GenAI4ELab/papercli-papers huggingface dataset:
MHED-SLAM: Multi-Scale Hybrid Encoding-Based Decoupled SLAM
Authors: Dengfang Feng; Wenyang Qin; Zhongchen Shi; Wei Chen; Yanhui Duan; Liang Xie; Erwei Yin
Venue: AAAI 2026 (main)

Abstract: Neural Radiance Fields (NeRF)-based Visual Simultaneous Localization and Mapping (SLAM) achieve superior scene geometric modeling and robust camera tracking by leveraging neural representations. 
Existing methods typically relied on multi-resolution hash encoding with truncated signed distance fields (TSDF) to achieve high frame rates. However, unavoidable hash collisions can lead to artifacts, and multi-view color inconsistencies in indoor scenes can result in shape-radiance ambiguity,  adversely affecting geometric quality and tracking accuracy.
To address these issues, we propose a novel Multi-scale Hybrid Encoding-based Decoupled SLAM (MHED-SLAM). 
First, to mitigate the adverse effects of hash collisions and reduce the number of learnable parameters, we innovatively fuse a coarse-scale hash tri-plane with a fine-scale hash grid within a single latent volume. 
Second, to enable precise geometric reconstruction and camera tracking, we decouple the reconstruction and rendering processes, independently learning a TSDF field for reconstruction and a density field for rendering.
Third, we devise a Symmetric Kullback-Leibler (SKL) strategy based on ray termination distributions to align the probability distributions derived from the TSDF and density fields for their synchronous convergence. 
Extensive experimental evaluations demonstrate that our approach surpasses the state-of-the-art (SOTA) methods by utilizing a faster frame rate of 20 Hz and fewer parameters, while achieving higher tracking and reconstruction accuracy.
VPN: Visual Prompt Navigation
Authors: Shuo Feng; Zihan Wang; Yuchen Li; Rui Kong; Hengyi Cai; Shuaiqiang Wang; Gim Hee Lee; Piji Li; Shuqiang Jiang
Venue: AAAI 2026 (main)

Abstract: While natural language is commonly used to guide embodied agents, the inherent ambiguity and verbosity of language often hinder the effectiveness of language-guided navigation in complex environments. To this end, we propose Visual Prompt Navigation (VPN), a novel paradigm that guides agents to navigate using only user-provided visual prompts within 2D top-view maps. This visual prompt primarily focuses on marking the visual navigation trajectory on a top-down view of a scene, offering intuitive and spatially grounded guidance without relying on language instructions. It is more friendly for non-expert users and reduces interpretive ambiguity. We build VPN tasks in both discrete and continuous navigation settings, constructing two new datasets, R2R-VP and R2R-CE-VP, by extending existing R2R and R2R-CE episodes with corresponding visual prompts. Furthermore, we introduce VPNet, a dedicated baseline network to handle the VPN tasks, with two data augmentation strategies: view-level augmentation (altering initial headings and prompt orientations) and trajectory-level augmentation (incorporating diverse trajectories from large-scale 3D scenes), to enhance navigation performance. Extensive experiments evaluate how visual prompt forms, top-view map formats, and data augmentation strategies affect the performance of visual prompt navigation.
Learning Diffusion Policy from Primitive Skills for Robot Manipulation
Authors: Zhihao Gu; Ming Yang; Difan Zou; Dong Xu
Venue: AAAI 2026 (main)

Abstract: Diffusion policies have recently shown great promise for generating actions in robotic manipulation. However, existing approaches often rely on global instructions to produce short-term control signals, which can result in misalignment in action generation. We conjecture that the primitive skills, referred to as fine-grained, short-horizon manipulations, such as "move up" and "open the gripper", provide a more intuitive and effective interface for robot learning. To bridge this gap, we propose SDP, a skill-conditioned diffusion policy that integrates interpretable skill learning with conditional action planning. SDP abstracts eight reusable primitive skills across tasks and employs a vision-language model to extract discrete representations from visual observations and language instructions. Based on the representations, a lightweight router network is designed to assign a desired primitive skill for each state, which helps construct a single-skill policy to generate skill-aligned actions. By decomposing complex tasks into a sequence of primitive skills and selecting a single-skill policy, the proposed SDP ensures skill-consistent behavior across diverse tasks.
Extensive experiments on two challenging simulation benchmarks and real-world robot deployments demonstrate that SDP consistently outperforms state-of-the-art methods, providing a new paradigm for skill-based robot learning with diffusion policies.
Just Few States Are Enough: Randomized Sparse Feedback for Stability of Dynamical Systems
Authors: Zaid Hadach; Hajar El Hammouti; El Houcine Bergou; Adnane Saoud
Venue: AAAI 2026 (main)

