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- TacO: A Benchmark Evaluation of Tactile Sensors for Object Manipulation
The authors propose the TacO framework to systematically evaluate tactile sensors based on manipulation policy performance, covering four modalities: visual, acoustic, magnetic, and resistive. They train separate policies for three tasks—pick-and-place with unknown mass, object reorientation, and plug insertion—and analyze how sensor properties such as spatial resolution, shear sensing, tactile representation, and material friction affect task performance. The results show that the value of tactile information depends on the sensor modality, material properties, and specific manipulation task, rather than being universally beneficial; the sensors, code, data, and hardware designs will be made public on the project website.
arXiv cs.RO ·
- AlphaAdj: A Robot Navigation Framework That Adaptively Tunes Control Barrier Functions with Vision-Language Models
The authors propose AlphaAdj, a vision-to-control navigation framework that uses first-person RGB observations and a vision-language model (VLM) to adjust the conservativeness of control barrier functions (CBFs) according to visual semantic context. The VLM outputs a scalar contextual caution signal that dynamically modulates the CBF parameter alpha, while a geometry- and speed-dependent cap bounds how aggressive the behavior can be. To cope with VLM latency, inference and the control loop run asynchronously, and the semantic signal is taken directly from token-level output probabilities rather than from full autoregressive text generation.
arXiv cs.RO ·
- Specification-aware action distribution optimization framework for robotics foundation models
The paper proposes a specification-aware action distribution optimization framework that enforces a class of Signal Temporal Logic (STL) constraints at execution time without modifying the parameters of a pretrained robotics foundation model. At each decision step, the method reasons over the remaining horizon via forward dynamics propagation to compute a minimally modified action distribution that satisfies a hard STL feasibility constraint, and it is validated in simulation with state-of-the-art robotics foundation models across multiple environments and complex specifications.
arXiv cs.RO ·
- FlowTouch: A View-Invariant Visuo-Tactile Prediction Model
FlowTouch is a view-invariant visuo-tactile prediction model that uses an object's local 3D mesh to encode information for predicting tactile patterns, enabling tactile feedback even before the object is touched. The method combines scene reconstruction with a Flow Matching-based image generation model. Results show that it narrows the sim-to-real gap and generalizes to new sensor instances, and the generated tactile images can also be used for downstream grasp stability prediction. Code, dataset, and videos are publicly available.
arXiv cs.RO ·
- π-StepNFT: A Step-wise Negative-aware Online Reinforcement Learning Framework for Streaming VLAs
The authors propose π-StepNFT, an online reinforcement learning framework for streaming VLAs that treats transitions in SDE sampling as supervision units and uses variance-normalized transition errors to distinguish positive and negative velocity branches, without computing the final-action likelihood.
arXiv cs.RO ·
- CHOP: Counterfactual human preference labels improve obstacle avoidance in visuomotor navigation policies
The authors propose CHOP, which fine-tunes visuomotor navigation policies with counterfactual human preference labels to align them with human intuition about safety and obstacle avoidance. On the SCAND dataset, multiple advanced models fine-tuned with CHOP reduced near-collision events by an average of 49.7%, decreased deviation from human-preferred trajectories by 45.0%, and increased average obstacle clearance by 19.8%.
arXiv cs.RO ·
- WildOS: An Open-Vocabulary Object Search System for the Wild
WildOS is a unified system for long-range, open-vocabulary object search that combines safe geometric exploration with semantic visual reasoning for complex outdoor environments without prior maps and with limited depth sensing. The system builds a sparse navigation graph to maintain spatial memory and uses a foundation-model-based vision module, ExploRFM, to score the graph's frontier nodes, simultaneously predicting traversability, visual frontiers, and object similarity to enable real-time onboard semantic navigation.
arXiv cs.RO ·
- EquiContact: A Hierarchical SE(3) Vision-to-Force Equivariant Framework for Spatially Generalizable Contact-Rich Tasks
EquiContact proposes a hierarchical framework consisting of a high-level vision planner, Diff-EDF, and a low-level compliant visuomotor policy, G-CompACT, for learning contact-rich manipulation policies that generalize across task configuration spaces.
