Actualités & Recherche

arXiv:2610.41868v1 Announce Type: new Abstract: LLM-based robotic agent systems can generate programs for robot control, yet their generalization to new tasks often depends on elaborate scaffolding. Expanding their skill repertoires alone offers limited guidance on how to assess new capabilities or identify what remains beyond their reach. We introduce META-Robot, a framework that couples self cognition with capability scaling by expanding the criteria through which an agent evaluates and develops its skills. At its core, action-level predicates make execution outcomes explicit and available for LLM reasoning. The agent uses these assessments to identify its own limitations, extend relevant evaluation criteria, and guide the structured expansion of its skills. Proprioceptive feedback further grounds this process in the robot's physical state, enriching the evidence available for self-cognition. Experiments in simulation and on real robots demonstrate stronger overall task performance,

arXiv:2610.41857v1 Announce Type: new Abstract: Simulation benchmarks score robot policies by success rate, and every failed episode counts against the policy. Many of these failures are the simulator's fault, not the policy's; we find the cause and fix it. Testing a vision language action model and a world action model in simulation, we find that the policy often places the object and the simulator then ejects it, and that the benchmark's own scripted demonstrations fail the same way. We present SimuGuard, an auditing plugin for LIBERO, RoboTwin, RoboCasa and RoboDojo. It leaves the evaluation unchanged. At every physics step, it records the state and flags an object that starts moving at a contact faster than the robot's contact or gravity account for. It replays a recorded episode bit for bit, and it reruns the episode with one simulation parameter changed. With it we trace these episodes on RoboTwin to the collision detection of the physics engine: for geometry one to two centimete

arXiv:2610.41844v1 Announce Type: new Abstract: Inserting an unseen part requires sub-millimetre precision and the correct orientation of keyed features. Current zero-shot agentic manipulation methods built on vision-language models (VLMs) produce spatial outputs that are far too coarse for this. We propose to let a VLM build a sketch model of the task, i.e., a few geometric primitives and two mate frames, which a VLM agent measures with geometric tools in multi-view depth and which a standard planner and compliant controller execute. Because a sketch describes the parts independently of their placement, the robot needs to build it only once per task. A sketch memory stores each agent run and transfers the stored sketch to later placements by point-cloud registration, so that the VLM is called only for unfamiliar parts. After grasping the part, the robot inspects both mating faces at close range and corrects the sketch by shape matching. On 69 simulated insertion episodes, our method i

arXiv:2610.41768v1 Announce Type: new Abstract: Recent years have seen multiple impressive results in visual-inertial odometry (VIO) techniques, by which accurate state estimation can be achieved via the extended Kalman filter (EKF) or nonlinear optimization. However, these approaches are rarely open source, and they tend to fail in real experiments due to the temporary lack of feature points and fast motion. Therefore, in this study, we used encoders to overcome the temporary failure of purely vision-based simultaneous localization and mapping (SLAM). Here, we propose a generative measurement model for encoders and derive an expression for the maximum a posteriori (MAP) estimate and necessary Jacobians for optimization. We use our theory to present a novel tightly coupled visual-inertial encoder RGB-Depth (RGB-D) SLAM system. Tests on our system on an in-house dataset confirmed that our modeling effort led to accurate (with a root mean squared error (RMSE) of approximately 2-7 cm) and

arXiv:2610.41752v1 Announce Type: new Abstract: This paper leverages the higher-order kinematic derivatives of serial robots to achieve efficient, aspect-preserving real-time inverse kinematics. First, a family of higher-order solvers is derived, with a necessary orientation-error correction map, yielding the QuIK4 and QuIK5 methods with fourth- and fifth-order convergence. Second, an aspect-preserving step limiter extrapolates the Jacobian determinant along the step to predict and avoid singularities, shortening steps only when they head toward a singularity rather than when the current iterate is ill-conditioned. Third, a Hermite-interpolation certifier verifies the aspect of each answer, and a certified restart algorithm recovers failures from proven same-aspect seeds. The proposed methods are shown to outperform prior methods, reducing failures by up to four orders of magnitude and nearly eliminating aspect switching at tracking-level errors. A C++ library is released with ROS 2 in

