News & Research

arXiv:2507.04356v3 Announce Type: replace-cross Abstract: Research, innovation and practical capital investment have been increasing rapidly toward the realization of autonomous physical agents. This includes industrial and service robots, unmanned aerial vehicles, embedded control devices, and a number of other realizations of cybernetic/mechatronic implementations of intelligent autonomous devices. In this paper, we consider a stylized version of robotic care, which would normally involve a two-level Reinforcement Learning procedure that trains a policy for both lower level physical movement decisions as well as higher level conceptual tasks and their sub-components. In order to deliver greater safety and reliability in the system, we present the general formulation of this as a two-level optimization scheme which incorporates control at the lower level, and classical planning at the higher level, integrated with a capacity for learning. This synergistic integration of multiple metho

arXiv:2605.16673v2 Announce Type: replace Abstract: A "path-based sensor" produces a single observation along a continuous path. For example, a boolean path-based sensor returns a single "1" if an event of interest is detected at any point along the path and a "0" otherwise. Notably, a "1" provides no direct information about where along the path the event(s) may have occurred. Previous work has demonstrated that observations from multiple path-based sensors can be fused to create a Bayesian belief map over the spatial locations of the underlying event or phenomenon. Moreover, path planning can employ Shannon information theory to accelerate the rate of convergence of the belief map. In this paper, we present a new method to update the belief map based on a path-based sensor observation, and then plan paths to increase information gain. In contrast to prior work that approximates the posterior by averaging over the alternative event histories, we introduce a Bayesian Network (BN) formu

arXiv:2605.16537v2 Announce Type: replace Abstract: We present Nori A3, a 19-DoF bimanual mobile manipulator that ships assembled for $1,688. In a 45 x 45 cm footprint at 20.4 kg, a three-stage telescoping column carries the head and both arms from 69 cm to 145 cm in height, giving floor-to-counter reach; each 55 cm arm has 7+1 DoF and a 1.5 kg payload, alongside lidar navigation and four cameras. This is roughly a fifth of the parts cost of the cheapest comparable research platform. The capabilities usually lost at this price are recovered in software rather than by buying more expensive parts. A protection architecture eliminates the thermal burn-out of commodity serial-bus servos, attacking both the stall conditions that cause it and, through a two-tier interlock, the temperature rise itself. A sensorless grip-force channel recovers a continuous force estimate from the servo's own current register. A3 has been shipping to customers since July 2026; we give the design rationale and t

arXiv:2605.14232v2 Announce Type: replace Abstract: This paper addresses the motion control problem for mobile robots in obstacle-cluttered environments. The mobile robot has partial environment information only, and aims to move from an initial position to a target position without collisions. For this purpose, a reactive planning based control strategy (RPCS) is proposed. First, the initial and target positions are connected as a reference trajectory. Then, a reactive planning strategy (RPS) is developed to ensure the collision avoidance by modifying the reference trajectory locally based on the partial environment information. Next, an adaptive tracking control strategy (ATCS) is proposed to track the reference trajectory with potentially local modifications via the discretization techniques. Finally, the RPS and ATCS are combined to establish the RPCS, whose efficacy and advantages are illustrated by numerical examples.

arXiv:2605.00416v3 Announce Type: replace Abstract: Generalist robot policies increasingly benefit from large-scale pretraining, but offline data alone is insufficient for robust real-world deployment. Deployed robots encounter distribution shifts, long-tail failures, task variations, and human correction opportunities that fixed demonstration datasets cannot fully capture. We present Learning While Deploying (LWD), a fleet-scale offline-to-online reinforcement learning framework for continual post-training of generalist Vision-Language-Action (VLA) policies. Starting from a pretrained VLA policy, LWD closes the loop between deployment, shared physical experience, policy improvement, and redeployment by using autonomous rollouts and human interventions collected across a robot fleet. To stabilize learning from heterogeneous, sparse-reward fleet data, LWD combines Distributional Implicit Value Learning (DIVL) for robust value estimation with Q-learning via Adjoint Matching (QAM) for pol

