Actualités & Recherche

arXiv:2610.24522v1 Announce Type: cross Abstract: Human responses to sound depend not only on its semantic content, but also on the evolving spatial relationship between source and listener, conveyed through spatial audio and its temporal dynamics. Modeling human reactions to such dynamic spatial audio is important for character animation, human behavior analysis, and robotics. As of yet, existing audio-driven motion generation methods primarily focus on non-spatial or static acoustic conditions, leaving this important problem largely underexplored. To bridge this gap, we formulate the novel task of dynamic spatial-audio-driven human motion generation and introduce two motion-captured datasets. We introduce the Dynamic Spatial Audio and Motion dataset (DySAM) as the first dataset and benchmark for this task, providing synchronized binaural audio, 3D human motions, and moving sound source trajectories. Given the importance of dynamic spatial cues of sound sources, we further provide the

arXiv:2610.24209v1 Announce Type: cross Abstract: Slender structures made of stimuli-responsive materials represent an emerging class of programmable material systems, with applications in soft robotics, minimally invasive surgery, wearable electronics, and deployable structures. In biomedical applications, the on-demand design of slender structures, encompassing both their material layout and actuation, is essential to harnessing their large deformation and multiphysics actuation to meet patient-specific design requirements. However, owing to geometric and material nonlinearities, their structure-actuation-response relationship is highly nonintuitive and computationally demanding. This often makes trial-and-error and conventional design approaches challenging for addressing patient-specific needs. To this end, we present a Cosserat-rod-based design optimization framework for the autonomous design of programmable slender structures. Leveraging the Cosserat rod representation, the end-t

arXiv:2610.23940v1 Announce Type: cross Abstract: Collision detection for complex non-convex objects is a bottleneck in physics simulation, virtual reality, and robotics. Inner sphere trees (ISTs) answer proximity and penetration queries efficiently, but their greedy leaf-sphere construction is sequential and computationally expensive, which limits both large-scale preprocessing and runtime use. In this work, we present ML-IST, a machine-learning-based method that reformulates leaf-sphere generation as a single-shot set-prediction problem: a network predicts a fixed set of inscribed sphere candidates from multichannel voxel geometry, differentiable coverage feedback corrects the coarse prediction and fills residual gaps at finer scales, and a lightweight geometric projection enforces validity. Averaged over the test set, a single-GPU implementation of ML-IST reduces mean leaf-generation time by a factor of $2.0$--$2.6$ over single-core CPU construction across target capacities of 64--5

arXiv:2610.23907v1 Announce Type: cross Abstract: Real-world egocentric videos capture multi-step household procedures, but their recorded trajectories leave alternative actions and recovery from the learner's own mistakes largely unexplored. We introduce EgoVine, a post-training framework that turns a fixed demonstration set into interactive experience for long-horizon planning. EgoVine operates in executable symbolic environments compiled from real-world video annotations, where an agent must coordinate object manipulations under partial observation and account for delayed consequences. Restoring a decision state allows the learner to explore alternative actions and compare their eventual outcomes. Union jointly trains action generation and consequence prediction in one model, using the same executions to supervise candidate comparison and recovery. Verified repairs add recovery targets from failed attempts, while comparisons among sampled continuations guide policy updates. The upda

arXiv:2610.23882v1 Announce Type: cross Abstract: Due to their low accuracy, industrial robots commonly require online teaching after the offline development of a robot program in simulation. The accuracy is limited by compliance and transmission errors in the robot joints. Transmission errors are defined as errors between the load-side angles of the gearboxes and the motor encoder angles. An approach to identify the transmission errors based on laser tracker measurements is presented. The developed velocity-dependent Fourier series model is integrated in a virtual commissioning software as a co-simulated functional mock-up unit. Additionally, it is used to compensate the transmission errors by correcting the setpoint from the computerized numerical control (CNC). The model identification and the compensation are experimentally evaluated with a St\"aubli TX2-40 industrial robot. The model has a mean absolute error of 0.114 mrad, and the compensation improves the accuracy by 83.26 % for

arXiv:2610.23881v1 Announce Type: cross Abstract: The position and path accuracy of industrial robots is strongly affected by temperature. In particular, temperature-dependent friction and altered hysteresis and transmission errors in the gearboxes are the main causes of these deviations. Since standard industrial robots lack temperature sensors, it is proposed to estimate the gearbox lubricant temperature using observer structures. It is planned to apply the proposed lubricant temperature observer to provide the input for temperature-dependent error compensation for gearboxes in industrial robots. First, a two-state heat transfer model describing the heat transfer from the lubricant through the gearbox housing to the environment is described and identified. It is integrated into a Luenberger observer and a Kalman filter with a correction of the lubricant temperature estimation based on measurements from a housing temperature sensor. Temperature measurements were performed in an experi

