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#Research
Oct 26Mon
  1. arXiv cs.RO62

    OWLS: Open-world language-conditioned speed fields for zero-shot rover navigation

    The authors propose OWLS (Open-world Language-conditioned Speed Fields), which plans directly over the dense feature maps of a vision-language model (VLM): given a description of traversable terrain, the VLM scores every pixel of a single camera image, and OWLS projects these scores onto the ground, adapts them to the platform with two morphological operations, and uses them as the speed field of a minimum-time planner, so that terrain appearance and obstacle spacing jointly shape a single trajectory.

  2. arXiv cs.RO58

    UAV-SwarmAgent: Multi-UAV reconnaissance and task allocation via code generation and vision-language reasoning

    UAV-SwarmAgent is a hierarchical multi-agent system in which an operator issues a mission in one natural-language sentence, and a ground-station agent generates and executes code that turns it into routes for each UAV. The system uses a vision-language model to locate inspection targets on an aerial map, balances them among N UAVs by route length, and then worker agents equipped with lightweight decision models verify incidents.

  3. arXiv cs.RO53

    HGWM: Zero-Shot Object Navigation Using Hierarchical Graph-Guided World Models and Graph Matching

    The authors propose HGWM, which combines an LLM-generated room-anchor prior with persistent spatial-semantic memory to select search directions in zero-shot object navigation. On HM3D v0.1, it reports 62.6% success rate and 31.5% SPL, versus 58.1% and 31.2% for WMNav; on MP3D, 46.8% and 17.9%, versus 45.4% and 17.2%.

  4. arXiv cs.RO38

    Yonghyeon Lee releases v1 of compiled lecture notes "Optimization, Optimal Control, and Reinforcement Learning for Robotics"

    The v1 lecture notes "Optimization, Optimal Control, and Reinforcement Learning for Robotics," compiled and edited by Yonghyeon Lee, have been released on arXiv, emphasizing the mathematical connections and practical applications among the three. The notes were written through human-AI collaboration and are a compilation of existing material rather than new research results; they are currently a preliminary version with no figures or graphs, and revisions and expansion are planned.

  5. arXiv cs.RO42

    Facultative Swarming: A Capability, Not a Category, for Multi-Robot Systems

    This research proposes a unified control framework that shifts multi-robot systems from "obligate swarms" constrained to pure self-organization toward "facultative swarms"—hybrid architectures that operate with centralized planning when conditions are permissive and degrade smoothly into decentralized swarming under environmental stress. The authors argue for redefining swarming behavior as a dynamic capability rather than a fixed system category, thereby exploiting centralized optimality when conditions allow and the flexibility of self-organization when they do not.

  6. arXiv cs.RO71

    From Freehand Demonstrations to Autonomous Robotic Ultrasound: An Anatomy-Anchored Imitation Learning Framework

    The study proposes an anatomy-anchored imitation learning framework that combines anatomy-guided probe placement with learned ultrasound navigation. Across 16 unseen participants, task success rates for standard-view acquisition of the kidney, gallbladder, and liver and for radial artery scanning were 96%, 94%, 88%, and 94%, respectively.

  8. arXiv cs.RO60

    R²E: A multi-role LLM evolutionary framework for automated reward function design

    The authors propose R²E, a framework for automated reward function design that uses explicit role specialization to mitigate insufficient exploration and optimization instability in LLM-based reward design. The framework includes a novelty explorer responsible for expanding the search space, a guardian responsible for conservative refinements to improve training stability, and an evolver that recombines high-performing rewards through crossover and mutation, with a dynamic scheduling strategy that gradually shifts the search from broad exploration to focused exploitation. R²E also introduces a global elite pool that retains the best-performing rewards to guide subsequent iterations. Experiments on multiple robotic tasks show the effectiveness of the method.

  9. arXiv cs.RO58

    GTB: A Trajectory Bundle Graph Motion Planning Method for Nonlinear Dynamics

    The authors propose the Graphs of Trajectory Bundles (GTB) method, which combines sampling-based motion planning, trajectory optimization, and graph search for motion planning of systems with nonlinear and even black-box dynamics. Based on a graph-of-convex-sets decomposition of the workspace, the method iteratively constructs a graph of trajectory bundles that captures the local dynamics at each vertex, then solves a shortest-path problem on the graph to obtain trajectories that satisfy the system dynamics and constraints such as collision avoidance and safety in the limit. The authors claim it can handle highly nonlinear, unstable, and even black-box dynamics with complex constraints.

