The paper proposes the PRISM framework, which disentangles depth noise into two modalities—sensing invalidation and measurement inaccuracy—and distills features from 3D visual foundation models as spatial-semantic priors.
This framework combines flow-matching-based path planning with model predictive path-following control for mobile robot navigation. The flow matching model captures multimodal path distributions, while the predictive controller satisfies state and input constraints online and avoids obstacles, and event-triggered replanning feeds control solutions back into subsequent path generation. Simulation results show that the framework performs robustly in cluttered environments.
This paper proposes a new method for generalizing feedforward control actions with respect to payload parameters, independent of the underlying controller; given only the parameters of a new payload, it can execute the same trajectory without knowledge of the robot's dynamic model and even in the presence of elastic joints.
The paper proposes PAL (Plan first, Act Later), a hierarchical memory framework for vision-language robot manipulation. A high-level vision-language model (VLM) converts streaming visual observations into a subtask_list, and at execution time selects the currently relevant subtask from it to pass to a low-level vision-language-action (VLA) policy.
QSelect combines three quantizers—AWQ, GPTQ, and AutoRound—by assigning them to linear modules to form a single static low-bit model, scoring each substitution by the final next-token distribution.
The paper proposes Relational Geometry Forcing (RGF), which injects geometric priors from a frozen 3D foundation model into World Action Models (WAMs) by aligning pairwise feature relations instead of pointwise feature alignment, avoiding the degradation of action learning caused by dense geometric targets over-constraining compact action features.
The authors propose TIDE (Training In DEployment), a human-in-the-loop online post-training system that uses a provenance-conditioned supervised learning framework to update the policy online from human interventions and policy rollouts during deployment, without learning a critic, reward model, or action scorer.
The authors propose DepState-JEPA, a dual-stream RGB-D joint-embedding predictive architecture that connects spatial understanding with action-conditioned prediction through a shared latent state, DepState. Built on V-JEPA 2.1, the framework uses depth both as a complementary observation stream and as the grounding for metric 3D space, and constrains state learning with ray-conditioned readouts, object-trajectory supervision, and calibrated forward kinematics.
NavBind proposes a method for diagnosing whether navigation vision-language-action (VLA) models truly follow instructions: with the navigation context fixed, it checks whether changing the instruction affects the probability distribution of actions. The authors recombined route-aligned instruction segments from R2R-CE and RxR-CE to construct 3,252 route-fork contexts, each pairing two full instructions that share a description of the route already traveled but specify different branches, and evaluated them under the same agent state and navigation history.
SMR is a streaming neural motion retargeter learned from semantic-conditioned physics experts. It converts human motion into robot task-space targets, which the physics experts track in simulation, and then uses a strictly causal Transformer to learn their executed trajectories. Each new frame uses only current and past human observations to generate a robot motion reference for a downstream whole-body controller. Experiments show that the semantic-conditioned physics experts outperform direct-optimization retargeting in semantic fidelity and physical validity, and that the resulting streaming retargeter performs better in closed loop under a unified whole-body controller than kinematic and physics-refinement baselines.
This study proposes that the generalization performance of world action models (WAMs) diverges significantly depending on the representational geometry of their generative backbones, and quantifies this prior using nuisance orthogonality (the degree to which features are orthogonal to the in-distribution manifold under out-of-distribution visual variations). Experiments across various image and video backbones find that higher orthogonality before training is associated with stronger robustness to visual variations after WAM training, enabling prospective prediction of policy generalization before training. Analysis of robust WAMs also reveals suppressed feature displacement and robust action generation.
SimPT is an automated simulation framework that integrates policy rollouts, corrective data collection, policy training, and standalone evaluation for human-intervention-based vision-language-action (VLA) post-training.
An arXiv paper proposes a lightweight state-dependent objective coordination method for end-to-end balance and navigation of two-wheeled inverted-pendulum robots. The method introduces a pitch-angle-conditioned scalar coefficient β(s)∈[0,1] that dynamically adjusts the relative weight of balance stabilization and navigation objectives during training, without requiring an additional control hierarchy or attitude-stabilization controllers such as PID or LQR.
ACT-Flow is an online learning framework that combines an adaptive Action Chunking Transformer (ACT) with a conditional residual flow policy, allowing ACT to absorb successfully executed actions through behavioral cloning, with residual targets recomputed relative to the updated prior.