arXiv cs.RO ·
- CausalWAM: Learning the Causal Sources of Actions in World Action Models
The authors propose CWAM (Causal WAM), which adds sparse routing paths to the action head of a world action model to separate the latent variables that truly drive actions from the nuisance variables that scene prediction depends on, while keeping the world modeling objective unchanged. The paper proves that under a sparsity constraint, the latent variables used to generate actions are an invertible function of the action-relevant latent variables alone, and the true structure is identifiable up to a permutation of the latent variables, without requiring sufficient temporal dependence or assumptions about how unobserved states evolve. Synthetic data and real-robot experiments validate the theory and show improved closed-loop success rate efficiency.
arXiv cs.RO ·
- Sensorium: An open-source multimodal data collection and curation library for embodied surgical AI
The authors released Sensorium, an open-source Python library built on ROS and ROS2 middleware that records, processes, annotates, and curates multimodal surgical data from configurable robot and sensor streams, covering operator-side acquisition, configurable processing, semantic annotation, and episodic dataset generation. The library was evaluated on 1516 recordings of phantom, ex vivo, and in vivo porcine experiments involving multiple surgical tasks and sensing configurations, with preliminary usability feedback reported.
arXiv cs.RO ·
- PGGT-Loc: Feed-Forward Camera Localization with Primitive-Grounded Geometry Transformer
The authors propose PGGT (Primitive-Grounded Geometry Transformer), a network that takes a single query image and a set of planar map primitives as input and directly predicts a 6-DoF camera pose, enabling feed-forward camera localization. The method sets new state-of-the-art localization performance on both dense surface maps and minimal layout models. Code and models are publicly available, and the paper was accepted to NeurIPS 2026.
arXiv cs.RO ·
- DrawSplat: Atlanta World-Guided Implicit Gaussian SLAM for Indoor Reconstruction
DrawSplat proposes an Atlanta World-guided implicit Gaussian SLAM framework for indoor reconstruction. The method first estimates camera poses, sparse geometry, and gravity direction through joint point-line tracking, then infers structural planes from tracked lines using the shared gravity component of the Atlanta World prior and converts them into differentiable regularization terms on Gaussian centers and normals. The framework also combines an anchor-based implicit neural Gaussian representation with adaptive structural sampling, achieving robust tracking, high-fidelity rendering, and geometrically consistent reconstruction across multiple datasets.
arXiv cs.RO ·
- FOCUS: A First-Order Control Method for Observation-Only Imitation Learning Supervised by Optimal Transport
FOCUS combines local optimal transport alignment with short-horizon differentiable actor-critic learning for observation-only imitation learning, requiring neither temporally aligned expert trajectories nor expert-aligned initialization. The method matches agent rollout windows to multiple sampled expert segments using a soft best-of-K objective, with the transport cost providing a differentiable imitation reward and a learned critic bootstrapping value beyond the rollout horizon.
arXiv cs.RO ·
- SACQ: Structure-Aware Codebook Quantization for Ultra-Low-Bit Vision-Language-Action Models
SACQ is an ultra-low-bit weight quantization framework for Vision-Language-Action (VLA) models, combining structure-aware codebook fitting, action-aware mixed-bit allocation, and two-stage post-quantization refinement.
arXiv cs.RO ·
- MotorSense releases first large-scale paired egocentric video and EMG dataset
MotorSense is the first large-scale paired egocentric video and surface electromyography (EMG) dataset, containing synchronized video, bimanual EMG, IMU, and audio across 100 hours of unscripted manipulation. The research uses this data for cross-modal pre-training between video and EMG to learn an action organization shared across visual and motor spaces, and transfers it to tasks including action recognition, 3D hand reconstruction, and 3D contact reconstruction. Across four action-related visual tasks and eight video datasets, this sensorimotor-aligned representation outperforms conventional video self-supervised methods.
arXiv cs.RO ·
- arXiv paper compares perimeter defense performance of centralized vs. decentralized LLM multi-agent teams
An arXiv paper compared the performance of a centralized commander, an auction-based consensus team, and independent LLM agents under five network conditions in a simplified perimeter-defense simulation. The consensus team had a 15% breach rate, the commander 32%, and independent agents 29%.
arXiv cs.RO ·
- cuPIQP: A Differentiable Batched QP GPU Solver for Robot Learning and Control
cuPIQP is a GPU batched differentiable quadratic programming (QP) solver based on a proximal interior-point method, with unified support for dense, sparse, and multistage problems. In benchmarks with up to 16,384 QPs per batch, it solves up to 18.8 times faster than Moreau and 4.26 times faster than eight-core PIQP; in an end-to-end learning task, training is accelerated by 4.6 times over QPAX and 7.8 times over Moreau.
