Apple's research team published the paper "Perceptive Humanoid Motion from Simulation" on arXiv, unveiling HALO, an autonomous control system for humanoid robots, and simultaneously open-sourcing the training and deployment code.
Professor Lan Xuguang's team at Xi'an Jiaotong University has proposed AffordSim, which connects open-vocabulary 3D affordance prediction to simulation trajectory generation. Starting from a natural language task description, it locates an object's functional regions, then combines cuRobo motion planning to generate executable manipulation demonstrations.
Gamma Intelligence, TeleAI and Unitree have jointly launched LocoManiLab, a unified evaluation framework for humanoid robot mobile manipulation. It covers five types of home spaces, 13 scenario instances and 234 tasks, with the Unitree G1 and Dex3 dexterous hands as the unified test platform.
A team at the University of California San Diego proposed EgoThumb in an arXiv preprint, a wearable robotic thumb driven by a palm-side wide-angle camera and an imitation learning model that can autonomously grasp and stabilize objects, allowing users to complete tasks such as unscrewing bottle caps and stirring tea with one hand.
A paper proposes that how tactile data is represented may matter more than resolution. The authors pretrained a tactile encoder, LingBot-Touch, on various sensors ranging from 3x3 glove arrays to 240x240 optical fingertips, and tested it with 4 strategies on 26 real tasks.
Ken Goldberg's team at UC Berkeley and Spencer Huang, NVIDIA's head of robotics engineering, proposed an "agentic robotics" method that lets AI agents generate, test, and correct robot control programs in simulation without additional data collection.
Why it matters: The report describes a method for using multi-agent systems to generate, test, and correct robot programs in simulation, and presents real-world data from warehouse sorting.
The authors release the FHNW Muttenz dataset, covering a contiguous 10 km street network and two mobile mapping campaigns about 1.5 years apart, with ground-truth poses accurate to 6-9 mm in position and orientation error below 0.1°. Using this dataset, they systematically evaluate sparse-feature visual localization; with daytime imagery and pre-calibrated cameras, median translation error is 1-5 cm and rotation error is 0.03-0.1°. The authors note that, provided the scene representation is precisely georeferenced, visual localization can approach survey-grade GNSS positioning accuracy and additionally provides full orientation information.
VOMMI is a portable demonstration collection and learning framework that connects portable RGB demonstrations to VLA post-training through offline trajectory reconstruction and online visual-motion conditioning. R2-VO refines trajectories using sparse geometric anchors, reducing the average absolute trajectory error of body and hand streams by 24.6%; a policy post-trained only on portable demonstrations has an 18.2% lower base velocity error than a policy trained on robot-collected demonstrations, with comparable end-effector translation accuracy.
The study constructs a geometric visual pretraining dataset containing only a plane, an object, and a cube hand, with trajectories generated automatically and scenes without textures or backgrounds, so that the policy is first exposed to the geometric relationship between the hand and the object. The authors evaluate ACT on three simulated robots with five manipulation tasks each and on three real-world robot tasks; in most combinations, fine-tuning from the geometric prior yields higher success rates in early training than training from scratch, and requires only a small number of task demonstrations. The results indicate that highly simplified geometric scenes can also provide an initialization that transfers across robots and to real-world tasks.
The authors propose LAG-Fusion, a latency-aware guidance fusion framework for asynchronous multimodal diffusion policy composition that allows modality-specific policies to run at their own inference rates and provide denoising guidance whenever output is available. To keep asynchronous composition consistent, the authors derive a reference-frame rebasing rule for diffusion variables under relative action representations, so that delayed guidance is aligned before fusion. In contact-rich manipulation, the authors compose a low-frequency vision policy with a high-frequency force policy; experiments with heterogeneous modality latency show better responsiveness and task performance than synchronous fusion and a purpose-designed force-aware baseline.
The authors propose DexTeleop-0, a bimanual dexterous teleoperation framework that uses fingertip tactile feedback to overlay robot-side contact corrections on human tracking commands, mitigating kinematic mapping errors and the inability of vision alone to regulate contact force. The method computes localized corrections in real time via contact-stiffness approximation and the kinematic Jacobian, and adds per-contact force tracking, soft regularization of predicted contact moments, and bounded hand-joint residuals.