  10. arXiv cs.RO62

    LogLook: Execution-Log Memory for Frontier-Model Robot Planners

    LogLook proposes an execution-log-style memory that lets frontier vision-language model planners record executed actions and earlier notes, and selectively review image frames based on expected changes. With the benchmark's built-in motion planner, it matches the success rate of note-only memory while using 43% fewer tokens and less than half the images, and it causes notably fewer failures due to memory or observation errors; under direct control, it solves 93.5% of the RoboMME test set.

  11. arXiv cs.RO62

    DynaSlots: A Slot-Level 3D Dynamics Pretraining Framework for Robot Manipulation

    DynaSlots is an action-label-free 3D visual pretraining framework that decomposes each point cloud frame into a small set of temporally consistent slots and models dynamics at the slot level rather than the scene level. A temporal slot propagation module keeps slot assignments consistent across frames; an inverse dynamics model extracts a latent action for each slot from feature differences; and a diffusion-based forward dynamics model, paired with a self-supervised interaction head, predicts each slot's future state and learns pairwise proximity and motion relationships.

  12. arXiv cs.RO62

    MAST-VLA: Multi-Skill Robot Manipulation with Skill-Conditioned Experts and Trajectory-Affordance Priors

    MAST-VLA proposes a dual-conditioning framework that introduces skill-conditioned LoRA Mixture-of-Experts adapters into the LLM backbone and converts predicted affordance heatmaps into a patch-level trajectory-affordance spatial prior for the action head. In the CALVIN ABC→D long-horizon multi-skill cross-scene setting, it achieves an average completion length of 4.65 over five-subtask sequences, the best performance in that setting. Real-robot multi-skill sequence experiments further validate its deployability under real perception and control noise. The paper has been accepted to CoRL 2026.

  14. arXiv cs.RO60

    From Tabletop to Walking Robot: Deploying a VLA Model on a Legged Manipulation Robot

    The research team deployed a VLA model on a Unitree Go2 quadruped equipped with an AgileX Piper robotic arm, achieving closed-loop mobile manipulation, and reported choices such as model variant, action horizon, and demonstration conventions that made real-robot deployment feasible. For the pick-and-place task, expanding demonstration coverage reduced held-out end-effector error by 30%, and adding secondary skills such as pushing introduced new behavior modes without degrading baseline performance. Closed-loop real-robot experiments showed that dataset composition affects task success.

  15. arXiv cs.RO57

    RoboCode: Learning Reusable Action Primitives for Robot Manipulation

    RoboCode learns short-horizon motion patterns that can be reused across tasks through a shared soft-gated primitive action interface, representing action chunks as soft compositions of multiple learned primitives rather than a single discrete code. The interface is learned on action chunks pooled across multiple tasks and then frozen, after which task-specific state encoders are trained to map into the shared latent space. On five ManiSkill tasks, it achieves an average closed-loop success rate of 75.6%, above ACT's 68.8% and Diffusion Policy's 41.6%, with an average action chunk reconstruction MSE of 0.0187.

  16. arXiv cs.RO58

    Do VLAs Know They Will Fail? Interpretable Success and Failure Patterns in Physical AI Systems

    Research shows that sparse autoencoder (SAE) representations of OpenVLA activations contain outcome-predictive information and can predict task failure before the robot reaches an unrecoverable state. The authors also identify individual SAE features associated with specific stages of success or failure, and uncover recurring behavioral patterns by localizing temporal intervals where these features show significant activation changes. The team further builds a VLM-based interpretation pipeline to generate behavioral descriptions of these patterns and validates their correspondence to feature activations on held-out intervals.

  17. arXiv cs.RO34

    RAISE-Vision: An Automated Contact Angle Measurement Platform for Contact Lens Wettability Characterization

    RAISE-Vision is an automated platform for contact angle (CA) measurement on curved hydrogel surfaces, intended for in vitro wettability analysis of contact lenses. Its 3D-printed lens mounts, camera holders, and drying apparatus enable an automated, repeated sessile-drop workflow within a liquid handling system. A custom open-source image processing script combines elliptical fitting and the tangent method to handle droplet deformation on curved surfaces. In 10 consecutive measurements of a senofilcon A lens in air, the contact angle rose from 62.6±5.5° to 83.4±2.1°, with no significant difference from the manual protocol results (p=0.470).

  18. arXiv cs.RO58

    Geometry-Aware Reinforcement Learning for Close-Fitting Wearable Insertion

    The paper formulates robot-assisted dressing as a geometry-aware insertion problem and proposes a reinforcement learning framework combining a staged curriculum with a finger-wise insertion soft gate. In post-training evaluation on the deformable bracelet task, the full method achieved a success rate of 84.7±1.2%, with the insertion gate contributing the most and the highest score when combined with the curriculum. On a dummy hand, the same policy succeeded in 15 of 20 real-robot trials.

