation learning, as pre-training objectives should cover comprehensive information for downstream adaptation. Howev

arXiv:2605.23235v1 Announce Type: new Abstract: Globalization and multiculturalism continue to produce increasingly diverse speech varieties. Yet current spoken dialogue systems frequently fail on under-represented dialects and accents, often misidentifying the input language and causing cascading failures in downstream dialogue tasks. Addressing this dialectal variance under low-resource constraints remains an open challenge, as standard fine-tuning is computationally expensive and prone to overfitting on high-dimensional speech data. We propose Convex Language Detection (CLD), a novel framework that integrates theoretically grounded convex optimization techniques into the spoken dialogue systems pipeline. Our method is efficiently implemented via multi-GPU Alternating Direction Method of Multipliers (ADMM) in JAX, thus providing global optimality guarantees and fast training in polynomial time. Theoretically, we prove that our convex objective induces certified margin stability and p

arXiv:2605.23523v1 Announce Type: new Abstract: Reconstructing the motion of objects from videos is a key component for embodied AI and robot manipulation. While diverse approaches to object pose tracking have been studied, they rely heavily on strong external priors, such as depth data or 3D templates, and remain highly vulnerable to severe occlusions by hand grasps despite the use of explicit masks. In this work, we present ComPose, a 6DoF object tracking framework designed for hand-aware object pose estimation from RGB video. Rather than treating the hand purely as an occluder, our method harmonizes hand motions as a \textit{complementary cue} for object tracking. In detail, we recover a variety of object motions over time by combining object and hand cues from foundation models within a unified tracking pipeline. Here, ComPose adaptively selects informative hand joints, combines object- and hand-derived cues for motion estimation, and refines the resulting object motion using visib

arXiv:2604.10077v2 Announce Type: replace Abstract: In Document Understanding, the challenge of reconstructing damaged, occluded, or incomplete text remains a critical yet unexplored problem. Subsequent document understanding tasks can benefit from a document reconstruction process. In response, this paper presents a novel unified pipeline combining state-of-the-art Optical Character Recognition (OCR), advanced image analysis, masked language modeling, and diffusion-based models to restore and reconstruct text while preserving visual integrity. We create a synthetic dataset of 30{,}078 degraded document images that simulates diverse document degradation scenarios, setting a benchmark for restoration tasks. Our pipeline detects and recognizes text, identifies degradation with an occlusion detector, and uses an inpainting model for semantically coherent reconstruction. A diffusion-based module seamlessly reintegrates text, matching font, size, and alignment. To evaluate restoration quali

arXiv:2602.11146v2 Announce Type: replace Abstract: Preference optimization for diffusion and flow-matching models relies on reward functions that are both discriminatively robust and computationally efficient. Vision-Language Models (VLMs) have emerged as the primary reward provider, leveraging their rich multimodal priors to guide alignment. However, their computation and memory cost can be substantial, and optimizing a latent diffusion generator through a pixel-space reward introduces a domain mismatch that complicates alignment. In this paper, we propose DiNa-LRM, a diffusion-native latent reward model that formulates preference learning directly on noisy diffusion states. Our method introduces a noise-calibrated Thurstone likelihood with diffusion-noise-dependent uncertainty. DiNa-LRM leverages a pretrained latent diffusion backbone with a timestep-conditioned reward head, and supports inference-time noise ensembling, providing a diffusion-native mechanism for test-time scaling an

Autonomous robot navigation requires reliable path planning under uncertainty. Classical algorithms such as $\mathrm{A}^{*}$ and Dijkstra's, and sampling methods like PRM/RRT, work well on static, discrete maps but degrade in dynamic, high-dimensional settings. We study reinforcement learning (RL) for this problem, contrasting value-based Q-learning/DQN (effective with discretised motion primitives) with policy-gradient, actor-critic methods that natively support continuous control. We present an actor-critic approach for continuous navigation that uses offpolicy training with experience replay and entropy regularisation (SAC-style) to stabilise learning while producing smooth, kinodynamically feasible commands (linear and angular velocities). Evaluations in a first-person navigation task and an unmanned-driving simulator use success rate, SPL/path-length ratio, collisions and learning curves. Compared with a DQN baseline and a vanilla actor-critic, our method converges faster and more stably, achieving rising episode returns (from −5 to +7 by 10 episodes) and the lowest average training time ($\sim 1300 \mathrm{s}$ versus $\sim 1300-1700 \mathrm{s}$ for DQN and $\sim 2300-2750 \mathrm{s}$ for standard actor-critic). Qualitative rollouts show fewer collisions and smoother trajectories. These results indicate that actor-critic RL is a strong choice for continuous-control navigation, outperforming discrete value-based methods in dynamic environments.

This paper compares the performance of a traditional approach for autonomous robot navigation and a new approach for the same problem, all within the framework of reinforcement learning. A new approach for the problem has been introduced and compared against the traditional approach, referred to as the value iteration approach for holonomic robots and the new approach for the same problem in relation to the traditional approach. These approaches include sampling-based policy estimation and interpolation and optimization-based filtering for obstacle avoidance. Actor critic methods for non-holonomic scenarios include Deep Deterministic Policy Gradient (DDPG) and Soft Actor-Critic (SAC) approaches. The simulation of various 2D and 3D scenarios, ranging from simple to complex, shows that value iteration method convergence occurs rapidly, while at the same time a success rate of over 95% can be attained within 31 minutes of virtual time. DRL techniques, however, succeed in attaining an average of 88.5% ± 4.2% in 300 scenarios, where in each case, 35% faster convergence of SAC compared to DDPG could be attained. The efficacy of reinforcement learning approaches to find practical solutions to robot navigation in various scenarios, while meeting constraints, has been proven.

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.