Noticias e Investigación

arXiv:2610.30528v1 Announce Type: new Abstract: Predicting surrounding vehicles' lane-change intentions enables autonomous vehicles to anticipate potential hazards and respond proactively rather than relying solely on reactive maneuvers. This study investigates the benefits of integrating lane-change prediction, based on knowledge graph embeddings and Bayesian inference, into autonomous vehicle decision-making. Three driving strategies are evaluated in risky merging scenarios simulated in CARLA: reactive driving, prediction-based longitudinal action, and prediction-based lateral action. The proposed prediction model anticipates the surrounding vehicle's lane change approximately 3-4 seconds before its occurrence, allowing the ego vehicle to adjust its behavior accordingly. In the representative simulation records, reactive driving results in a collision accompanied by aggressive deceleration. In contrast, prediction-based longitudinal action initiates early braking to establish a safe

arXiv:2610.30452v1 Announce Type: new Abstract: Currently, CT-guided lung biopsy is a highly manual procedure requiring patient breath-holding, iterative manual needle insertions, and repeated CT confirmation scans to accurately target a tumor. Automating this process within the CT bore--without relying on manual needle placement or patient breath-holding--has the potential to reduce procedure time, patient discomfort, and radiation exposure. In this work, we present an integrated sensing and control system capable of performing a gated needle insertion within the CT bore. The experiments demonstrate the system's ability to track and predict patient breath cycles, perform autonomous respiratory-gated needle insertion, and detect breath cycle anomalies.

arXiv:2610.30438v1 Announce Type: new Abstract: Tactile sensing provides robots with direct access to physical interactions at the contact interface, making it a critical modality for robust and dexterous manipulation. Over the past several decades, tactile-based manipulation research has evolved from mechanics-driven approaches, which explicitly interpret tactile signals through contact mechanics and task-specific reasoning, to data-driven approaches that learn tactile representations and manipulation policies directly from data. This paper reviews the development of tactile-based manipulation from these two complementary perspectives. We first introduce the fundamentals of tactile sensing, and then review, from a mechanics-driven perspective, how those signals form manipulation-relevant state representations and subsequently influence the robot action. We then examine the recent advances in data-driven tactile manipulation, covering imitation learning, reinforcement learning, vision-

arXiv:2610.30334v1 Announce Type: new Abstract: Designing and tuning reward functions remains one of the main challenges in building effective reinforcement learning (RL) pipelines for humanoid locomotion. While shaping rewards can accelerate learning, naively formulated terms often conflict with the desired behavior, overfit to a specific robot, or become unstable under small weight changes. Potential-Based Reward Shaping (PBRS) offers an alternative solution: shaping the reward via differences of a potential function to guide exploration without changing the optimal policy. In this paper, we study Multi-PBRS for humanoid locomotion and propose a unified reward configuration. We then evaluate the same reward structure and weights across nine humanoid robots, requiring only robot-specific metadata (joint/link mappings and default poses) rather than per-robot reward redesign. Our experiments show that, compared to standard shaping, MPBRS provides more consistent scaling behavior and red

arXiv:2610.30320v1 Announce Type: new Abstract: World Action Models (WAMs) have emerged as a promising approach to learning generalist robot policies using rich spatiotemporal priors from pre-trained video diffusion models. A recent line of WAMs improves inference efficiency by conditioning a separate action decoder on intermediate future video representations extracted from a single denoising step, avoiding costly iterative denoising required for video generation. This design makes action prediction depend directly on the quality of these future representations. In this paper, we introduce Future REpresentation learning via self-Distillation with temporal context (FRED), a framework that trains WAMs to improve the quality of future video representations from noise in a single denoising step. Specifically, FRED uses self-distillation to predict future representations from noise, using representations extracted from ground-truth future observations as targets. We further condition the v