The authors propose SGM-SLAM, a distributed SLAM framework for multi-robot collaboration in GPS-denied, communication-constrained environments, where robots can each initialize their coordinate frames independently and share only limited spatial overlap. The method uses fused RGB-LiDAR point clouds to build a scene graph containing a semantically segmented point cloud layer and a discrete bounded object layer, and performs collaborative matching by exchanging and matching object data with neighboring robots, using multi-step data exchange and optimization to improve communication efficiency. The authors validated effectiveness and communication efficiency on real-world datasets containing limited trajectory overlap, viewpoint variation, repeated structures, and degraded visual conditions.
CLAW is a fully end-to-end self-supervised framework that jointly learns a world model and continuous latent action representations directly from videos without action annotations. It uses adversarial latent regularization to prevent the latent variables from encoding future observations, so that they capture action structure, and a diffusion model models environment dynamics as a distribution over future observations rather than a single deterministic rollout. The authors say this latent action world model supports imitation learning from observation and goal-directed planning, and outperforms existing methods in multi-task, multi-embodiment experiments.
The authors propose XIT, a sampling-based planner that formulates active gas distribution mapping (GDM) as a next-best-trajectory informative path planning problem, growing a single shared tree toward multiple frontier goals while taking informative gas samples along the way.
The authors propose the Visual Learned Distance (VLD) framework, which decouples perception learning from policy learning for image-goal navigation: first, a distance-to-goal predictor is trained self-supervised on about 3,000 hours of internet and simulated video; then, in simulation, a reinforcement learning policy is trained using privileged geometric distances perturbed by GeoNoise; at deployment, VLD predictions are used zero-shot.
Augmentron is a geometry-aware data augmentation pipeline that generates reusable scene representations and renders them according to recorded camera poses, producing environment and workspace variations that are consistent across time and multiple views while preserving the robot, task objects, actions, and annotations.
The authors propose the Seeing What Matters benchmark to evaluate task-conditioned actionable spatial belief in simulated tabletop manipulation, covering 860 frozen evaluation cases across three scenarios—coffee workstation, drawer/cabinet, and microwave—which expand to more than 115,000 evaluation episodes.
The research team developed a wearable haptic input/output device that integrates a 36-channel electrotactile array with capacitive touch sensing, enabling stimulation locations and user-reported perceived locations to be aligned within the same body-referenced coordinate frame. Experiments with 24 participants showed notable individual differences in electrotactile and thermal percept maps, but both modalities retained electrode-pair-specific spatial structure across participants.
SURE is a data-efficient vision-language-action (VLA) training framework that models spatial uncertainty with a lightweight transposed-convolutional probabilistic action decoder and uses Soft-DTW alignment supervision to accommodate temporal variation across demonstrations. On LIBERO, RoboTwin 2.0, and real manipulation tasks, SURE matches or exceeds baseline VLA manipulation success rates using only 10% of demonstration data or 20% of training steps.
MDF++ is a visuo-tactile world action model that uses a diffusion backbone to learn latent representations of multisensory observations end-to-end, addressing the problem that existing world action models rely on frozen pretrained encoders whose representations are not well suited for control. The authors propose a multi-task learning strategy that leverages cross-modal correlations to learn task-relevant representations, avoiding the feature collapse caused by naive joint training. On contact-rich manipulation tasks including real-world bolt screwing and nut mating, it outperforms world action models built on frozen pretrained representations by up to 50%.
SUGARv2 proposes a unified framework for learning diverse visual humanoid loco-manipulation skills through interaction reference synthesis and a shared training pipeline. The method adapts reusable task templates to different spatial configurations to synthesize coordinated robot and object motion references at low cost, and uses reference-based imitation as a common learning objective across tasks, first training privileged teachers and then training visual policies through teacher distillation and reinforcement learning.
