Nachrichten & Forschung

arXiv:2610.27677v1 Announce Type: new Abstract: In object goal navigation (ObjectNav), an embodied agent must find a target object (e.g., "find a bed") in a previously unseen environment, relying on spatial commonsense such as object co-occurrence. Large Language Models (LLMs) encode this commonsense, but deploying them on robots exposes a trade-off between reasoning quality and efficiency: cloud-based models incur latency and privacy risks, whereas small on-device LLMs are fast but prone to hallucinated spatial judgments. Hybrid reasoning LLMs offer both a fast non-thinking mode and a slow thinking mode, which raises a key question for embodied agents: when is slow thinking worth its cost? We propose CovNav, a training-free framework that answers this question with Conformal Prediction (CP), turning dual-process (System 1 / System 2) inference into an uncertainty-aware decision. Much like recent decision models such as Jev, CovNav casts each spatial query as a structured binary decisi

arXiv:2610.27657v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models generate robot actions from visual observations and language instructions, but effective manipulation also requires anticipating how the environment will change. Existing world-action models explore future image prediction and latent dynamics modeling. However, generating complete future images can be computationally expensive, while highly compressed latent representations may obscure local changes. To address these challenges, we propose PLaCA, a world-to-action framework built around future latent change. PLaCA directly predicts spatially structured patch-level future latent change from the current multimodal input, preserving local information about expected environmental changes. We introduce patch-level change-aware supervision to emphasize regions undergoing larger changes and a future-to-action interface that explicitly incorporates predicted future latent change into action generation. Experime

arXiv:2610.27622v1 Announce Type: new Abstract: Handheld demonstration devices decouple data collection from the robot, but conventional designs burden the hand with a wrist camera, cables and a pose tracker (SLAM, an IMU, a VR controller, or a fixed external camera that the collector's body occludes), and burden the pipeline with as many data streams to timestamp, synchronise and cross-calibrate. We show that a single egocentric camera (USD 122) worn on the collector's head replaces the wrist camera, the pose tracker and their separate streams with one. It reads printed fiducials on an electronics-free glove (USD 22) against paper tiles taped around the workspace, giving the 6-DoF gripper pose and the jaw width from the same frames the policy will see; at deployment the same camera rides a low-cost (USD 120) arm beside the robot that mimics the collector's head from the robot's joint state. Against the robot's encoders the labels are accurate to 6.5 mm and 1.6 degrees median with 93-1

arXiv:2610.27618v1 Announce Type: new Abstract: Visual manuals communicate procedural knowledge through stepwise diagrams, but translating these instructions into reliable long-horizon robotic execution remains challenging. We introduce JARVIS, a method-agnostic benchmark for autonomous visual-manual-guided execution without additional language instructions. JARVIS comprises 236,679 LEGO assembly tasks across 8,987 scenes, with a carefully curated JARVIS-v1 suite of 50 tasks for interactive robotic evaluation. Tasks span varying initial structural complexities and assembly horizons, requiring agents to interpret manuals, ground actions in an evolving workspace, and execute the intended procedure. A structure-aware evaluation protocol captures geometric accuracy, structural relations, construction order, partial progress, and completion. We further introduce JARVIS-agentic, a tool-grounded embodied agent that composes robot primitives into executable programs and uses execution feedback

arXiv:2610.27616v1 Announce Type: new Abstract: Visual navigation in cluttered environments requires robots to recover previously observed structure and infer obstacles and free space that remain occluded. We present a framework that combines temporal aggregation and occupancy completion in an evidential bird's-eye-view (BEV) representation predicted from short RGB sequences. A decoder adapts frozen VGGT-Omega features and combines historical residual proposals using cellwise weights and a learned no-update option. The BEV predictor produces a local map in the latest ego frame without depth or camera-pose inputs or explicit geometric warping. The evidential model treats observed free space separately and predicts occupancy with associated confidence across the remaining supported region. We train the predictor on simulated RGB sequences paired with complete and visibility-masked occupancy annotations. Our experiments evaluate navigation in simulation and on a wheeled robot, with additi