PHD-Hand is a linkage-driven humanoid dexterous hand with three drive degrees of actuation (DOA) per finger. It uses a unique flexure hinge and elastic drive rods made of spring steel to simplify the transmission, avoiding the need for many miniature moving parts, and can be manufactured with budget-level 3D printers, lowering the barrier to use and development of dexterous hands and bringing its size closer to that of a human hand. A prototype has been used for grasping and object manipulation evaluation, and the paper also presents the latest research results on the hand's kinematics and control. It has been accepted by IEEE-RAS Humanoids 2026.
SLoSa is a neuro-symbolic compression and fine-tuning framework that builds a safety-aware dataset using classical planners, a teacher large language model, and formal verifiers, then applies supervised fine-tuning and reinforcement learning fine-tuning guided by a verification-based reward machine to train small, low-power, safety-aware robotic task planning models.
The authors' team has released GGraph, an open-source GPU-accelerated graph library for robot deployment and large-scale learning. It supports real robots through ROS 2 interfaces and supports vectorized GPU policy training, with built-in support for tasks including 3D navigation, exploration, and loop-closure integration. In single-robot operation, graph updates take milliseconds; in large-scale training, it supports hundreds of parallel robots training graph-based exploration policies, reducing wall-clock training time by an order of magnitude.
HyperDrone is a quadrotor simulator built for high-performance batch rendering, over 20 times faster than existing state-of-the-art batch quadrotor simulators.
COWBOY is a scalable sim-to-real framework for learning contextual whole-body manipulation. On whole-body dexterous grasping tasks, it transfers zero-shot to hundreds of objects and diverse in-the-wild scenes, including cluttered tabletops, shelves, drawers, and other unstructured environments.
A study compared four preference aggregation methods—reward-level, advantage-level, loss-level, and PCGrad—across two humanoid robot control frameworks, finding that no aggregation level is optimal across all environments and metrics. The experiments used Preference Reward Responsiveness (PRR) combined with coverage and utility metrics; results show that early aggregation produces a wider range of behavioral responses but introduces more unintended objective interference, while later aggregation improves control precision at the cost of some responsiveness.
The study proposes the Context-Aware Navigation Explanation framework, which uses a large language model combined with perceptual context and planned trajectories to generate natural-language explanations for assistive robot obstacle avoidance and navigation. The system is deployed on a Unitree Go2 platform in indoor navigation scenarios to evaluate how different explanation context configurations affect user trust and explanation satisfaction. Results show a strong positive correlation between explanation satisfaction and trust, and that the configuration combining obstacle information with future trajectory produced the best explanations.
This study proposes a general learning framework for nonlinear, nonconvex dynamic games, applied to multi-agent cooperative iterative tasks with shared constraints. It uses previously executed feasible trajectories to construct a sampled joint safe set and player-specific cost-to-go functions.
The paper proposes the Flexible Feature Editing Pipeline (FLEX), which uses a language model to iteratively correct a robot's internal representation of task features based on natural language corrections through three complementary operations: Add, Generalize, and Mask.
The paper proposes the inr2action framework, which fits a neural implicit representation to each robot manipulation episode starting from a shared meta-learned initialization; the resulting weight delta serves as memory, with a size that does not vary with episode length and stays fixed from one hundred frames to one hundred thousand frames.
Real2Sem2Real (R2Sem2R) proposes a real-semantic-real pipeline that turns a single monocular RGB human demonstration video into thousands of robot demonstrations covering multiple semantic object instances within the same category. The method tracks object SE(3) poses using DINOv3-distilled Gaussian splats, outperforming existing methods and FoundationPose with estimated metric depth, and maps the human hand to a feasible robot grasp that can be transferred to digital twins.
T-WLA is a World-Language-Action architecture that uses shared visual memory to guide both action generation and tactile correction, addressing the problem in contact-rich manipulation where contact conditions change before the visual policy updates.
VeriCalib formulates target-based LiDAR-camera extrinsic calibration as a verifiable estimation problem and outputs a calibration state indicating whether to accept the result or collect more data. It uses a structured covariance to decouple rotation and translation accuracy, guiding data collection toward the weakest directions, and uses in-view and cross-view checks plus the minimum detectable deviation to define the smallest fault detectable in each direction.
The framework combines a neural-network-enhanced robust wrench observer with a hybrid pose-force controller for physical human-robot interaction (pHRI), enabling end-effector pose and force tracking without force/torque sensors. The neural network compensates for system friction and unmodeled dynamics to improve observer performance, and task-null-space decomposition separates joint velocities and reduces the effect of human disturbance on the observer. Experimental results show that the framework achieves robust and accurate wrench estimation while maintaining reliable pose and force tracking, and can be applied to various robotic arm platforms without force/torque sensors.
