Noticias e Investigación

arXiv:2611.03103v1 Announce Type: new Abstract: Vision-language-action (VLA) models offer a promising path toward general-purpose robot manipulation. While inference latency can affect task success in a constantly evolving physical world, the relationship between latency and task success rate remains poorly understood. In this paper, we introduce VLALagBench, a comprehensive benchmark for studying VLA inference latency sensitivity across task types, object speeds, execution modes (synchronous and asynchronous), and VLA policies. Using VLALagBench, we find that even a modest inference latency of 100 ms can nearly halve task success on moving targets, with sensitivity varying substantially across tasks and policies. These differences reflect task-specific requirements for precision and continuous motion. By overlapping inference with action execution, asynchronous execution can recover some of the lost success at the cost of more frequent inference requests. Building on these findings, w

arXiv:2611.03091v1 Announce Type: new Abstract: KlaskTron is an open-hardware physical platform for two-player adversarial reinforcement learning, with a learned actuator model that supports zero-shot sim-to-real transfer of trained policies. Building on this platform, we contribute KlaskTron-IL: a multi-tier imitation learning dataset (80k human demonstration timesteps collected directly on the physical robot, 320k symmetry-augmented timesteps, and synthetic expert trajectories up to 5M steps generated by the zero-shot-validated PPO policy), and diagnostic baselines that expose three characteristic failure modes of standard IL methods under contact-rich adversarial dynamics: BC's data scaling plateau, GAIL's interpretable but weak-gradient discriminator (Spearman $\rho = -1.0$ peg-ball proximity recovery, but a logit span under 0.1), and an asymmetric BC-prior x reward-density interaction. Because the policy interface used throughout this paper is the same one the actuator model bridg

arXiv:2611.03059v1 Announce Type: new Abstract: Contact-based robotic manipulation is constrained by end-effector reachability and the mechanical properties of objects. Airflow can exert forces without direct robot-object contact, enabling manipulation of objects beyond the reach or when direct contact is undesirable. However, the lack of suitable simulation has hindered the application of reinforcement learning to airflow-based manipulation, limiting the broader adoption of this mechanism compared with contact-based methods. To address this gap, we developed a robot- and task-agnostic airflow extension for Isaac Lab that supports massively parallel training. The extension uses experimentally identified airflow models to compute the forces and torques exerted on objects. Based on this extension, we train a hybrid push-blow (HPB) policy that combines contact-based pushing with airflow manipulation and a rectangular reorientation (RR) policy that controls a cuboid's heading using airflow

arXiv:2611.03016v1 Announce Type: new Abstract: This paper presents a decentralized guiding vector field framework for rigid formation path following in multi- UAV systems based on the superposition of simple rule-driven vector fields. Each control objective is encoded as a dedicated vector-field component: a normalized tangential term to ensure progression along a prescribed geometric path at a desired speed, a convergence term driving the formation toward the path, a local coordination term that preserves the desired formation shape through a pseudo-path parameter, and a local separation term for inter-agent collision avoidance. The coordination mechanism requires the exchange of only a single scalar per agent, resulting in low communication bandwidth. Partial theoretical results establish convergence of the swarm to the desired path and formation in the absence of the sepa- ration term, while collision avoidance is formally guaranteed. The approach is experimentally validated using

arXiv:2611.02999v1 Announce Type: new Abstract: Markerless hand-eye calibration under continuous motion requires jointly resolving camera geometry and the time offset between images and joint states. We present SyncHeC (Synchronized Hand-Eye Calibration), a dense differentiable-rendering framework that estimates both from a single robot trajectory. Continuous-time forward kinematics makes latency differentiable, metric depth complements mask contours, and an offline planner combines spatial sensitivity with temporal excitation. Alternating updates support Eye-to-Hand, Eye-in-Hand, and multi-camera calibration. On a 7-DoF arm, one $6.5$\,s motion yields $0.21$\,cm translation error against a marker-board reference, $0.34$\,cm touch-test error, and $2.7$\,ms latency error relative to an $87$\,ms reference with $95\%$ uncertainty of $\pm1.5$\,ms. Against CM-Calib on matched recordings, translation and nominal latency errors improve by $3.7\times$ and $2.3\times$. Experiments span seven ar

