Noticias e Investigación

arXiv:2609.27342v2 Announce Type: replace Abstract: Cutting changes both the shape and topology of deformable objects, making accurate simulation challenging for robotic manipulation. A simulator must track the cutting tool as a cut develops, preserve the resulting discontinuities after tool withdrawal, and enable newly exposed surfaces to interact with the tool and with each other. Existing formulations often prescribe cut surfaces in advance or couple material separation to auxiliary geometric fields. We introduce BladeMaster, a GPU-accelerated cutting framework based on the total Lagrangian material point method (TLMPM). Our key idea is to encode the cutting history directly on material points through persistent side labels generated online from the blade geometry. These labels govern particle-grid coupling, preserving connectivity within intact material while preventing spurious coupling across cut faces after tool withdrawal. Our formulation supports progressive and intersecting c

arXiv:2609.27154v2 Announce Type: replace Abstract: In human-robot interaction, traditional interfaces such as joysticks and handheld tablets introduce latency into navigation tasks and require the operator's explicit attention on the device, instead of the robot. We propose a new human-robot interaction framework in which a human communicates intent directly through sparse multimodal signals such as physical pushes and spoken commands. Human intent is represented as a parameterized linear dynamical system (LDS) that encodes the desired goal and motion behavior. The robot estimates this intent (parameters) online using a particle filter, where each particle represents a candidate LDS hypothesis and is reweighted online as new information becomes available. We validate this framework on a robotic blimp, whose inherent compliance and collision tolerance make it well-suited for repeated physical interaction. Experiments with multiple participants across 300 trials show that combining push

arXiv:2609.27145v2 Announce Type: replace Abstract: Robot navigation methods tend to avoid contact, and consequently search for collision-free trajectories. For robots with high inertia and limited maneuverability, however, avoiding contact can require substantial steering effort and time, even when interactions with surrounding surfaces could be safely exploited. In this paper, we develop a planning method that deliberately uses controlled wall reflections to generate trajectories that can be easier and more efficient to execute than purely collision-free motion. We consider planar navigation in environments where a mobile robot is permitted to bounce off surrounding surfaces. To represent the resulting alternatives, we construct a reflection-augmented state graph in which paths are partitioned into distinct classes according to the sequence of walls used for reflection. This representation enables systematic enumeration of reflection strategies and identification of the lowest-cost p

arXiv:2605.21257v2 Announce Type: replace Abstract: Planning through crowded environments under uncertain obstacle motions remains difficult, as stochastic interactions often induce overly conservative behavior or reduced efficiency. To address this challenge, we propose an end-to-end risk adaptation framework for crowd navigation under obstacle-motion uncertainty modeled by a Gaussian mixture model. The framework combines reinforcement learning~(RL) with a differentiable quadratic-program safety layer based on Conditional Value-at-Risk~(CVaR) barrier functions, jointly learning nominal control input, risk level, and safety margin and enforcing explicit probabilistic safety constraints. This design enables context-aware adaptation, promoting efficient behavior while invoking caution only when necessary. We conduct extensive evaluations in dynamic, uncertain, and crowded environments across varying obstacle densities and robot models, and further assess generalization under three out-of

arXiv:2605.19600v2 Announce Type: replace Abstract: In the field of Vision-Language Navigation (VLN), aerial datasets remain limited in their ability to combine scale, diversity, and realism, often relying on either costly real-world scenes or visually limited simulations. To address these challenges, we introduce FlyMirage, a highly scalable and fully automated data generation pipeline for aerial VLN. Our approach leverages large language models (LLM) as an environment designer to promote scene diversity, paired with a generative world model that instantiates these designs into high-fidelity 3D Gaussian Splatting (3DGS) scenes. To substantially reduce human labor and ensure the feasibility of flight data, FlyMirage automates scene exploration and semantic information acquisition, and further integrates a dynamically feasible planner for uncrewed aerial vehicle (UAV) trajectory generation. Utilizing this toolchain, we generate a large-scale, diverse, and photorealistic aerial VLN datas