arXiv:2610.30197v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into embodied robotic planning, yet standard benchmarks primarily evaluate end-to-end task completion in nominal environments, obscuring failure origins across the execution stack. We present Alpha, a systematic fault-injection and robustness-evaluation framework for LLM-driven manipulation in simulation. Alpha injects calibrated faults across perception (sensor lag), actuation (grip slip), kinematics (unreachable targets), and planning (planner-output corruption), alongside concurrent conditions. To separate physical vulnerabilities from planner sensitivities, we introduce a matched methodology pairing an LLM agent (Llama-3 via Ollama) against a deterministic Scripted Oracle planner across a balanced 150-episode matrix under identical seeds, geometries, and safety criteria. Across the matrix, the Oracle achieves 64.0% (48/75) success while the LLM achieves 44.0% (33/75) (paired McN

arXiv:2605.21723v2 Announce Type: replace Abstract: This paper studies heterogeneous multi-team collaboration through dynamic robot allocation, where the robots themselves are treated as transferable resources. Leveraging Hamilton's rule from ecology as an altruistic decision-making mechanism, we propose a multi-team collaborative resource allocation framework with heterogeneous capabilities, transfer costs, and capability-dependent contributions. The resulting general heterogeneous resource assignment problem is combinatorial and is shown to be NP-hard. To address scalability, we develop a graph neural network policy under centralized training and decentralized execution that approximates the altruistic allocations based on Hamilton's rule. The model operates over the team interaction graph and predicts robot-level transfer decisions and subsequent robot-to-team assignments. The proposed approach is evaluated in a fire-fighting scenario against exact optimization and multi-step simula

arXiv:2605.15705v2 Announce Type: replace Abstract: World models aim to improve robotic decision making by predicting the consequences of actions. However, in practice, their predictions often become unreliable once the robot encounters states outside the training distribution, limiting their effectiveness at deployment. We observe that execution itself provides a natural but underutilized signal: after each action, the robot directly observes the true next state, revealing the mismatch between predicted and actual outcomes. Building on this insight, we propose feedback world model, an inference-time mechanism that closes the loop between prediction and observation at inference time. Instead of treating the world model as a static open-loop predictor, our method maintains a lightweight feedback state that is updated online to iteratively correct future predictions, compensating for model errors using real-time observations without additional training data or parameter updates. We show

arXiv:2604.10809v2 Announce Type: replace Abstract: Recent advancements in learning from human demonstration have shown promising results in addressing the scalability and high cost of data collection required to train robust visuomotor policies. However, existing approaches are often constrained by a reliance on multiview camera setups, depth sensors, or custom hardware, and are typically limited to policy execution from third-person or egocentric cameras. In this paper, we present WARPED, a framework designed to synthesize realistic wrist-view observations and actions from human demonstration videos to facilitate the training of visuomotor policies using only monocular RGB data. With data collected from an egocentric RGB camera, our system leverages vision foundation models to initialize the interactive scene. A hand-object interaction pipeline is then employed to track the hand and manipulated object and retarget the trajectories to a robotic end-effector. Lastly, photorealistic wri

arXiv:2602.11321v4 Announce Type: replace Abstract: Building a low-latency humanoid teleoperation system is essential for collecting diverse reactive and dynamic demonstrations. However, end-to-end latencies in existing approaches consistently hover around 200ms, roughly equal to human reaction time, that severely limits responsiveness and prevents tasks requiring rapid feedback. We identify position-only PD control as the root cause of this latency floor and propose ExtremControl, a low-latency whole-body control framework that: (1) incorporates scaled velocity feedforward in low-level control, reducing tracking delay by approximately 70ms with a provably optimal feedforward ratio; (2) operates directly on SE(3) poses of selected rigid links, primarily humanoid extremities, to avoid joint-space retargeting in the control loop. To minimize retargeting time, we derive a closed-form Cartesian-space mapping that uses a single anthropomorphic calibration step to translate human motion into