arXiv:2604.15052v2 Announce Type: replace Abstract: Autonomous robots operating in natural karstic caves face perception and navigation challenges that are qualitatively distinct from those encountered in mines or tunnels: irregular geometry, reflective wet surfaces, near-zero ambient light, and complex branching passages. Yet publicly available datasets targeting this environment remain scarce and offer limited sensing modalities and environmental diversity. We present CAVERS, a multimodal dataset acquired in two structurally distinct rooms of Cueva de la Victoria, M\'alaga, Spain, containing 24 sequences with approximately 335 GB of recorded data. The sensor suite combines an Intel RealSense D435i RGB-D-I camera, an Optris PI640i near-IR thermal camera, and a Velodyne VLP-16 LiDAR, operated both handheld and mounted on a wheeled rover under full darkness and artificial illumination. For most of the sequences, mm-accurate 6-DoF ground truth pose and velocity at 120 Hz are provided by

arXiv:2603.26757v2 Announce Type: replace Abstract: Does multi-view demonstration truly improve robot manipulation, or merely enhance cross-view robustness? We present a systematic study quantifying the performance gains, scaling behavior, and underlying mechanisms of multi-view data for robot manipulation. Controlled experiments show that, under both fixed and randomized backgrounds, multi-view demonstrations consistently improve single-view policy success and generalization. Performance varies non-monotonically with view coverage, revealing effective regimes rather than a simple "more is better" trend. Notably, multi-view data breaks the scaling limitation of single-view datasets and continues to raise performance ceilings after saturation. Mechanistic analysis shows that multi-view learning promotes manipulation-relevant visual representations, better aligns the action head with the learned feature distribution, and reduces overfitting. Motivated by the importance of multi-view data

arXiv:2603.16673v5 Announce Type: replace Abstract: Embodied robotic systems increasingly rely on large language model (LLM)-based agents to support high-level reasoning, planning, and decision-making during interactions with the environment. However, invoking LLM reasoning introduces substantial computational latency and resource overhead, which can interrupt action execution and reduce system reliability. Excessive reasoning may delay actions, while insufficient reasoning often leads to incorrect decisions and task failures. This raises a fundamental question for embodied agents: when should the agent reason, and when should it act? In this work, we propose RARRL (Resource-Aware Reasoning via Reinforcement Learning), a hierarchical framework for resource-aware orchestration of embodied agents. Rather than learning low-level control policies, RARRL learns a high-level orchestration policy that operates at the agent's decision-making layer. This policy enables the agent to adaptively d

arXiv:2603.14554v2 Announce Type: replace Abstract: Generalizing learned locomotion policies across quadrupedal robots with different morphologies remains a challenge. Policies trained on a single robot often fail when deployed on embodiments with different mass distributions, kinematics, joint limits, or actuation constraints, forcing per-robot retraining. Prior works have approached this primarily either by scaling via training across real or generated embodiments or using large architectures producing transferable policies which are both storage and compute heavy. We argue that both of these approaches circumvent a key failure mode in actor-critic learning: a shared value function tends to average incompatible value targets across embodiments, yielding miscalibrated advantages and fixing that helps transfer and reduce the compute cost. We present MorFiC, a reinforcement learning approach for zero-shot cross-morphology locomotion which fixes this issue using multiplicative critic con

arXiv:2602.14255v2 Announce Type: replace Abstract: Industrial robots are increasingly deployed in construction and manufacturing tasks, where the deployment of end-to-end visuomotor policies is challenged by the observation-execution gap induced by observation, inference, and execution latencies. This gap is often significant on industrial robotic arms due to high-level control interfaces and slower closed-loop dynamics, making execution timing a dominant system-level concern. This paper presents a system-level, latency-aware framework for deploying and evaluating visuomotor policies on industrial robotic arms. The framework integrates latency-calibrated multimodal sensing, data synchronization, a unified communication pipeline, and a teleoperation interface for collecting expert demonstrations. Within this framework, we formalize a latency-aware execution strategy that assigns timestamps to policy-predicted action sequences and schedules only temporally feasible actions according to