arXiv:2605.08774v2 Announce Type: replace Abstract: Long-horizon robotic manipulation requires dense feedback that reflects how a task advances through its procedural stages, not merely whether the final outcome is successful. Existing reward models often rely on trajectory-level success labels or time-based interpolation, which can conflate elapsed time with true task progress and therefore fail to capture unfinished steps, stagnation, and failure states. We present ProcVLM, a progress-aware vision-language model that learns procedure-grounded progress as a dense reward signal for manipulation. Rather than deriving progress from terminal outcomes or temporal proxies, ProcVLM grounds progress estimation in procedural structure and intra-stage visual change, and further adopts a reasoning-before-estimation paradigm that infers the remaining atomic actions before estimating task progress. Specifically, we construct this supervision by synthesizing frame-level subtask-semantic annotations

arXiv:2605.06222v3 Announce Type: replace Abstract: World Action Models (WAMs) jointly predict future visual observations and actions, but typically execute a fixed number of actions before replanning, regardless of whether the imagined future remains consistent with reality. We formulate adaptive WAM execution as future--reality verification and propose Future Forward Dynamics Causal Attention (FFDC), a lightweight verifier that reasons over predicted actions, predicted visual dynamics, real observations, and instruction-conditioned semantics. FFDC allows continued execution when a plan remains reliable and triggers replanning when its validity deteriorates. We combine verification with Mixture-of-Horizon Training to improve long-horizon trajectory coverage. On RoboTwin, FFDC-WAM achieves the highest average success rate among the evaluated methods in both clean and randomized settings. Under randomization, it improves success by 2.54 percentage points over Base-Motus while reducing W

arXiv:2605.03821v2 Announce Type: replace Abstract: Existing robot video world models are typically trained with low-level objectives such as reconstruction and perceptual similarity, which are poorly aligned with the capabilities that matter most for robot decision making, including instruction following, manipulation success, and physical plausibility. They also suffer from error accumulation in long-horizon autoregressive prediction. We present RoboAlign-R1, a framework that combines reward-aligned post-training with stabilized long-horizon inference for robot video world models. We construct RobotWorldBench, a benchmark of 10,000 annotated video-instruction pairs collected from four robot data sources, and train a multimodal teacher judge, RoboAlign-Judge, to provide fine-grained six-dimensional evaluation of generated videos. We then distill the teacher into a lightweight student reward model for efficient reinforcement-learning-based post-training. To reduce long-horizon rollout

arXiv:2604.21894v2 Announce Type: replace Abstract: The design of multi-agent robotic systems involves tightly coupled decisions spanning heterogeneous domains, including robot design, fleet composition, and planning. Much effort has been devoted to isolated improvements in these domains, while system-level co-design considering trade-offs and task requirements remains underexplored. In this work, we present a formal and compositional framework for the task-driven co-design of heterogeneous multi-robot systems, and propose robotic phase diagrams as a qualitative design guideline. Building on monotone co-design theory, we introduce general abstractions of robots, fleets, planners, executors, and evaluators as interconnected design problems with well-defined interfaces that are agnostic to both implementations and tasks. This structure enables efficient joint optimization of robot design, fleet composition, and planning under task-specific performance constraints. A series of case studie

arXiv:2604.12879v2 Announce Type: replace Abstract: Fast dexterous grasping while a mobile base remains in motion requires coordinated whole-body control and rapid adaptation to physical contact. We propose FastGrasp, a two-stage learning framework that integrates grasp guidance, whole-body control, and tactile feedback. First, a pretrained conditional variational autoencoder generates diverse grasp candidates from object point clouds. Candidates are filtered by approach direction and supporting-surface constraints, then ranked using an envelopment-based criterion combining grasp width and depth coverage measures. Second, a reinforcement learning policy jointly controls the mobile base, arm, and dexterous hand, using the selected grasp as guidance and tactile observations for online grasp adjustment. The policy is trained with domain randomization and deployed with command filtering and tactile-triggered grasp tightening. Simulation experiments show higher grasp success rates than the

