Noticias e Investigación

arXiv:2611.01437v1 Announce Type: new Abstract: Surface cleaning requires robots to adapt both where they clean and how they interact with contamination whose response to cleaning may vary spatially and remain unobservable prior to interaction. We formulate adaptive surface cleaning as a partially observable Markov decision process and propose a belief-aware planning framework that uses observed cleaning outcomes to update a latent local cleaning-response model and adapt subsequent cleaning motions, forces, and passes. Preliminary grid-world simulations demonstrate the resulting closed-loop planner adapting its spatial decomposition and cleaning actions as the surface evolves.

arXiv:2611.01404v1 Announce Type: new Abstract: Agentic robots combine a vision-language brain, a tool-using harness, and a vision-language-action (VLA) policy. Serving a fleet of these agents introduces two coupled resource constraints: the brain repeatedly revisits long, growing key-value (KV) cache states, while the VLA executes action chunks under tight control deadlines. We characterize 80 LIBERO-PRO episodes generated by GPT-6-Astra, RPent, and pi0.5 (1,862 brain turns and 1,582 VLA inference chunks), profile both layers on H800 graphics processing units (GPUs), and replay the resulting agent programs on a vLLM serving engine. Brain contexts reach 215k tokens; the median Astra request takes 7.78 s, while the intervening environment-step gap is only 1.0 s. Under a 0.25 s action-chunk deadline, one VLA GPU meets the 1% starvation target for one robot; under a 1.0 s deadline, its measured capacity reaches at least 32. In sustained brain replay, increasing concurrency from 20 to 32 r

arXiv:2611.01390v1 Announce Type: new Abstract: Vision-language-action (VLA) models predict chunks of future actions, yet deployed robots waste time in two ways that are independent of model capability: they halt while the next chunk is inferred, and they move at the speed of slow, cautious demonstrations. Existing remedies remove one loss each, and stacking them fails, because asynchrony and speed-up each change the correspondence between planned progress and executed motion. We present Path-preserving Time Scaling (PTS), a training-free execution layer for frozen action-chunking policies built on one principle: the robot's motion is a time reparameterization of a reference path, and the path changes only in response to new observations. A path layer decodes every replan deterministically in one step and enters it at the progress consumed during inference. A time layer sums the planned increments, keeps the arm at 1x while the gripper moves, and caps the rate by the actuator's linear

arXiv:2611.01277v1 Announce Type: new Abstract: Exploring systems that bridge the gap between non living and living matter requires a reconfigurable environment coupled to synthetic systems capable of autonomous motion, adaptive behaviour and persistent dynamics. Self propelled droplets provide a minimal platform for such life like behaviour, but quantitative measures capturing both persistence and adaptability to enable closed loop reconfiguration remain lacking. Here we introduce an autonomous closed loop robotic platform to characterise and dynamically program the autonomous behaviour of multicomponent oil in water droplets. We explored two chemical composition spaces, with and without external pH perturbations, analysing droplet behaviour in real time and iteratively selecting formulations. To quantify this behaviour, we introduce two descriptors, persistence and diversity. Persistence is quantified by mapping temporal dynamics in a speed shape complexity space, where we identify a

arXiv:2611.01250v1 Announce Type: new Abstract: As learned manipulation policies become increasingly capable of solving practical manipulation tasks, assessing how well a task is performed becomes a fundamental challenge. While binary labels confirm task success, they cannot distinguish successful executions with different levels of geometric precision. To address this, we introduce SPECTRA, an object-centric framework for quantifying execution quality in manipulation of rigid objects. From a small set of oracle demonstrations generated in simulation, SPECTRA represents object interactions through progress-aligned relative poses. SPECTRA identifies which relative-pose directions are constrained and their admissible variations from demonstrations. These constraints establish a reference for evaluating robotic manipulation tasks. To score an unseen execution, deviations beyond the admissible variation are summarized by a Task Quality Index Q in [0,1]. Across seven simulated tasks, SPECTR

arXiv:2611.01220v1 Announce Type: new Abstract: High-accuracy repetitive tracking is challenging for position-commanded manipulators whose low-level controllers are inaccessible, while constrained model-based iterative learning control (ILC) can require substantial manual parameter tuning. We present Differentiable Iterative Learning Control (Diff-ILC), an online bilevel framework that adapts the parameters of a constrained ILC update between physical robot executions. A closed-loop differential model is identified once from paired smooth perturbations and subsequently kept fixed. At each trial, a constrained quadratic program computes a feasible command correction, while a real-anchored next-trial prediction defines a task-level objective. Hypergradients are obtained by implicitly differentiating the locally active KKT system, enabling multiple computational parameter updates without additional robot executions. Experiments on a 6-DoF DOBOT CR3 across unseen planar and three-dimension

arXiv:2611.01215v1 Announce Type: new Abstract: Diffusion-based vision-language-action (VLA) models cascade pretrained vision-language models (VLMs) with diffusion action experts, but typically restrict the VLM to providing static conditioning. This separation limits how much pretrained perception and reasoning can guide action generation, especially in out-of-distribution settings. We introduce JADE, a Joint Action DEnoiser that enables the VLM and action expert to jointly refine actions. Our key insight is that structured action latents allow the VLM to read the current noisy action together with visual observations and language instructions. At every denoising step, the VLM contextualizes the noisy action and guides the action expert through gated cross-attention, so that its guidance evolves with the action being generated. We further supervise the VLM to recover clean action latents from noisy ones, which gives it a direct action-learning signal and couples perception with control