The framework uses a meta-learning-trained neural network to recognize two human-robot interaction modes, active guidance and passive following, without manually setting switching thresholds. The passive mode recognition rate rose from 90.1% to 99.1%, and the active mode from 79.8% to 97.1%. An adaptive meta-learning controller can compensate for human disturbances and adapt online to varying interaction forces. A multi-trajectory meta-learning algorithm jointly learns an environment basis representation and force-related parameters to handle unmodeled dynamics. Experiments verified its robust control and reliable mode recognition performance.
This study proposes a coupled visual-motor remapping method that pairs panoramic azimuth warping with its inverse upper-limb yaw transformation for first-person teleoperation of humanoid robots, to extend the operator's reach to side and rear targets. The prototype implements two mapping modes: Global Zoom (GZ) and Focus+Context (F+C).
The study proposes two RANSAC-based ground normal vector estimation methods: single-scan estimation and estimation based on a LiDAR-inertial odometry (LIO) global 3D map, for odometry in a spherical mobile mapping system (SMMS). Evaluated on diverse real-world datasets against terrestrial laser scanner ground truth, the LIO method significantly outperforms the single-scan method in accuracy and robustness. Integrating ground normal vector estimation into the motion model improves pose estimation and supports autonomous navigation in hazardous environments.
SpatialBrain is a framework that retains experience in spatial reasoning decisions on a fixed vision-language backbone. 3DFusionMemory fuses current-scene observations into queryable spatial relations, the Spatial Skill Library provides reusable tool procedures, and Spatial Cognition associates task conditions with experience about when and how to use memory and skills; completed trajectories are abstracted into process records and consolidated between task blocks.
The paper presents DONIC, a generalist bimanual dexterous tracker jointly trained across manipulation categories and human motion sources, and releases BiHOI, a multi-view dataset containing 1,575 sequences covering 229 objects, emphasizing sustained bimanual interaction and retargeting reconstructed interactions into robot references.
The paper proposes Memory-Centric Continual Learning (MCCL), arguing that memory should be judged by whether it improves an agent's subsequent actions rather than by fact retrieval. It includes a three-axis taxonomy (organizing memory by storage location, form, and management approach), a two-loop lifecycle, an evaluation protocol, and a memory scaling framework.
This work proposes AW-0, an agentic world action model framework that models code, future video, and robot actions as three coupled generative streams within a unified mixture-of-transformers architecture. A code expert generates parameterized skill routines and control logic, while video and action experts generate future visual states and low-level motor commands.
The study proposes ALPS, a pretrained visual state encoder that unifies three capabilities—physical state information, language alignment, and pixel reconstruction—within a single set of patch tokens. It pretrains a ViT-B/16 from scratch using only still images and text, and further refines it through cross-view self-distillation.
The Dual Exam benchmark evaluates multimodal agents and world models separately: the agent track compares 10 configurations under fixed environment response mechanisms, covering 1,088 items and 15 reporting categories, while the world model track contains 400 cases across 8 scenarios.
Echo proposes a framework that treats two task status signals, stop and failure, as two dimensions of each action chunk and denoises them together with robot actions, thereby distinguishing ongoing execution, successful completion, and failure. The method requires no frame-level progress annotations; it provides complementary supervision by constructing successful, failed, and recovery trajectories, and it lets status signals denoise faster than actions so that subsequent action denoising steps can build on cleaner status estimates. Real-world manipulation experiments show that Echo accurately predicts task status and consistently improves policy performance.
FocusVLA proposes a stage-aware multi-view routing framework that predicts future interaction intent from historical actions and uses it to dynamically coordinate global and local views during action generation. On RoboTwin 2.0, its average success rate improves by 10.10 percentage points over the strongest baseline, π_{0.5}; in real-world evaluation, the success rate rises from 71.7% to 85.0%. The framework's intent prediction and dynamic routing modules can be integrated into existing VLA frameworks without modifying the core architecture.
HiSTEVE 1.0 is a hierarchical VLA agent that achieves long-horizon autonomy and native keyboard-and-mouse control in Minecraft through a capability-aware agent harness. Its orchestration layer uses structured memory to track progress, with a VLM planner selecting subtasks within the VLA's capabilities; the control layer uses a single VLA to execute all subtasks with vision-only perception.