Abstract: While classical control theory assumes that the controller has access to measurements of the entire state (or output) at every time instant, this paper investigates a setting where the feedback controller can only access a randomly selected subset of the state vector at each time step. Due to the random sparsification that selects only a subset of the state components at each step, we analyze the stability of the closed-loop system in terms of Asymptotic Mean-Square Stability (AMSS), which ensures that the system state converges to zero in the mean-square sense.  We consider the problem of designing both a feedback gain matrix and a measurement sparsification strategy that minimizes the number of state components required for feedback, while ensuring AMSS of the closed-loop system. Interestingly,  (1)  we provide conditions on the dynamics of the system under which it is possible to find a sparsification strategy, and (2) we propose a Linear Matrix Inequality (LMI) based algorithm that jointly computes a stabilizing gain matrix, and a randomized sparsification strategy that minimizes the expected number of measured state coordinates while preserving the AMSS. Our approach is then extended to the case where the sparsification probabilities vary across the state components. Based on these theoretical findings, we propose an algorithmic procedure to compute the vector of sparsification parameters, along with the corresponding feedback gain matrix. To the best of our knowledge, this is the first study to investigate the stability properties of control systems that rely solely on randomly selected state measurements. Numerical simulations demonstrate that, in some settings, the system achieves comparable performance to full-state feedback while requiring measurements from only 0.3 percent of the state coordinates.
SeqWalker: Sequential-Horizon Vision-and-Language Navigation with Hierarchical Planning
Authors: Zebin Han; Xudong Wang; Baichen Liu; Qi Lyu; Zhenduo Shang; Jiahua Dong; Lianqing Liu; Zhi Han
Venue: AAAI 2026 (main)

Abstract: Sequential-Horizon Vision-and-Language Navigation (SH-VLN) presents a challenging scenario where agents should sequentially execute multi-task trajectory navigation guided by complex, long-horizon natural language instructions. Current vision-and-language navigation models exhibit significant performance degradation with such instructions, as information overload impairs the agent's ability to attend to observationally relevant details. To address this problem, we propose SeqWalker, a novel navigation model built on a hierarchical planning framework. Our SeqWalker features: (1) A High-Level Planner that dynamically selects global instructions into contextually relevant sub-instructions based on the agent's current visual observations, thus reducing cognitive load; (2) A Low-Level Planner incorporating an Exploration-Verification strategy that leverages the inherent logical structure of instructions for trajectory error correction. To evaluate SH-VLN performance, we also extend the IVLN dataset and establish a new benchmark. Extensive experiments are performed to demonstrate the effectiveness and superiority of SeqWalker.
Learning Object-Centric Motion Priors from Human for Robotic Dexterous Manipulation
Authors: Zhengdong Hong; Guofeng Zhang
Venue: AAAI 2026 (main)

Abstract: Manipulating diverse objects with multi-fingered dexterous hands is challenging due to the high dimensionality and complex dynamics. Human-Object Interaction (HOI) datasets provide rich knowledge about task information and embodied interactions. Instead of solely imitating the human demonstrations, our method learns to holistically predict future hand-object states by leveraging these datasets. The predicted future states of the object can serve as a general-purpose reward term for reinforcement learning, reducing reliance on task-specific reward engineering and enhancing generalization across tasks. We conduct extensive experiments on three manipulation tasks in simulation and the real world. Our approach outperforms existing SOTA methods in both success rate and generalizability on novel objects. Furthermore, we validate the cross-embodiment compatibility of our methods by successfully deploying the skills on different robot hands.
LOG-Nav: Efficient Layout-Aware Object-Goal Navigation with Hierarchical Planning
Authors: Jiawei Hou; Yuting Xiao; Xiangyang Xue; Taiping Zeng
Venue: AAAI 2026 (main)