  21. arXiv cs.RO42

    TTT in VLA Models: Exploring Training with Human Demonstrations in InternVLA-A1 and Pi0.5

    The study integrates Test-Time Training (TTT) into InternVLA-A1 and Pi0.5, training on paired demonstrations and robot trajectories to explore action generation conditioned on human demonstrations. At the current scale, adding TTT and training on paired data did not produce consistent target selection based on demonstrations, and shorter sequences, full-trajectory gradient connectivity, and contrastive regularization also did not clearly improve responsiveness to demonstrations. When demonstrations were reduced to single-frame cues, real-robot execution still preferred cups at fixed positions.

  22. arXiv cs.RO69

    DiscoDemo: Using Reinforcement Learning to Generate Diverse Demonstration Data to Improve Imitation Learning

    DiscoDemo uses a latent-conditioned reinforcement learning generator with privileged information, together with a parallel reverse curriculum and a success-gated diversity objective, to produce multiple successful closed-loop behaviors; after safety filtering, these are exported as a static dataset for downstream visuomotor imitation learning. On four manipulation tasks, the average downstream success rate in simulation rose from 40% for the strongest baseline to 62%, and on real robots from 39% to 78%. The authors believe that the diversity of successful solutions can improve imitation learning for pretrained flow-matching VLAs and complements corrective data collection.

  23. arXiv cs.RO52

    Robotic Budding: Homogeneous Modular Robots Achieve Physical Self-Replication

    Researchers propose a standardized robotic module that combines a magnetic docking interface with two actuated joints and can fold from a flat two-dimensional configuration into three-dimensional structures. Modules of a single design can be assembled into two morphologies: a three-module "trilobite" and a five-module "seal". Using a small set of discrete motion commands, an operator steers an assembled parent robot to dock magnetically in sequence with spare modules, assembling a second, structurally identical individual that then separates from the parent. The parent retains all of its original modules, forming budding-style physical reproduction.

  24. arXiv cs.RO47

    Input Bias Correction in Gaussian Process Regression

    To address input bias in GP regression caused by mobile sensor pose estimation errors, a bias-aware Gaussian process (Ba-GP) framework is proposed. It uses the recorded GP posterior-mean gradient to propagate input bias and noise into mean and covariance corrections, and provides a numerically efficient first-order correction algorithm based on the additive covariance structure.

  25. arXiv cs.RO60

    Distributed Swarm Morphogenesis with Neural Cellular Automata

    The study proposes a modular framework that combines neural cellular automata (NCA) with existing site selection and collision-aware control for swarm robot morphogenesis. The framework introduces a decentralized attachment-site discovery mechanism in which robots already in place identify new attachment sites by updating and sharing local NCA states, eliminating the need for a predefined site list during deployment. The authors also propose offline sparse-retention fine-tuning and a false-positive loss to bridge NCA growth and gradual physical assembly. Simulations scale to several hundred robots and improve morphology accuracy, and experiments with Crazyflie quadrotors validate the integrated execution pipeline.

  26. arXiv cs.RO56

    Move, don't think: Sample-efficient collision avoidance learning with lossy proximity sensors

    This paper investigates whole-body proximity sensors as an inductive bias for learning reflexive collision-avoidance behavior, finding that low-resolution lossy sensors were a stronger bias than high-resolution full coverage. The study compared eight candidate representations in a reinforcement learning environment, including latent compression, future contact anticipation, lossy corruption, and privileged state estimation, and trained over 200 humanoid robots end-to-end to play a simplified version of dodgeball under intentionally constrained sample and compute budgets. The authors also found that anticipating when and where contact occurs actually reduced learning sample efficiency.

  27. arXiv cs.RO63

    Lunar swarm robots exhibit emergent cooperative thermoregulation via reinforcement learning

    The study used reinforcement learning to train a single policy shared by a set of twenty robots, with only the mean temperature of the five coldest robots at the end of the night as a terminal reward, and two behaviors emerged: aggregation and rotation between the core and the edge. The minimum stored energy required per robot for survival was measured at 155.6 +/- 1.3 pcm-h (1 pcm-h is 7.2 kJ of stored heat), 37% lower than stationary huddling and within 13% of a physics-based lower bound; under an energy budget in which stationary huddling would lose six robots, all robots survived.