arXiv:2611.02992v1 Announce Type: new Abstract: Autonomous robots are gaining traction in off-road environments, driven by their rising demand in agriculture, defense, and search-and-rescue operations. However, these environments are inherently unpredictable and navigation remains a formidable challenge given the complex environmental conditions including challenging terrain and varying illumination. Off-road autonomy requires robust perception across multiple spatial scales and imaging modalities, yet most sensing setups struggle to capture both far-field environmental context and near-field terrain detail simultaneously across various illumination conditions. To facilitate development of robust off-road perception and traversability estimation systems, we propose MS-DOTT (Multi-Sensor Dataset for Off-road Terrain Traversability) a comprehensive multi-modal off-road dataset with long-range 3D sensing and short-range, fine-grained perception to facilitate high-fidelity terrain understa

arXiv:2611.02968v1 Announce Type: new Abstract: Contact transitions allow humanoid robots to redistribute object support across the whole body, enabling manipulation beyond hand-only grasps. Human videos provide a scalable source of such transitions for robots to learn from. However, body occlusion disrupts object pose tracking and corrupts retargeted motion. Moreover, accurately reproducing the object trajectory does not ensure that the robot applies stabilizing forces at the intended contacts, risking slips or drops in later phases. To address these challenges, we propose ReContact, which reconstructs and retargets human-object interactions through an object-centric contact plan. The plan specifies contact phases, contact pairs, anchors, pressing directions, and sticking or sliding modes. Contact priors stabilize object pose reconstruction through occlusion. A residual policy over a frozen whole-body tracker is rewarded for making contact at planned anchors with forces aligned to the

arXiv:2611.02947v1 Announce Type: new Abstract: Collision-free reaching is central to manipulation, yet real cells often leave the size and exact position of an obstacle unobserved, which excludes reinforcement learning controllers that take obstacle geometry as input. A reward limited to a collision penalty does not depend on the clearance outside the obstacle, so under a step cost its learned policy skims the obstacle surface and fails once the obstacle radius exceeds its training value. We investigate whether a single analytic reward term can induce a safety margin without exposing the obstacle to the policy. We augment the reward of a simulated Franka Emika Panda reaching task with a horizontal artificial potential field that penalizes proximity within a safety margin around each of two pillars (fixed nominal layout, random drift). Under hidden observation, policies trained with this term maintain a safe success rate above 88% across a radius sweep from 0.03 m to 0.055 m (30 seeds)

arXiv:2611.02937v1 Announce Type: new Abstract: Social navigation is essential for autonomous service robots operating in human environments, but progress remains constrained by the lack of extensive real-world benchmarks. This paper presents the Real Social Navigation Testbed (RESONATE), a reproducible benchmark for human-aware navigation based on a unified robotic platform, a shared ROS 2 software stack, precise trajectory ground truth, and structured social interaction scenarios. We evaluate eight controller configurations, combining classical and predictive local planners with different social modeling strategies, over eight representative scenarios with five repetitions each, yielding 320 physical experiments. The benchmark combines quantitative navigation and social metrics with participant-based qualitative assessments to jointly measure efficiency, safety, comfort, and perceived social behavior. Results reveal scenario-dependent trade-offs among the tested methods. The algorith

arXiv:2611.02930v1 Announce Type: new Abstract: Generalization remains a central challenge in robotics, calling for policies that address domain variations from multiple dimensions such as diverse embodiments, camera poses, and/or scene layouts. In order to approach this problem, we introduce PointPilot, a task-focused and 3D aligned manipulation framework built on a 3D space point-flow prediction. Rather than modeling the global scene, PointPilot only represents task-focused objects (robot grippers treated as objects), avoiding the prediction of potentially redundant scene parts unrelated to the manipulation tasks. Moreover, PointPilot aligns 3D spatial representation in one unified coordinate and supports closed-loop and fully automatic action execution across different manipulation modes from object point flow without a separately trained action expert, while existing works typically need an extra action head or are (semi-)automatic and single-arm mode specific. Given sensor observa