arXiv:2610.27613v1 Announce Type: new Abstract: Can embodied task agents use tactile experience to perform fine manipulation without task-specific model training? We investigate this capability through Tactile-ETA, a system that incorporates tactile experience into an embodied task agent through in-context learning. Successful expert demonstrations show how contact changes during manipulation. We pair each recorded robot movement with its camera views and bilateral tactile observations. We keep tactile observations at the start and end of each movement and add two neighboring time points when the tactile images change markedly in between. The agent combines these demonstrations with current vision, tactile observations, and proprioception to select actions through general-purpose robot tools, without updating model parameters. Across four simulated manipulation tasks, adding tactile observations to the same two expert demonstrations increases average success from 15.75% to 24.00%, with

arXiv:2610.27504v1 Announce Type: new Abstract: Heterogeneous multi-robot task allocation depends on estimates of how suitable individual robots are for candidate tasks, yet suitability can be defined in substantially different ways. We compare ten deterministic methods and two LLM-based variants spanning rule-based, similarity-based, requirement-oriented, fuzzy, and semantic approaches. Using common robot and task profiles, we compare how the methods score and rank candidate robots, identify feasibility, and select a top robot, while examining how these outcomes change with team composition, scale, spatial context, capability uncertainty, and task representation. The methods frequently disagree despite operating on identical scenarios. Generic similarity measures can violate properties expected under a one-sided requirement-satisfaction interpretation of suitability, while LLM-based scoring introduces additional stochastic and linguistic sensitivity. These results show that robot--tas

arXiv:2610.27496v1 Announce Type: new Abstract: Foundation models now serve as reasoning layers for robot teams, but it remains unclear whether embodiment-specialized vision-language models (VLMs) preserve the social-reasoning capabilities of general-purpose VLMs. We evaluate Gemini Robotics-ER on SHREC, a benchmark of real human-robot social interactions, and compare it with published general-purpose VLM baselines. Robotics-ER outperforms GPT-4o-mini on error/competence detection but performs worse on detailed social-attribute judgments; adding a visual frame helps one task while hurting the other. These results suggest embodiment-specialized models may perform differently on social judgments that can be read more directly from observable interaction cues versus those requiring more inference about a person's internal state. Because the comparison spans model families, these differences cannot be attributed to embodiment specialization alone. This matters for multi-robot systems, wher

arXiv:2610.27485v1 Announce Type: new Abstract: Capability claims in robot manipulation are unverifiable, and existing leaderboards are each closed, simulated, or gameable. We propose HATCH, a robot escape room: an openly specified, physically replicable, sensor-scored, and unrehearsable benchmark. Tasks span five skill categories at three difficulty levels and are chained so that the sensor confirming one task's completion unlocks the next, turning a room into a single long-horizon evaluation. Success is confirmed by instrumentation rather than human judgment, scoring is embodiment-agnostic, and object placements are randomized by a run-day seed. Entrants submit attested self-runs (uncut video, sensor logs, and the seed), with occasional organized events for calibration. Task specifications, build guide, bill of materials, and scoring rubrics are released under an open license.

arXiv:2610.27472v1 Announce Type: new Abstract: Mobile legged robots offer unique advantages for on-site construction, including the ability to traverse unstructured terrain, climb stairs, and operate independently between spatially separated work zones. This paper presents a proof-of-concept system for autonomous brick stacking using a Boston Dynamics Spot quadrupedal robot equipped with a manipulator arm. The system operates between a supply area and an assembly area, using AprilTag fiducials for localization and autowalk missions for navigation. Target brick positions and orientations are specified in CAD software and exported directly for robotic execution. We evaluate placement accuracy through ten repeated assemblies of a curved four-layer wall comprising 14 wooden bricks. Results show mean translation errors below 10 mm and mean yaw errors below 1.5 degrees, while the overall RMSE across all runs is 27.72 mm (translation) and 2.06 degrees (rotation). The system was also tested o