Abstract: We introduce LOG-Nav, an efficient layout-aware object-goal navigation approach designed for complex multi-room indoor environments. 
By planning hierarchically leveraging a global topologigal map with layout information and local imperative approach with detailed scene representation memory, LOG-Nav achieves both efficient and effective navigation.
The process is managed by an LLM-powered agent, ensuring seamless effective planning and navigation, without the need for human interaction, complex rewards, or costly training.
Our experimental results on the MP3D benchmark achieves 85% object navigation success rate (SR) and 79% success rate weighted by path length (SPL) (over 40% point improvement in SR and 60% improvement in SPL compared to exsisting methods). Furthermore, we validate the robustness of our approach through virtual agent and real-world robotic deployment, showcasing its capability in practical scenarios.
Real Garment Benchmark (RGBench): A Comprehensive Benchmark for Robotic Garment Manipulation Featuring a High-Fidelity Scalable Simulator
Authors: Wenkang Hu; Xincheng Tang; Yanzhi E; Yitong Li; Zhengjie Shu; Wei Li; Huamin Wang; Ruigang Yang
Venue: AAAI 2026 (main)

Abstract: While there has been significant progress to use simulated data to learn robotic manipulation of rigid objects, applying its success to deformable objects has been hindered by the lack of both deformable object models and realistic non-rigid body simulators. In this paper, we present Real Garment Benchmark (RGBench), a comprehensive benchmark for robotic manipulation of garments. It features a diverse set of over 6000 garment mesh models, a new high-performance simulator, and a comprehensive protocol to evaluate garment simulation quality with carefully measured real garment dynamics. Our experiments demonstrate that our simulator outperforms currently available cloth simulators by a large margin, reducing simulation error by 20% while maintaining a speed of 3 times faster. We will publicly release RGBench to accelerate future research in robotic garment manipulation.
UNeMo: Collaborative Visual-Language Reasoning and Navigation via a Multimodal World Model
Authors: Changxin Huang; Lv Tang; Zhaohuan Zhan; Lisha Yu; Runhao Zeng; Zun Liu; Zhengjie Wang; Jianqiang Li
Venue: AAAI 2026 (main)

Abstract: Vision-and-Language Navigation (VLN) requires agents to autonomously navigate complex environments via visual images and natural language instructions—remains highly challenging. Recent research on enhancing language-guided navigation reasoning using pre-trained large language models (LLMs) has shown promising prospects. However, the reasoning of such methods is limited to the linguistic modality, lacking visual reasoning capabilities. Moreover, existing reasoning modules are optimized separately from navigation policies, leading to incompatibility and potential conflicts in optimization objectives.  To tackle these challenges, we introduce UNeMo, a novel framework designed for the collaborative optimization of visual state reasoning and navigational decision-making. It introduces a Multimodal World Model (MWM) that takes visual features, language instructions, and navigational actions as inputs to jointly predict subsequent visual states, enabling cross-modal reasoning. Via a Hierarchical Prediction-Feedback (HPN) mechanism, MWM collaborates with navigation policies: the first layer generates actions using current vision-and-language features; MWM then infers post-action visual states to guide the second layer’s fine-grained decisions. This forms a dynamic bidirectional promotion mechanism where MWM reasoning optimizes navigation policies, while policy decisions feedback to improve MWM’s reasoning accuracy. Experiments on R2R and REVERIE datasets show UNeMo outperforms state-of-the-art methods by 2.1% and 0.7% in navigation accuracy for unseen scenes, validating its effectiveness.
GraphCoT-VLA: A 3D Spatial-Aware Reasoning Vision-Language-Action Model for Robotic Manipulation with Ambiguous Instructions
Authors: Helong Huang; Min Cen; Kai Tan; Xingyue Quan; Guowei Huang; Hong Zhang
Venue: AAAI 2026 (main)