  28. arXiv cs.RO78

    Study: Full-weight VLA fine-tuning without an fp32 master copy silently erases most updates

    The paper points out that when VLA models are fully fine-tuned in bf16 without keeping an fp32 master copy, AdamW rounds away any update smaller than 1/512 of a weight; 9 of 12 open-source VLA codebases train this way by default or in documented recipes, including LeRobot's SmolVLA default configuration and the π0/π0.5 guides.

    Why it matters: The original quantifies the proportion of updates lost in bf16 fine-tuning and provides a LIBERO success-rate comparison for the fp32 master copy, which can serve as a reference for training configurations.

  29. arXiv cs.RO63

    BM2PC: A Bayesian Multimodal MPC Framework for Dynamic Manipulation of Deformable Linear Objects

    This paper proposes BM2PC, a Bayesian multimodal MPC framework for dynamic manipulation of deformable linear objects (DLOs). It formulates multimodal planning as a set optimization problem that balances control quality and behavioral diversity, and maintains multiple candidate control modes through incremental updates.

  30. arXiv cs.RO70

    EgoForesight: Scaling Policy Learning with Noisy Egocentric Action Data

    EgoForesight is a vision-language-action framework that jointly leverages robot demonstration trajectories and noisy egocentric videos for policy learning, reducing the impact of unreliable action labels through reliability-conditioned corruption and reliability-weighted regression, and supplementing supervision from demonstrated outcomes via privileged-future representation distillation.

  32. arXiv cs.RO65

    WorldMirror: An agentic framework for generating interactive simulation worlds from in-the-wild robot demonstrations

    WorldMirror is an agentic real-to-sim framework that builds interactive digital twins from in-the-wild robot demonstrations. It frames real-to-sim construction as the iterative refinement of an executable world program: a coding agent decomposes the scene, plans reconstruction steps, and dispatches image and 3D generation tools, then revises the program based on verification feedback. Experiments on DROID show that the framework can reconstruct visually faithful, geometrically consistent interactive worlds while preserving the physical validity of the recorded robot trajectories.

  33. arXiv cs.RO62

    SEVI: A Safety Evidence Framework for Real-World Reinforcement Learning

    The authors propose the SEVI framework, which uses an evidential safety critic to simultaneously estimate future constraint-violation risk and epistemic uncertainty in a single forward pass, judging before an action is executed whether it is unsafe or insufficiently supported by evidence, and overriding that action with an offline-trained recovery policy.

  34. arXiv cs.RO64

    PrefO2O: Zero-Shot Preference Reinforcement Learning via Offline-to-Online Latent Preference Transfer

    PrefO2O proposes an offline-to-online latent preference transfer framework that enables zero-shot preference-based reinforcement learning (PbRL) on target tasks without any human feedback. The method learns a task-invariant latent preference space from a small amount of offline preference data on source tasks, disentangles transferable preference structures from task-specific variations, and uses confidence-weighted optimal transport alignment to generate pseudo-preference supervision for online reward adaptation.

  35. arXiv cs.RO58

    KiRoSe: Efficiently Segmenting Robot Demonstrations with Kinematic Data

    KiRoSe proposes segmenting demonstration data using robot kinematic data rather than multi-camera video streams, compactly representing recurring motion events through Sparse Motion Tokens (SMTs). The method supervises an open language model with subtask annotations to interpret token sequences, clearly outperforming baseline segmentation methods on the F1 metric, and can transfer zero-shot to unseen robot embodiments and first-person human motion. Beyond segmentation, KiRoSe can also track task progress and detect action reversals on unseen robots.

  36. arXiv cs.RO78

    RepresentNav: A Navigation Foundation Model Built on a Self-Constructed Representation Space

    The authors propose RepresentNav, a navigation foundation model that uses Representation Residuals, a three-layer transformer, to aggregate full-depth features from multiple vision foundation models and construct a representation space driven by the policy's own objectives.

    Why it matters: The paper proposes constructing a visual representation space using the policy's own objectives and predicting future scene representations within it, offering transferable ideas for the representation design of navigation policies.

  40. HuggingFace Daily Papers63

    PhysEvolve: A Self-Evolving Framework for Physical Reasoning Driven by Executable Worlds and Verifiers

    PhysEvolve proposes a verifier-grounded self-evolving framework that lets vision-language models learn causal physical reasoning from executable world programs they propose themselves and from simulation-generated tasks. In each round, a shared VLM proposes world programs and evidence schemas; a deterministic physics engine freezes each valid world and simulates matched baseline and body-elided counterfactual branches to generate discrete visual tasks; then exact verifier feedback updates the policy and curriculum via GRPO.