arXiv cs.RO ·
- Robot Navigation and Canine Welfare: A Pilot Study Surfacing Challenges for Cross-Species Legibility and Proxy Validity
A pilot study in a real home environment evaluated a "curve-out" navigation maneuver for canine welfare: a lateral arcing pass-by trajectory triggered at the dog's alert distance. Contrary to the hypothesis, the maneuver elicited a stronger stress response than a straight-line baseline. The authors argue that the maneuver's discontinuous execution, with the robot stopping and reorienting multiple times to approximate the arc, undermined behavioral legibility, which itself became a stressor. The study thus raises the legibility of robot behavior to non-humans and proxy stimulus validity as structural challenges for animal-machine interaction (AMI).
arXiv cs.RO ·
- AquaProbe: A Method for Adapting Underwater Vision Models by Allocating LoRA Rank According to Water Type
AquaProbe proposes an underwater image adaptation method for frozen vision models: it measures how domain differences are distributed across encoder depths using paired clean images and physically rendered images, then allocates a fixed LoRA rank budget according to that distribution.
arXiv cs.RO ·
- How a team that cannot communicate learns its own state from an opponent's reactions
A game theory and multi-agent study proposes that a team unable to communicate can learn information no single member can see by reading the opponent's reaction to the group as a whole (a tell); one tell can substitute for messaging among all members. In a solved Gaussian model, the team obtains the truth by committing not to use all of the information, and retains the hidden cost the opponent would have paid; an exact test can determine when this conclusion holds in finite games. The study also analyzes how trust is allocated, noting that two interchangeable members responding in different ways can substitute for a single member's private random source.
arXiv cs.RO ·
- ID-WM: A Hierarchical World Model Decoupling State Reachability from Local Control
The authors propose ID-WM (Implicit Dynamics World Models), a hierarchical world model in which a high-level model samples feasible transitions on the state manifold and a low-level world model acts as an inverse dynamics solver to ground those transitions into precise actions. On three navigation and manipulation test suites, it improves over existing policies and diffusion planners by 38.5%, and by 135% over single-layer world model MPC in complex unseen long-horizon configurations.
arXiv cs.RO ·
- RoboArchitect: Automating the Design of Robotic Agents with Persistent Memory
RoboArchitect lets a coding model observe robot rollouts, diagnose failures, and iteratively revise the agent harness around a frozen policy. Without updating any model weights, it raises average success from 9.7% to 43.3% (4.5x) on unseen scenes and from 12.7% to 40.2% (3.2x) on unseen tasks.
arXiv cs.RO ·
- ROOF: A Learning Method for Humanoid Fall Recovery in Overhead-Constrained Environments
ROOF proposes a reinforcement learning method with contact-supported geometric escape guidance that enables proprioception-only humanoid robots to first create clearance and then complete the rise under overhead obstacles. In simulation, the recovery success rate was 88.5% under plates that can only be lifted, compared with 18.9% without geometric guidance; it was 98.6% under free plates with randomized size, mass, and placement. After deployment on a Unitree G1, the policy successfully recovered in 95 of 150 attempts across seven real-world obstacle scenes, including furniture and multi-object arrangements not seen in training.
arXiv cs.RO ·
- REA: A Routed Embodied Agent Runtime for Asynchronous Brain-Cerebellum Control
The authors propose REA, a routed embodied agent runtime that lets a reasoning-focused System 2, an embodied router System 1, and reactive System 0 policies cooperate within a single runtime, with the core rule that a running policy never waits on the brain.
arXiv cs.RO ·
- Hybrid Path Planning Framework for Assistive Wheelchair Navigation in Dynamic Pedestrian Environments
A study proposes a hybrid path planning framework for multi-goal navigation of assistive wheelchairs, using the Traveling Salesman Problem (TSP) for global task ordering combined with the Potential Field (PF) method and heuristic local planners such as RRT and A* to achieve collision-free navigation, and develops two algorithms, TSP-PF-RRT and TSP-PF-A*. In Monte Carlo simulations of crowded and super-crowded scenarios, the framework achieves efficient multi-goal navigation in dynamic human environments containing heterogeneous static and dynamic obstacles, with TSP-PF-RRT producing fewer or zero collisions and shorter travel distances.