arXiv:2610.41724v1 Announce Type: new Abstract: Architectural spaces are increasingly gaining robotic capabilities. Architectural robots such as robotic partitions, ceilings, furniture, facades, and other spatial components can autonomously move and reconfigure in response to occupants' needs and activities. Yet, when architectural robots start shaping the spatial conditions in which people continuously live, work, and interact, we may begin to experience the architectural space itself as robotic. What does this shift mean for how we inhabit such spaces? Drawing on our previous studies, I reflect on how architectural robots interact with people not only through their motions, but also through the spatial configurations that persist after those motions stop; how their actions may benefit some occupants while disadvantaging others; and how multiple robots may together make a whole architectural space appear to act. I argue that as computation gains the capacity to physically reconfigure

arXiv:2610.41702v1 Announce Type: new Abstract: Flexible robot links challenge the rigid-body assumption, making joint measurements alone insufficient for accurate manipulation. This paper introduces a flexibility-aware manipulation framework that connects structural dynamics directly to sensing, kinematics, and control. Instead of prescribing a dense inertial measurement unit (IMU) network, the dominant vibration modes of each flexible link are first identified from response-convergence and excitation-bandwidth criteria. Under a full-rank modal observability condition, $m$ retained modes require only $m$ independent slope measurements and one reference IMU, defining the minimum observable IMU configuration. The resulting measurements are used to reconstruct the continuous link deformation, which is then incorporated into flexibility-aware forward kinematics and a deformation-informed partial differential equation-based controller. Experiments on hydraulic single- and double-link manip

arXiv:2610.41688v1 Announce Type: new Abstract: Recent advances in generative foundation models have expanded the capabilities of embodied AI, enabling increasingly general navigation, manipulation, and long-horizon task execution. However, effective interaction with dynamic environments requires more than only strong perception and reasoning: agents must act with partial knowledge and retain, retrieve, and update information from past observations, actions, and experiences. This survey examines embodied memory as a mechanism for preserving decision-relevant information beyond the current observation. We organize existing approaches along two complementary dimensions: six fundamental memory requirements, spanning world-state persistence, task progress, belief maintenance, and experiential learning; and eight architectural categories, including recurrent states, fast weights, visual and symbolic representations, spatial memory, and experience-based systems. We analyze their capabilities

arXiv:2610.41679v1 Announce Type: new Abstract: Wearable force-augmentation technologies, including exoskeletons and soft exosuits, are being developed for occupational and rehabilitation settings to enhance performance by reducing the muscular demands of movements. Yet enhanced performance also requires users to perceive the environment accurately, adapt to changing conditions, and decide whether actions are worth their physical cost. We used an elbow-assisting exosuit to investigate the physiological, perceptual and cognitive consequences of force augmentation during lifting. We assessed weight perception, adaptation in a volatile environment, and effort-based decision-making with and without assistance. While assistance reduced muscle activity, its behavioural outcomes did not simply reflect this biomechanical change. Assistance reduced perceived weight, but this effect was strongly shaped by participants' initial experience. Assistance also selectively reduced adaptation to changin

arXiv:2610.41676v1 Announce Type: new Abstract: Autonomous surface vessels operating in urban waterways must coordinate with other vessels in confined spaces while complying with traffic regulations, often without knowing their intentions. We present a decentralized motion planner based on model predictive path integral control, a sampling-based model predictive control scheme. The planner jointly computes a trajectory for the ego vessel and a prediction of the trajectories of the other vessels, and therefore accounts for their interaction. Traffic regulations are encoded as discontinuous costs, which the sampling-based formulation admits without a smooth approximation. We evaluate the method in maps taken from real sections of Amsterdam's canals, in encounters between two vessels and in scenarios with four interacting vessels. Across 780 randomized runs the method reaches every goal without collisions or deadlocks, and outperforms an optimization-based planner designed for the same do