The study proposes a planner-verified counterfactual (CF) test: holding the grasp pose fixed, a solid obstacle is inserted into the approach corridor and its points are added to the model input, after which the planner re-derives the label, flipping it from feasible to infeasible. Across eight predictors spanning point clouds, voxels, depth, and object tokens, CF-pair AUROC falls from 0.920-0.996 in natural scenes to near chance (0.470-0.504), and this flip holds across four planners, two robotic arms, and a real-object obstacle.
The authors propose A2-MEAS, an embodied multi-agent framework that uses code as a scaling interface, combining corrective language, spatial evidence, execution history, and action proposals into correction codes; during training, MetaBrain provides supervision and runtime guidance.
Why it matters: The framework uses code as a scaling interface, injects corrective language and execution history into the policy, and provides quantitative comparisons across benchmarks and real-robot tasks.
The paper formalizes the attribution problem of inconsistencies between scans and models in LiDAR-inertial odometry and BIM fusion as fault identification over three fault classes, and derives a geometric criterion for determining whether attribution is feasible. On cases that are detectable but not attributable in a two-dimensional study, the method based on this criterion had a conditional error rate of 0.83%, compared with 31.67% for a baseline using uncertainty-based abstention; across 5,600 attributable cases, it produced 5,574 unique attributions with no erroneous attributions.
The authors present RevoSim, a multimodal tactile simulator for the BrainCo Revo3 dexterous hand that jointly models 285 piezoresistive taxels and 5 visuotactile fingertips. The simulator can synchronously generate pressure, depth, RGB, and marker-displacement observations; on a single RTX 5090, its depth-conditioned marker-displacement configuration supports up to 4096 parallel environments. The authors trained in-hand manipulation and grasping policies entirely in simulation, laying a foundation for future sim-to-real transfer.
HOC2R introduces a hand-object contact trajectory extractor that combines frozen DINOv3 and SAM 2.1 features with learned heads to perform hand-object association and contact localization, extracting robot-usable motion cues from human videos.
The authors propose HomeLoop, a simulation framework and benchmark built on Isaac Sim that integrates robot bodies, networked devices and wearables, simulated residents, faulty sensors, and hazards that evolve over time within a single environment. The benchmark contains 20 tasks across 5 categories, where the same alert can have different causes. A runtime monitor uses temporal logic to score both the final outcome and process actions, and each action has a preset cost.
Lodos is a simulation framework designed from scratch, using new broadphase and narrowphase collision detection and contact dynamics computation to specifically handle mesh-mesh collisions between detailed meshes. The authors state that its physical fidelity is comparable to existing mature simulators, and that in mesh-mesh collision scenarios its throughput is about an order of magnitude higher; the source code has been released. The work targets contact-rich scenarios in large-scale parallel rigid-body simulation that are limited by approximating collision geometry with primitives such as spheres, capsules, cylinders, and boxes.
The authors propose an object-centric reinforcement learning framework that treats each part as an agent and learns a shared policy for continuous assembly control. A graph neural network represents the parts, their target poses, and their spatial relationships, and predicts translational and angular velocities for each component; per-agent PPO with a centralized graph critic learns motion and temporal coordination within a single feedback policy. The study validates the approach on planar Tangram assembly, using procedurally generated target configurations and randomized initial layouts to support training and evaluation, and demonstrates parallel assembly in simulation and transfer to sequential execution on a real robot.
The Meituan Robotics team has released Meituan-Robotics-0 (MR0), a vision-language-action foundation model and embodied-agent system for fixed-base desktop manipulation. Its training data includes 147 million vision-language samples and 15,060 hours of robot trajectories covering 12 embodiments.
Apple's research team published the paper "Perceptive Humanoid Motion from Simulation" on arXiv, unveiling HALO, an autonomous humanoid robot control system, and validating it on the Unitree G1.
Why it matters: Apple's HALO splits task orchestration, motion generation, and tracking into three layers, and reports comparative results of 91% in unfamiliar environments and 70% on composite tasks, offering a look at its layered design and data synthesis pipeline.