arXiv:2610.27387v1 Announce Type: new Abstract: Action-conditioned video generative models trained on successful, goal-directed demonstrations tend to ignore the commanded action, rendering a plausible expert-like future regardless of what is asked of them. We trace this failure to training data that lack sufficient action influence. We analyze this metric and prove that it upper bounds the action influence that any model trained on that data can express. Expert demonstrations, whose narrow action distributions let next states be predicted without attending to the action, set a low ceiling that no amount of model capacity alone can raise. Raising it requires collecting different data. We introduce Action Influence Maximization (AIM), an algorithm for actively collecting training data for improving dataset action influence. We compare the performance and data-efficiency of AIM against exploration heuristics and intrinsic-motivation methods on simulated navigation and manipulation tasks.

arXiv:2610.27350v1 Announce Type: new Abstract: Frontier models can control robots through agent harnesses that loop on one question: what motion comes next toward the user's goal? We show that this default loop carries out harmful instructions. With GPT-6 Astra controlling a low-cost SO-101 arm through the Inspect Robots harness on three household tasks whose hazard is common knowledge, such as "put the knife in the toy basket", the policy refused 0% of 15 trials, attempted the harm every time, and completed it in 27%. Its one exit, give_up, is meant for goals that cannot be achieved, and it never cited the hazard. We add an optional runtime safety check to the loop: before the first motion, the model is asked whether it would carry out the instruction, given the same camera frames. The check would have stopped all 15 hazardous instructions, including the 4 that completed the harm, and flagged none of the 15 harmless controls. Code and data: https://github.com/Arnav-Gr0ver/Refuses-Whe

arXiv:2610.27336v1 Announce Type: new Abstract: Accurate head immobilization is critical for medical procedures such as neuroimaging and intracranial radiation therapy, where even minor motion can compromise precision. Traditional methods, such as rigid stereotactic frames and thermoplastic face masks, are invasive, uncomfortable for patients, and require a time-consuming setup. To overcome these limitations, a novel approach employs a 6-degree-of-freedom (6DoF) robotic head holder that is integrated with real-time optical motion tracking, enabling dynamic compensation for head movements without physical restraint. However, implementing this system requires a detailed biomechanical understanding of skull-cervical spine interactions to define safe and effective motion limits. In this study, we conduct a human volunteer investigation using a 6DoF Stewart platform robot to characterize biokinematic workspace of the head-neck system and establish foundational data for human-robot interacti

arXiv:2610.27315v1 Announce Type: new Abstract: The integration of dynamic legged locomotion and aerial mobility offers versatility that is deterred by challenges in payload distribution, actuation, and the contrasting requirements of both domains. In this paper, we present the design and operation of a multimodal quadrupedal aerial-legged robot. The presence of legged aerial systems also allows for thruster-assisted legged locomotion, which we make possible with a unified control framework using Control Contraction Metrics (CCM). We search for separate domain-specific contraction metrics for walking and flight independently offline and unify them using an online energy-based Control Lyapunov Function Quadratic Program (CLF QP). This enables optimal, cross-domain control allocation, which we show using simulations. We also show experimental results that validate the mechanical viability of our hardware and the paradigm of thruster-assisted trotting. The stability of said experiments mo

arXiv:2610.27275v1 Announce Type: new Abstract: Maximum clique and maximum consensus are widely used formulations for robust point cloud registration in robotics. Maximum consensus seeks one transformation that aligns as many matches as possible, while maximum clique selects matches through pairwise distance tests. Maximum clique is often used as a substitute for or an approximation to maximum consensus, with empirical evidence suggesting a close relationship. However, how closely the two formulations agree is not fully understood. We study this relationship theoretically. We show that a maximum clique can have arbitrarily large alignment error and be arbitrarily larger than the maximum consensus set at any fixed positive tolerance, and derive geometric conditions that bound this error. When rotation is fixed, we derive sharp error bounds and tests that exactly characterize maximum consensus for the translation subproblem. Finally, we study group-$k$ consistency, which tests groups of