arXiv:2603.12099v2 Announce Type: replace Abstract: The da Vinci Research Kit (dVRK) has been widely used for research in robot-assisted surgery over the past decade, and its latest generation, the dVRK-Si was made publicly available in 2025. This manuscript presents the first complete kinematic and dynamic modeling framework for the dVRK-Si Patient-Side Manipulator (PSM), whose redesigned mechanism differs substantially from that of the first-generation dVRK and therefore requires a new formulation to accurately capture gravitational effects and improve overall control performance. We first develop a modified DH kinematic model that represents the dVRK-Si PSM's redesigned mechanism. The equations of motion are then derived using the Euler-Lagrange formulation, and the inverse dynamics are expressed in a linear-in-parameters regressor form. The dynamic parameters are identified from data collected along a periodic excitation trajectory optimized for numerical conditioning and estimated

arXiv:2603.02772v2 Announce Type: replace Abstract: Motion planners tuned under nominal conditions can fail when execution conditions change. Existing differentiable adaptation methods provide efficient online updates but remain constrained by static loss functions and gradient-only parameter updates. We propose Self-Evolutionary RePlanning (SERP), a failure-aware framework that jointly adapts motion-planner parameters and loss function weights from closed-loop execution feedback. SERP provides two adaptation mechanisms. First, in-context learning with auto-differentiation (ILAD) enables rapid online adaptation by coupling local numerical refinement with history-conditioned semantic intervention. Auto-differentiation performs numerical updates, while a vision-language model (VLM) interprets recent adaptation history to jointly revise planner parameters and loss weights when local adaptation becomes ineffective. Second, the memory-augmented rollout system (MARS) extends ILAD into a deli

arXiv:2601.22988v2 Announce Type: replace Abstract: Real-world robotic manipulation requires visuomotor policies capable of robust 3D scene reasoning under varying camera viewpoints. While recent 3D-aware manipulation policies have shown promise, they still face several limitations: (i) reliance on multi-view observations during inference, which is impractical in camera-constrained deployments; (ii) insufficient geometric fidelity in learned representations, limiting precise and viewpoint-robust control; and (iii) the lack of effective mechanisms for transferring pretrained 3D representations to downstream visuomotor policies. To address these challenges, we present GEM3D (Geometrically-Grounded 3D Manipulation), a unified 3D representation and policy learning framework for view-generalizable robotic manipulation. GEM3D learns amodal 3D representations from single-view RGB-D observations by jointly enforcing point-cloud reconstruction and Gaussian-splatting-based novel-view consistency

arXiv:2601.21667v3 Announce Type: replace Abstract: Current mobile manipulation research predominantly follows an instruction-driven paradigm, where robots rely on predefined textual commands to execute tasks. However, this setting confines robots to a passive role, limiting robotic autonomy and the ability to react to dynamic environmental events. To address these limitations, we introduce Sound-Triggered Mobile Manipulation (STMM), where robots must actively perceive and interact with sound-emitting objects without explicit action instructions. To support STMM, we develop Habitat-Echo, a simulation platform that integrates sound rendering with physical interaction. We further propose a hierarchical baseline that translates high-level planning into low-level executions, where a task planner predicts a skill chain for policy models to execute sequentially. Experiments indicate the feasibility of perceiving auditory events and executing corresponding physical interactions without explic

arXiv:2601.13177v2 Announce Type: replace Abstract: Spinal cord stimulation (SCS) is widely used for pain management and has recently shown promise in promoting functional recovery following spinal cord injury. Effective activation of motor neurons requires accurate placement of SCS leads in the ventral or lateral epidural space, where corticospinal and rubrospinal motor fibers are located, a task that remains challenging with conventional manual steering. This study presents a static modeling framework for ExoNav, a steerable robotic tool designed to enable precise navigation to ventral and lateral epidural targets. A Cosserat rod formulation is employed to relate tendon actuation forces to the robot shape, and a simulation environment is developed based on this model, incorporating gravity as an external load. Experimental validation across four prototypes yields tip position root mean square errors (RMSEs) of 1.76 mm, 2.33 mm, 2.18 mm, and 1.33 mm, corresponding to 2.3 , 3.1, 3.4, a