arXiv:2610.41671v1 Announce Type: new Abstract: Visual-inertial odometry (VIO) is a fundamental topic in robotics research. Beyond research, VIO has been adopted in a wide range of products. In this manuscript, we present OpenVIO, an open-source VIO system for research and practical applications. It is released under the MIT license. OpenVIO supports pinhole and fisheye cameras and extends this support to stitched 360{\deg} images. We describe each part of the system together with its design considerations. We also report benchmark results with other open-source VIO systems on public datasets. OpenVIO is available as open source at https://github.com/93won/openvio.

arXiv:2610.41667v1 Announce Type: new Abstract: In this letter, we present a volumetric mapping system that effectively calculates Occupancy Grid Maps (OGMs) and Euclidean Distance Transforms (EDTs) with parallel computing. Unlike these mappers for high-precision structural reconstruction, our system incrementally constructs global EDT and outputs high-frequency local distance information for online robot motion planning. The proposed system constructs OGM with massive amount of data from multiple types of sensor inputs. Using GPU programming techniques, the system quickly computes EDT in parallel within local volume. The new observation is continuously integrated into the global EDT using the parallel wavefront algorithm while preserving the historical observations. Experiments with datasets have shown that our proposed approach outperforms existing state-of-the-art robot mapping systems and is particularly suitable for mapping unexplored areas. In its actual implementations on aerial

arXiv:2610.41663v1 Announce Type: new Abstract: Continual adaptation to new tasks is essential for robots deployed in the real world, but adapting pre-trained policies remains challenging due to parameter growth and the need for continual demonstration collection. Parameter-Efficient Continual Fine-Tuning (PECFT) isolates task-specific updates, but adapter-per-task designs require demonstrations and grow with the task set. We propose ResMerge, a two-stage framework that learns mergeable residual policies atop a frozen pre-trained policy: residual reinforcement learning (RL) acquires task-specific corrections without additional expert demonstrations during adaptation, and parameter-space merging consolidates compatible residuals to reduce stored parameters. We introduce Residual-Field Consistency (RFC), a simple, bounded, and interpretable metric that assesses compatibility in residual action-field space by comparing correction direction and magnitude. Experiments on LIBERO across two b

arXiv:2610.41654v1 Announce Type: new Abstract: Robot policy learning is dominated by zeroth-order methods such as PPO, which treat the simulator as a black box and pay for it with enormous sample counts. First-order policy learning differentiates through the dynamics, but on contact-rich whole-body control it has rarely worked: its gradients explode. A recent world-model method showed that derivative information can make humanoid motion tracking far more efficient, using surrogate gradients of a learned model. We show that exact gradients of the simulator itself do better, once two obstacles are removed. Exact gradients contain rare unbounded samples, because contact dynamics is not differentiable everywhere and its derivative diverges near some events; and they are expensive, because the simulator's adjoint has to replay the contact solve. $\nabla$Mimic bounds these samples with median-relative per-sample gradient clipping at every step of the backward pass, and reuses each costly si

arXiv:2610.41643v1 Announce Type: new Abstract: Diffusion and flow matching visuomotor policies require iterative denoising or numerical integration for multimodal action generation, introducing latency and execution mismatch. We instead consider a different formulation: conditioned on vision and proprioception, demonstrated bimanual action chunks concentrate in a structured observation-dependent region of joint-action space, making the action target more direct to model than the generative process. Based on this view, we propose the Direct Manifold Action Transformer (DMAT), which reformulates action generation as the direct modeling of clean, observation-conditioned joint-space action chunks from stochastic interpolants, thereby enabling single-step inference without relying on iterative generative inference or auxiliary trajectory-straightening procedures. Within DMAT, local 3D geometry anchors predictions to spatial structure, while State-Consistent Spectral-Time Refinement (STR) r