arXiv:2602.01860v2 Announce Type: replace Abstract: Fast flights with aggressive maneuvers in cluttered GNSS-denied environments require fast, reliable, and accurate UAV state estimation. In this paper, we present an approach for onboard state estimation of a high-speed UAV using a monocular RGB camera and an IMU. Our approach fuses data from Visual-Inertial Odometry (VIO), an onboard landmark-based camera measurement system, and an IMU to produce an accurate state estimate. Using onboard measurement data, we estimate and compensate for VIO drift through a novel mathematical drift model. State-of-the-art approaches often rely on more complex hardware (e.g., stereo cameras or rangefinders) and use uncorrected drifting VIO velocities, orientation, and angular rates, leading to errors during fast maneuvers. In contrast, our method corrects all VIO states (position, orientation, linear and angular velocity), resulting in accurate state estimation even during rapid and dynamic motion. Our a

arXiv:2510.04161v2 Announce Type: replace Abstract: This paper considers the path planning problem for autonomous exploration of an unknown environment using multiple heterogeneous robots such as drones, wheeled, and legged robots, with different capabilities to traverse complex terrains. A key challenge there is to intelligently allocate the robots to the unknown areas to be explored and determine the visiting order of those areas subject to traversability constraints, which leads to a large-scale constrained multiple hamiltonian path problem (mHPP) that needs to be quickly and iteratively solved every time when new space is explored. To address the hallenge, we propose HEHA (Hierarchical Exploration with Heterogeneous Agents) using a recent hierarchical method that decomposes the exploration into global planning and local planning. The major contribution in HEHA is its global planning, where we propose a new routing algorithm RCH (Routing with Constraints and Heterogeneity) that can

arXiv:2510.01433v3 Announce Type: replace Abstract: Vision-based manipulation policies are commonly learned from rich visual observations, typically using dense visual features extracted from full resolution images. It remains unclear how much of this visual information is really needed for effective policy learning. In this paper, we investigate the opposite end of the spectrum: how effective can manipulation policies be if they use as input only a compact visual representation. Sparse 2D keypoints provide a deliberately compact representation, giving the policy spatial information while removing direct access to appearance, texture, and background. We design AFFORD2ACT (Affordance-Guided Keypoints to Act) to study how much manipulation capability can be retained under this representation constraint. An off-the-shelf language-conditioned affordance model identifies the task-relevant region, from which up to 15 object keypoints are selected, together with 4 end-effector keypoints. The

arXiv:2508.07406v2 Announce Type: replace Abstract: Agricultural robots are serving as powerful members across a wide range of tasks, nevertheless, they are still heavily relying on manual operations or railway systems for movement. In contrast, Vision-and-Language Navigation (VLN) enables navigating robots from starting points to target positions following natural language instructions, having shown strong performances across various domains. However, none of the existing benchmarks or methods are specifically designed for agricultural robots. To bridge this gap, we build our A2A benchmark, which consists of 1,560 episodes across 6 classes of agricultural scenes using the step-by-step format for instructions and the continuous environment for actions. We evaluate several existing state-of-the-art VLN methods on A2A, then we observe that they perform well on short instructions while perform poorly on long instructions. To alleviate this phenomenon, we design our STL module, which decom

arXiv:2503.06669v5 Announce Type: replace Abstract: We explore how scalable robot data can address real-world challenges for generalized robotic manipulation. Introducing AgiBot World, a large-scale platform comprising over 1 million trajectories across 217 tasks in five deployment scenarios, we achieve an order-of-magnitude increase in data scale compared to existing datasets. Accelerated by a standardized collection pipeline with human-in-the-loop verification, AgiBot World guarantees high-quality and diverse data distribution. It is extensible from grippers to dexterous hands and visuo-tactile sensors for fine-grained skill acquisition. Building on top of data, we introduce Genie Operator-1 (GO-1), a novel generalist policy that leverages latent action representations to maximize data utilization, demonstrating predictable performance scaling with increased data volume. Policies pre-trained on our dataset achieve an average performance improvement of 30% over those trained on Open X