arXiv cs.RO ·
- Proof-Carrying Evolution: Language Models Design Formally Verifiable Controllers
The study proposes Proof-Carrying Evolution (PCE), in which a language model provides both a controller and its certificate during search; only candidates that pass certificate verification can be returned, and the deployed artifact is the control law together with its certificate. The authors compare four language models with classical search and synthesis methods on speed-limited docking, maneuvering-target pursuit, and SO(3) attitude control. Certificates for linear problems are verified using exact rational arithmetic; on SO(3), floating-point verification is followed by exact re-verification of each reported champion. The best run of each method also receives a Lean 4 proof.
arXiv cs.RO ·
- Nexus-VLA: Connecting Human and Robot Experience with a Shared Hierarchical Semantic Latent Space
The authors propose Nexus-VLA, a hybrid policy learning framework that enables cross-embodiment knowledge transfer through end-to-end human-robot joint training, allowing human manipulation experience to directly contribute to robot policy learning. The framework first establishes hierarchical cross-embodiment semantic alignment from task intent to fine-grained interactions, then encodes multi-level shared knowledge into a shared manipulation latent representation used directly for continuous action generation. In RoboCasa-GR1, OOD settings, and real-robot experiments, Nexus-VLA achieved an 80.5% task success rate on RoboCasa-GR1.
arXiv cs.RO ·
- Suspended fabric robotic manipulation surfaces: modeling, design space, and multimodule extension
This paper proposes a separable catenary model for suspended-fabric Robotic Manipulation Surfaces (RMS) that can quickly compute the surface shape for given actuator heights, as well as the actuator heights needed to achieve a desired local slope. The closed-form inverse solution lets a simple PID controller position objects on the fabric surface, and the model relates fabric length, module span, and actuator stroke to the reachable workspace, yielding a lower bound on minimum fabric length and design charts.
arXiv cs.RO ·
- Open-Source Knee Exoskeleton: Generating Biological Torque Control with Musculoskeletal Simulation
A research team has released an open-source knee exoskeleton built from off-the-shelf components, whose biological torque controller is generated entirely by simulation, requiring no training data collected on the target device. The method uses a muscle-driven imitation policy to reproduce squatting motions from a public biomechanics dataset, then distills the knee joint torque into an estimator that relies only on onboard sensors.
arXiv cs.RO ·
- Language-conditioned behavioral cloning for autonomous endoscope guidance
The study proposes language-conditioned behavioral cloning for autonomous endoscope guidance, defining 6 clinically relevant guidance behaviors and collecting 235 subtask demonstrations. In preliminary phantom experiments, a single visuomotor policy achieved 100% success on focus, overview, and stop, 80% on search-tip and follow-tip, and 50% on horizon leveling. The authors note that the failure modes indicate more complex guidance tasks require temporal context.
arXiv cs.RO ·
- Adaptive Coverage Algorithms for Mobile Ad Hoc Networks in Dynamic Worlds: CADENCE and DADENCE Extended to DPOCGAGP
The study extends two algorithms, CADENCE and its decentralized version DADENCE, to the dynamic version of POCGAGP (DPOCGAGP), adding two deployment-time perturbation settings, Agent Loss and World Change, for multi-agent systems to establish a fully covering communication network in unknown constrained spaces. On more than 4,500 benchmark test cases of varying scale and structure, the improved algorithms outperform another state-of-the-art benchmark algorithm. The work focuses on simulation benchmark evaluation and has not yet involved real-robot validation.
arXiv cs.RO ·
- Online Learning of Heterogeneous Locomotion Dynamics for Precise Mobile Manipulation
The study proposes an online method for learning closed-loop dynamics under a frozen locomotion policy, using a contaminated Gaussian model to distinguish predictable from unpredictable responses and combining a Gaussian process with random Fourier features to build an online learner that reduces bias from heterogeneous observations. Combined with model predictive control (MPC), the method predicts floating-base motion more accurately; in simulation and hardware experiments, end-effector tracking error was 0.01 m, and a finite-sample error bound for the online learning is provided.
arXiv cs.RO ·
- Movens-Zero: Scaling humanoid motion foundation models with large-scale human videos
The research team proposes Movens-Zero, a scalable understanding-generation-execution architecture that supports real-time closed-loop humanoid motion generation under text, speech, music, and video inputs. Its accompanying dataset, Movens-5M, is built from Internet videos through clip generation, quality filtering, semantic annotation, motion recovery, and physical refinement with humanoid retargeting. It contains 15 million human motion sequences and 5 million physically executable humanoid motion sequences, totaling over 10,000 hours.