arXiv:2611.02884v1 Announce Type: new Abstract: Recent robot systems combine wrist and fixed cameras, requiring camera-to-robot transforms for both. Most hand-eye calibration methods rely on targets or robot-body observations. Scene-based methods using 3D reconstruction require neither but address only wrist cameras. We introduce AnchorHEC, a targetless hand-eye calibration framework for wrist and fixed cameras in a single procedure using RGB images, forward kinematics (FK), and known camera intrinsics. We initialize both camera types using FK and a shared 3D foundation model reconstruction. FK-constrained wrist-image bundle adjustment reduces the impact of reconstructed pose and scale errors. If this refinement fails or retains too few inliers, we relax the constraints on potentially inaccurate FK poses and lens parameters. We use the refined wrist transform and FK to triangulate metric points and estimate fixed-camera poses. Across three backbones, calibration errors in simulation de

arXiv:2611.02871v1 Announce Type: new Abstract: Humanoid loco-manipulation requires coordinating object acquisition, transport, and release from onboard sensing while responding to user commands and incomplete visual observations. We present ReGATE, a whole-body controller that combines egocentric depth, proprioception, and online locomotion and contact commands. Built on a teacher-student framework, ReGATE integrates interaction-state estimation through a recurrent adapter that predicts object geometry and physical contact without an external object-pose estimator. Head-wise and global gates regulate visual updates to a proprioceptive pathway. A visual-grounding and corruption curriculum trains the adapter, while phase-aware initialization increases coverage of brief contact transitions. In simulation, ReGATE achieves 85.2% success across 330 static-object acquisition trials, compared with 48.5% for VisualMimic under the same protocol, and 85.9% across 440 moving-object acquisition tr

arXiv:2611.02794v1 Announce Type: new Abstract: Robust multimodal perception for soft robotic grasping is challenged by modality-dependent noise, semantic ambiguity, and limited coordinated multisensory data. We introduce Soma, a dataset containing 100 objects, 1,000 continuous grasping trajectories, and 74,914 multimodal samples grouped by software acquisition cycle, spanning remote RGB, hand RGB, depth, tactile, IMU, and structured text observations. We also collect Soma-Success, an independent grasp-outcome dataset with 1,000 trials (998 retained after quality control), to evaluate frozen-encoder transfer for post-hoc grasp outcome classification and early grasp success prediction. Built on Soma, SomaAlign learns multimodal representations in three stages: masked reconstruction estimates relative modality reliability; reliability-aware contrastive alignment uses pair-specific adaptive temperatures to adjust alignment sharpness; and conflict-aware semantic constraints improve intra-s

arXiv:2611.02777v1 Announce Type: new Abstract: Vision-language-action (VLA) policies can handle noncritical stages of long-horizon manipulation yet fail during short precision-critical phases. Lightweight reinforcement learning (RL) specialists can address these failures without directly applying RL to the full VLA, but retain auxiliary controllers and switching mechanisms at deployment. We study how to transfer their capabilities into a single flow-matching VLA. Direct imitation of specialist rollouts can reproduce action jitter and leave the VLA unprepared for states encountered during its own execution. We introduce REPAIR, a value-guided distillation framework that combines offline initialization with on-policy refinement. At each critical-phase observation, REPAIR queries the corresponding specialist for multiple action candidates and weights them using its critic to construct a supervision distribution. A shared weighted flow-matching objective trains the VLA first on observatio

arXiv:2611.02699v1 Announce Type: new Abstract: We reimplement the dynamics model of the Robotic World Model (RWM) and test the uncertainty estimate that its follow-up, RWM-U, uses to penalize reward in offline policy optimization. Our implementation matches the released model's gradients. On held-out data, training on the model's own rollouts beats one-step training at the preregistered long horizon for all ten episodes, but loses on short one-step windows. RWM-U's ensemble disagreement ranks rollout errors better than forecast step (Spearman rho = 0.90 at 100 steps), yet its spread substantially understates error: one-sigma coverage on the released checkpoint is 4.6% at 100 steps, versus 68% for a calibrated Gaussian. Rolling ensemble members on their own predictions raises coverage to 33%. The implemented squared-error loss on sampled predictions drives each member's variance toward zero. A horizon-specific rescaling restores one-sigma coverage under leave-one-episode-out evaluation