Abstract: Vision-language-action models have emerged as a crucial paradigm in robotic manipulation. However, existing VLA models exhibit notable limitations in handling ambiguous language instructions and unknown environmental states. Furthermore, their perception is largely constrained to static two-dimensional observations, lacking the capability to model three-dimensional interactions between the robot and its environment. To address these challenges, this paper proposes GraphCoT-VLA, an efficient end-to-end model. To enhance the model's ability to interpret ambiguous instructions and improve task planning, we design a structured Chain-of-Thought reasoning module that integrates high-level task understanding and planning, failed task feedback, and low-level imaginative reasoning about future object positions and robot actions. Additionally, we construct a real-time updatable 3D Pose-Object graph, which captures the spatial configuration of robot joints and the topological relationships between objects in 3D space, enabling the model to better understand and manipulate their interactions. We further integrates a dropout hybrid reasoning strategy to achieve efficient control outputs. Experimental results across multiple real-world robotic tasks demonstrate that GraphCoT-VLA significantly outperforms existing methods in terms of task success rate and response speed, exhibiting strong generalization and robustness in open environments and under uncertain instructions.
RENEW: Risk- and Energy-Aware Navigation in Dynamic Waterways
Authors: Mingi Jeong; Alberto Quattrini Li
Venue: AAAI 2026 (main)

Abstract: We present RENEW, a novel global path planning framework for Autonomous Surface Vehicle (ASV) operating in dynamic environments with external disturbances (e.g., water currents). These disturbances significantly affect both the risk and energy cost of navigation, particularly in constrained coastal waterways, by dynamically reshaping the navigable area. RENEW addresses this challenging scenario through a unified, risk- and energy-aware planning strategy that guarantees safety by explicitly identifying states at risk of entering non-navigable regions and enforcing adaptive safety constraints. Our planner incorporates a best-effort strategy under worst-case scenarios, inspired by contingency planning concepts from maritime domains, to ensure feasible control actions even under adverse conditions. RENEW employs a hierarchical architecture: a high-level planner explores topologically distinct paths via constrained triangulation, while a low-level planner selects an energy-efficient and kinematically feasible trajectory within a safe corridor. We validate our approach through extensive simulations using both custom realistic scenarios and real-world ocean current data. To our knowledge, this is the first global planning framework to jointly address the adaptive identification of non-navigable areas and topological diversity within a risk-aware paradigm, enabling robust navigation in maritime environments.
Towards Autonomous UAV Visual Object Search in City Space: Benchmark and Agentic Methodology
Authors: Yatai Ji; Zhengqiu Zhu; Yong Zhao; Beidan Liu; Chen Gao; Yihao Zhao; Sihang Qiu; Yue Hu; Quanjun Yin
Venue: AAAI 2026 (main)

Abstract: Aerial Visual Object Search (AVOS) tasks in urban environments require Unmanned Aerial Vehicles (UAVs) to autonomously search for and identify target objects based on visual inputs without external guidance. Existing approaches struggle in complex urban environments due to redundant semantic processing, similar object ambiguity, and the exploration-exploitation dilemma. To advance research and support the AVOS task, we introduce CityAVOS, the first benchmark dataset for autonomous search of static urban objects. It features 2,420 tasks of varying difficulty across six object categories, designed to rigorously evaluate UAV search strategies. To solve the AVOS task, we also propose PRPSearcher (Perception-Reasoning-Planning Searcher), a novel agentic method powered by multi-modal large language models (MLLMs) that enables a UAV agent to think and reason like humans on visual cues when searching for objects. Specifically, PRPSearcher constructs three specialized maps: an object-centric dynamic semantic map enhancing spatial perception, a 3D cognitive map based on semantic "attraction" values for target reasoning, and a 3D uncertainty map for balanced exploration-exploitation search. Moreover, we propose a denoising mechanism to mitigate interference from similar objects and design an Inspiration Promote Thought prompting mechanism for adaptive action planning. Experimental results on CityAVOS demonstrate that PRPSearcher surpasses existing baselines in both success rate and search efficiency (on average: +37.69% SR, +28.96% SPL, -30.69% MSS, and -46.40% NE). Our work paves the way for future advances in embodied visual target search.
PanoNav: Mapless Zero-Shot Object Navigation with Panoramic Scene Parsing and Dynamic Memory
Authors: Qunchao Jin; Yilin Wu; Changhao Chen
Venue: AAAI 2026 (main)