This technical report presents a complete path for building a humanoid motion dataset from Internet videos and training a unified understanding-generation-execution model, offering a reference for data scale and architecture design.
arXiv cs.RO ·
- SimuGuard: Auditing Contact Artifacts in Simulation Benchmarks Such as LIBERO and RoboTwin
The authors propose SimuGuard, an auditing plugin for LIBERO, RoboTwin, RoboCasa, and RoboDojo that identifies failures caused by the simulator rather than the policy. On RoboTwin, the authors trace these failures to the physics engine's collision detection: when geometry is 1 to 2 centimeters apart, the engine reports a penetration of about 2 centimeters, and the solver ejects the object at several meters per second because the distance algorithm discards triangles below a fixed area threshold.
The original traces failures in simulation benchmarks to a physics engine collision detection constant and provides a before-and-after success rate comparison, which can serve as a reference for teams doing simulation evaluation.
arXiv cs.RO ·
- SketchMate: Building Sketch Models with VLMs for Zero-Shot Precision Insertion
SketchMate has a vision-language model build a sketch model for insertion tasks, consisting of a few geometric primitives and two mating coordinate frames, which a planner and compliant controller then execute. In a strict kinematic test of 69 simulated insertion episodes, the method inserted 88% of parts at the assembled pose, while ReKep and MOKA using the same VLM achieved no successful insertions.
arXiv cs.RO ·
- Integrating Low-Thrust Transfer Dynamics into POMDP Planning for On-Orbit Servicing Missions
This research models low-thrust (LT) orbital transfers within a partially observable Markov decision process (POMDP) for on-orbit servicing (OOS) mission planning, adding the servicing spacecraft's remaining propellant to the state space to support mass updates. High-thrust transfers are modeled with a precomputed transfer matrix, while low-thrust transfers are represented by an analytical GEO phase model, with propellant consumption and maneuver feasibility computed for given transfer-duration alternatives. In a simplified OOS scenario, SARSOP is used to solve and compare high-thrust and low-thrust policies, quantitatively assessing the feasibility and operational impact of the low-thrust option.
arXiv cs.RO ·
- Visual-Inertial-Encoder RGB-D SLAM for Differential Wheeled Robots
The study proposes a generative measurement model for encoders and derives the maximum a posteriori estimation expression and the Jacobians required for optimization, building a tightly coupled visual-inertial-encoder RGB-D SLAM system. On a self-built dataset, the system's real-time state estimation achieves a root mean square error of about 2-7 cm, used to compensate for the failure of pure vision solutions during temporary feature point loss and fast motion. The source code and the dataset containing encoder information have been made public.
arXiv cs.RO ·
- Higher-Order Kinematic Derivative Methods for Aspect-Preserving Real-Time Numerical Inverse Kinematics
The paper uses higher-order kinematic derivatives of serial robots to propose an aspect-preserving real-time numerical inverse kinematics method, yielding the QuIK4 and QuIK5 solvers with fourth- and fifth-order convergence. The method also includes an aspect-preserving step limiter that extrapolates the Jacobian determinant along the step to predict and avoid singularities, as well as a certifier that uses Hermite interpolation to verify the aspect of solutions and a certified restart algorithm. The authors claim the failure rate is reduced by up to four orders of magnitude compared with existing methods, and they release a C++ library with ROS 2 integration.
arXiv cs.RO ·
- When Architectural Spaces Begin to Act: How Architectural Robots Reshape Living and Spatial Control
Architectural robots, such as robotic partitions, ceilings, furniture, and facades, can move autonomously and reconfigure space. The author reflects on this: their impact goes beyond the moment of motion to include the spatial configurations that persist after motion stops. Such actions may benefit some occupants while disadvantaging others, and multiple robots together can make an entire architectural space appear to act. The author argues that understanding robotic architecture requires looking beyond short-term human-robot interaction to how people inhabit, negotiate, and retain control over autonomously reconfigurable spaces.
arXiv cs.RO ·
- Flexible Sensing Robot Manipulation: Deformation Estimation, Kinematics, and Control Based on Distributed Inertial Measurement
This paper proposes a flexible sensing manipulation framework that directly connects the structural dynamics of flexible links with sensing, kinematics, and control. The method first identifies the dominant vibration modes of each flexible link according to response convergence and excitation bandwidth criteria. Under the condition of full-rank modal observability, m retained modes require only m independent slope measurements and one reference IMU, and the continuous link deformation is reconstructed accordingly for flexible sensing forward kinematics and a PDE-based controller.
arXiv cs.RO ·