arXiv:2611.01197v1 Announce Type: new Abstract: Humanoid robots operating in shared, dynamic environments must respond to rapidly approaching objects while maintaining balance. We study dodgeball as a controlled test of this capability, coupling visual threat assessment with whole-body evasion, recovery, and target reacquisition. We develop a system that integrates onboard monocular perception, temporal ball-state and threat-flag estimation, and learned whole-body control. A ball detector supplies bounding-box histories, and a temporal estimator predicts relative ball position and a binary threat flag. Separate dodge/recovery and search/track policies share this interface and are composed to support repeated encounters. A three-stage curriculum first trains control with privileged ball states, then starts estimator supervision from stable policy rollouts, and finally replaces the actor's privileged inputs with estimator outputs. Analytic projection and perception randomization provide

arXiv:2611.01190v1 Announce Type: new Abstract: Garment flattening and folding require robots to identify garment structure during manipulation and adapt their actions as the cloth deforms. We present ClothDojo, a simulation platform that generates adaptive manipulation trajectories at scale across diverse garments and initial states and evaluates policies in closed loop. ClothDojo encodes each garment's canonical mesh coordinates as an NOCS texture, providing canonical surface-position cues through the visual interface of a pretrained vision-language-action model. We fine-tune the model on human demonstrations and corrections to obtain a data-generation policy. The policy autonomously generates simulated manipulation trajectories, and we retain rollouts that satisfy task-specific success criteria. Each retained trajectory is re-rendered under a randomly sampled appearance to provide RGB observations for downstream policies that do not receive NOCS. Across both tasks, the policy produc

arXiv:2611.01112v1 Announce Type: new Abstract: Vision-language models can interpret instructions and plan manipulation tasks, yet struggle when acting directly as robot policies. On RoboDojo, a large planning-execution gap separates high-level task decisions from physical execution: removing arm execution raises GPT-6 Astra's success rate from 22.5% under the reference control interface to 76.2%. We present Auriga, a task-agnostic agent harness that closes 68.3% of this gap in zero-shot manipulation without target-task trials or resets. In Auriga, the model makes all task decisions over an explicit task belief and acts through either direct tool calls or synthesized multi-step Python programs, while the harness supplies calibrated 3D measurements, finger-referenced dual-arm control, and a single admission gateway to the robot. Before motion, the model inspects a visual trajectory preview while simulation is paused; during execution, the harness checks declared stage tolerances and gra

arXiv:2611.01081v1 Announce Type: new Abstract: In this paper we present the methodologies, results, and analysis used to deploy a decentralized safety verification algorithm on a robotic swarm to prevent collisions at runtime in an outdoor environment. Typically, approaches toward verification of swarms of cyber-physical systems (CPS) are evaluated solely in numerical simulations or in highly controlled, noiseless indoor environments. Our efforts to implement collision avoidance on a real swarm system outdoors have revealed that the underlying algorithmic assumptions juxtaposed against the implementation details of the target system are critical factors in a method's efficacy in the real world. Here we modify, enhance, and analyze a decentralized reachability-based method for collision avoidance on a size, weight, and power-constrained uncrewed aerial vehicle (UAV) swarm with the objective of illustrating the challenges of real-world CPS deployments as well as provide guidance for res

arXiv:2611.00978v1 Announce Type: new Abstract: Hao Ren, Shuohan Chen, Senyuan Mu, Ruihan Huang, Wanfeng Shang, Yajing Shen

arXiv:2611.00957v1 Announce Type: new Abstract: Tactile sensing plays an important role in imitation learning for contact-rich manipulation by providing direct observations of physical contact with objects. Recently, several tactile foundation models (TFMs) have been proposed to learn general-purpose tactile representations through pretraining on large-scale tactile datasets. However, how these pretrained tactile representations should be leveraged for end-to-end imitation learning remains insufficiently explored. In this work, we integrate multiple TFMs into Diffusion Policy and systematically evaluate their effectiveness for contact-rich manipulation under common experimental settings. We further investigate adaptation strategies, including encoder fine-tuning and task-specific mid-training, to understand how pretrained tactile representations can be effectively used for downstream imitation learning. Through extensive evaluation across diverse manipulation tasks in both real-world a

arXiv:2611.00955v1 Announce Type: new Abstract: Fine-grained robotic manipulation requires instruction grounding, robust perception, and precise contact control. We describe our submission to the MetaFine Open Competition at CoRL 2026. Five independently trained models use a common architecture with frozen DINOv2 and CLIP encoders, trainable language-conditioned feature modulation, and task-specific action heads. We improve demonstrations through contact-aware grasp filtering, instruction-matched data, and expert correction of insertion failures. At inference, task-dependent strategies include photometric test-time augmentation, stochastic action averaging, and uncertainty-triggered replanning. Our submission achieves an official overall score of 0.914 and ranks third among 15 teams. We analyze the models and their task-specific behavior.

arXiv:2611.00951v1 Announce Type: new Abstract: We test whether Koopman structure, linear dynamics in a lifted state space, helps when a humanoid whole-body tracking policy is trained by backpropagating through a differentiable world model, replacing the MLP world model of an otherwise unchanged pipeline for simulated Unitree G1 tracking. It does not: return is at best equal (0.823 versus 0.830), one-step error on identical data is 1.3 to 1.6 times the MLP's, and training is about 15% slower. Lifted rollouts diverge or fail to train a policy; of the schemes tested, re-lifting at every imagined step trains best. Re-lifting never uses the lifted linear dynamics and leaves a model affine in the action, with state-dependent offset and slope; an action-affine MLP also predicts worse, so the cost belongs to this class. At a fixed state, the curvature of the true dynamics in the action explains only part of it. Across states, the policy's actions span a curved region, so an affine model fits