Abstract: Zero-shot object navigation (ZSON) in unseen environments remains a challenging problem for household robots, requiring strong perceptual understanding and decision-making capabilities. While recent methods leverage metric maps and Large Language Models (LLMs), they often depend on depth sensors or prebuilt maps, limiting the spatial reasoning ability of Multimodal Large Language Models (MLLMs). Mapless ZSON approaches have emerged to address this, but they typically make short-sighted decisions, leading to local deadlocks due to a lack of historical context. We propose PanoNav, a fully RGB-only, mapless ZSON framework that integrates a Panoramic Scene Parsing module to unlock the spatial parsing potential of MLLMs from panoramic RGB inputs, and a Memory-guided Decision-Making mechanism enhanced by a Dynamic Bounded Memory Queue to incorporate exploration history and avoid local deadlocks. Experiments on the public navigation benchmark show that PanoNav significantly outperforms representative baselines in both SR and SPL metrics.
PhyPlan: Learning to Plan Tasks with Generalizable and Rapid Physical Reasoning for Embodied Manipulation
Authors: Ankit Kanwar; Hartej Soin; Abhinav Barnawal; Mudit Chopra; Harshil Vagadia; Tamajit Banerjee; Shreshth Tuli; Rohan Paul; Souvik Chakraborty
Venue: AAAI 2026 (main)

Abstract: Given the task of landing a ball in a goal region beyond direct reach, humans can often throw, slide, or rebound objects against the wall to attain the goal. Enabling robots to replicate such reasoning is non-trivial as it requires multi-step planning and involves a mixture of discrete and continuous action spaces, a sparse and sensitive reward structure, computationally expensive simulations, and an incomplete understanding of the environment's physics. We present PhyPlan, a physics-informed and adaptable planning framework for efficient multi-step physical reasoning. At its core, PhyPlan comprises of Generative Flow Networks (GFlowNets) and Monte Carlo Tree Search (MCTS) to explore and evaluate sequences of object interactions. GFlowNets sample discrete action sequences in proportion to their associated reward, enabling broad and reward-driven exploration of the discrete planning space.  MCTS complements this by adaptively balancing the use of a fast but approximate pre-trained physics-informed dynamics predictor and costly but accurate environment rollouts, ensuring both speed and precision in planning. The known and actual physics discrepancy is captured using Gaussian Process Regression. Experiments on benchmark simulated tasks requiring composition of collisions, slides, and rebounds demonstrate that PhyPlan achieves a 45\% higher success rate and up to 3× efficiency gains over state-of-the-art model-based reinforcement learning approaches.
Lightweight Adaptive Topological Layout and Semantic Mapping in Vision-and-Language Navigation on Websites
Authors: Pingrui Lai; Zihao Xie; Hua Yang
Venue: AAAI 2026 (main)

Abstract: Vision-and-Language navigation on websites requires agents to navigate target webpages and answer questions based on human instructions. Current web agents primarily leverage Large Language Models (LLMs) for semantic understanding and reasoning, but still suffer from limited navigation performance and slow inference speed. Constructing a global map across webpages can effectively enhance both navigation accuracy and efficiency, however, this is challenged by the open structure of web navigation graphs and the dynamic nature of web layouts. In this paper, we propose ATLAS: Adaptive Topological Layout And Semantic mapping, a framework that adaptively constructs a time-varying, unbounded topological map across webpages and unifies heterogeneous elements through semantic representation. This enables both global path planning and local element selection for web-based navigation and question answering. As a lightweight approach, ATLAS significantly outperforms existing state-of-the-art methods on the WebVLN benchmark with a 10% improvement in success rate, and achieves the highest average task success rate on both the Mind2Web and WebArena benchmarks.
DiTEA: Mixture-of-Experts for Vision-Language-Action Model in Robotic Manipulation
Authors: Chengxuan Li; Xingwan Wang
Venue: AAAI 2026 (main)

Abstract: The current diffusion-based Vision-Language-Action (VLA) models have faster inference speed and the ability to solve the action muti-modality problem in robot manipulation tasks compared to traditional autoregressive models after large-scale pre-training and post-training. However, the diffusion-based VLA models were found to have poor instruction-following ability, and after fine-tuning training on multiple tasks, them often suffer from "skill forgetting" due to conflicting model weights on each task. To address this problem, we propose DiTEA, a Diffusion Transformer-based Mixture-of-Experts (MoE) VLA model. Specifically, it fuses the MoE module into the action head of VLA to form Action MoE, and in addition, we design the Task-Instruction Gate, which uses language instructions to select specific experts for tasks they specialize in, in order to improve the VLA's instruction-following ability. We conducted comprehensive experiments and ablation study to evaluate the efficacy of our model under different designs. Experimental results from simulation and real-world show that our DiTEA has excellent improvement in multi-task compared to baseline and other VLAs.