arXiv:2602.13739v2 Announce Type: replace Abstract: Mobile robotic gas distribution mapping (GDM) provides critical situational awareness during emergency responses to hazardous gas releases. However, most systems still rely on teleoperation, limiting scalability and response speed. Autonomous active GDM is challenging in unknown and cluttered environments, because the robot must simultaneously explore traversable space, map the environment, and infer the gas distribution belief from sparse chemical measurements. We address this by formulating active GDM as a next-best-trajectory informative path planning (IPP) problem and propose XIT (Exploration and Exploitation Informed Trees), a sampling-based planner that balances exploration and exploitation by generating concurrent trajectories toward exploration-rich goals while collecting informative gas measurements en route. XIT draws a batch of samples from an Upper Confidence Bound (UCB) information field derived from the current gas poste

arXiv:2602.03002v2 Announce Type: replace Abstract: Humanoid perceptive locomotion has made significant progress and shows great promise, yet achieving robust multi-directional locomotion on complex terrains remains underexplored. To tackle this challenge, we propose RPL, a two-stage training framework that enables multi-directional locomotion on challenging terrains, and remains robust with payloads. RPL first trains terrain-specific expert policies with privileged height map observations to master decoupled locomotion and manipulation skills across different terrains, and then distills them into a transformer policy that leverages multiple depth cameras to cover a wide range of views. During distillation, we introduce two techniques to robustify multi-directional locomotion, depth feature scaling based on velocity commands and random side masking, which are critical for asymmetric depth observations and unseen widths of terrains. For scalable depth distillation, we develop an efficie

arXiv:2601.14492v3 Announce Type: replace Abstract: Robotic strawberry harvesting remains challenging under partial occlusion, where leaves obscure fruit geometry and make grasp decisions based on a single shape estimate unreliable. A partial observation may admit multiple plausible 3D completions, so a grasp feasible on one completion may fail on another. Existing uncertainty-aware grasping methods estimate uncertainty in pose, shape, or individual candidates, but do not aggregate grasp feasibility across completion hypotheses for an object-level attempt-or-abstain decision. We present UNCLE-Grasp, a task-adapted framework integrating learned shape completion, dropout-based variability estimation, physically grounded grasp evaluation, and risk-aware target-level decisions for leaf-occluded strawberries. Monte Carlo dropout generates multiple completion samples. For each sample, retained grasp candidates are combined into a wrench space to compute a completion-level force-closure score

arXiv:2512.01946v4 Announce Type: replace Abstract: Robust robotic manipulation requires reliable failure detection and recovery. Although recent Vision-Language Models (VLMs) show promise in robot failure detection, their generalization is severely limited by the scarcity and narrow coverage of failure data. To address this bottleneck, we propose an automatic framework for generating diverse robotic planning and execution failures across both simulated and real-world environments. Our approach perturbs successful manipulation trajectories to synthesize failures that reflect realistic failure distributions, and leverages VLMs to produce structured step-by-step reasoning traces. This yields GuardianFail-36k, a large-scale failure reasoning dataset built upon the RLBench simulator and the BridgeDataV2 real-robot dataset. Using GuardianFail-36k, we train Guardian, a multi-view reasoning VLM for unified planning and execution verification. Guardian achieves state-of-the-art performance on

arXiv:2511.17774v4 Announce Type: replace Abstract: Fabrication uncertainty arising from tolerance accumulation, material imperfection, and positioning errors remains a critical barrier to automated robotic assembly in construction, particularly for contact-rich manipulation tasks under minimal geometric clearance. This paper investigates the deployment of diffusion policy learning on construction-scale industrial robots to enable robust, high-precision assembly under such uncertainty, using tight-clearance mortise and tenon timber joinery as a representative case study. Sensory-motor diffusion policies are trained using teleoperated demonstrations collected from an industrial robotic workcell equipped with force/torque sensing. A two-phase experimental study evaluates baseline performance and robustness under randomized positional perturbations up to 10 mm, far exceeding the joint clearance. The best-performing policy achieved 100% success under nominal conditions and 75% average succ