Tsinghua University and other institutions have proposed the ART (Agentic Robot with Tool-use) framework, which can fine-tune any end-to-end VLA model into an agent capable of plug-and-play use of external tools. The method uses 30,000 tool-call trajectories and two-stage LoRA fine-tuning, achieving average success rates of about 75% in LIBERO simulation and about 62% on a real Astribot S1 robot, an improvement of about 20% over baselines such as OpenVLA and π0.
WorldFoundry proposes open-source infrastructure for world intelligence, intended for reproducible inference and evaluation across video, 3D and 4D, embodied action, and interactive world systems. The work defines a generative world model as a predictive dynamical system that maintains a persistent, history-conditioned state representation, responds to actions or interventions, and generates temporally coherent trajectories. Its catalog includes 270 model entries, 74 benchmarks, and 62 metric entries, and it records execution readiness and evaluation conclusions separately through manifests, artifact contracts, and evidence-aware scorecards.
Apple released the HALO humanoid whole-body motion control system, which procedurally generates 1,000 virtual residential scenes and synthesizes nearly 300 hours of paired data, enabling the Unitree G1 to master skills such as sitting on chairs, turning sideways to pass through a half-open door, crouching under a table, and pushing boxes.
Why it matters: Apple's HALO uses simulation-synthesized data to train humanoid whole-body skills, transfers them zero-shot to real scenes, and ablation experiments show the contribution magnitude of each module.
A team from Northwestern, CMU, UNC Chapel Hill and Amazon, in the paper "WOVEN: Weaving Visual World Modeling into Multimodal LLMs," defines world modeling as estimating the state transition p(s′|s,a) and builds WOVEN, a training and evaluation base of 36K questions.
Why it matters: The paper breaks world modeling into independently intervenable state-transition tasks and provides reusable rules for selecting training data, offering reference value for multimodal model training.
The Hong Kong University of Science and Technology, Beijing Academy of Artificial Intelligence, Peking University and other institutions jointly proposed DexRoam, which uses first-person human whole-body demonstrations to train mobile dual-arm dexterous manipulation policies. The work has been accepted to CoRL 2026. The system narrows the human-robot motion distribution gap through three stages: embodiment alignment, action semantic alignment, and temporal alignment, reducing MMD and SWD metrics by an average of 41.7% and 31.1%, respectively.
Avnet, together with the National-Local Joint Humanoid Robot Innovation Center, produced a 76-page white paper (August 2026) that uses five functional domains and six core modules as its framework to break down 10 categories of industrial deployment challenges, including heterogeneous computing workloads and platform adaptation, multi-axis real-time control, and dexterous hand integration.
The study proposes SLA, a spatial-language-action model initialized from a pretrained pi05 policy, which jointly optimizes action prediction and the alignment of intermediate visual embeddings with the spatial representations of a frozen 3D perception model during continual pre-training. The team unified heterogeneous robot datasets spanning embodiments, tasks, and scenes, and introduced feature-dependent gates that adaptively aggregate visual embeddings from multiple layers; the teacher model can use partially available depth and camera calibration information. All auxiliary components are used only during training, and the inference architecture remains unchanged.
The research team proposes A2-MEAS, an embodied multi-agent framework that uses code as a scaling interface. It combines correction codes with corrective language, spatial evidence, execution history, and action proposals, while MetaBrain provides training supervision and runtime guidance.
Why it matters: The framework uses code as a scaling interface, injects corrective language and execution history into the policy, and presents comparison data across multiple benchmarks and real-robot experiments.
Sun Yat-sen University, Peng Cheng Laboratory, and other institutions propose RePO-VLA, which segments recovery trajectories, trains a progress-aware semantic value function, and performs value-conditioned fine-tuning so that dual-arm VLA policies can continue advancing tasks after disturbances. The study also builds FRBench, a failure-recovery benchmark; the RoboTwin simulation contains 46 dual-arm tasks and 23,453 trajectories. On real robots in water-pouring and towel-folding tasks, as recovery demonstrations per task increase from 50 to 200, the average disturbed success rates are 30%, 65%, and 75%, respectively.