arXiv:2610.26854v1 Announce Type: new Abstract: Multimodal reasoning provides robotic foundation models with predictive context needed for reliable action generation. However, language-based reasoning captures task semantics but struggles to express fine-grained physical dynamics, while latent reasoning models future state evolution but offers limited structure for organizing these dynamics into reusable reasoning primitives. To bridge this gap, we introduce PhysWord, a reasoning-before-acting foundation model built on Physical Words, which use atomic action verbs as shared semantic anchors for continuous representations of physical dynamics. Specifically, PhysWord predicts upcoming atomic action words and directly aligns their contextual hidden states with latent targets derived from the corresponding future observations. Building on these primitives, we construct a temporally structured PhysWord space with visual and optional tactile variants of each action word. Autoregressively pre

arXiv:2610.26852v1 Announce Type: new Abstract: Reinforcement learning enables mobile robots to make fast and scalable navigation decisions in constrained human-shared environments. However, directly extending single-robot approaches to multi-robot settings often fails to ensure safety and efficiency, especially under occlusions and out-of-distribution (OOD) scenarios, where individual robots may overlook potential risks or make inaccurate predictions. Leveraging cooperation effectively for both prediction and navigation remains a major challenge. We propose CoopUQ, a multi-robot social navigation framework that bridges uncertainty-aware cooperative occupancy forecasting and robust decision making. CoopUQ predicts future occupancy grid maps through cross-robot spatiotemporal feature fusion and further leverages forecasts and their uncertainty to guide policy via constrained multi-agent reinforcement learning, where the cost penalizes navigation through regions predicted to be occupied

arXiv:2610.26848v1 Announce Type: new Abstract: In this paper, we systematically investigate the mechanisms, scaling behavior, and generalization of physical Recursive self-improvement. We introduce PhysRSI-eval to quantify physical RSI through a five-axis metric: First-Attempt Performance, Efficiency, Success, Budget and Improvement from failure. Different VLMs are evaluated on 18 manipulation tasks under a fixed interaction budget. The analysis of experimental results motivates the Physical Barrel Hypothesis: different capabilities may constrain different parts of improvement. The five-axis metrics also distinguish systems with close public benchmark averages and show that high scores need not come with early or efficient gains. We study observational scaling from two perspectives: predicting the relative performance of VLM agents from their public capabilities, and forecasting later progress from early physical feedback. Together, these analyses ask how quickly and how far an agent

arXiv:2610.26846v1 Announce Type: new Abstract: Recent advances in multimodal models have enabled GPT-6 Astra to perform robotic manipulation through in-context learning. Yet contact-rich control and tactile perception are often regarded as strengths of specialist policies, leaving open whether Astra can directly understand and use tactile observations. We provide, to our knowledge, the first demonstration that Astra directly interprets vision-based tactile images and uses their contact cues for manipulation. Tactile RGB images enter through its existing visual interface, enabling few-shot closed-loop control on nineteen simulated UniVTAC and OpenViTac tasks with 210 sampled scene seeds per setting. With visuo-tactile input, Astra achieves mean success rates of 93.8% and 81.8%, exceeding the strongest reported specialist baselines in our comparison by 22.9 and 13.1 percentage points, respectively. Vision-only performance also leads these baselines, reaching 76.3% and 80.6%. Trajectory

arXiv:2610.26810v1 Announce Type: new Abstract: This paper presents the hardware implementation of a quadratic program-based controller on Harpy: thruster-assisted bipedal robot to perform thruster-assisted trotting. This is inspired by wing-assisted locomotion observed in birds such as the chukar partridge, which employ thrust vectoring to manipulate the ground reaction force. The proposed controller formulates the thruster-assisted spring-loaded inverted pendulum dynamics as a condensed QP-based model predictive control problem, enabling simultaneous regulation of ground contact force and active thrust vectoring at 500 Hz. A decoupled control architecture is used, where the robot's sagittal dynamics are controlled using QP-MPC while the frontal dynamics is regulated using a cascade orientation stabilization controller. Two experiments were conducted with different forward reference velocities of 0 m/s and 0.1 m/s. While the instantaneous velocity oscillates, the mean velocities can t

arXiv:2610.26765v1 Announce Type: new Abstract: General-purpose manipulation requires reliable behavior across embodiments, objects, and task demands. We introduce HandsOn, a composable simulation platform and benchmark for cross-embodiment dexterous manipulation. It supports five robotic arms, six dexterous hands, and approximately 200 rigid, articulated, and deformable assets in randomized scenes. The benchmark pairs 18,000 curated human demonstrations with 66 human-verified tasks: Core-50 supports demonstration-based learning and execution tests, while Transfer-16 evaluates asset and relation transfer, skill composition, and deformable manipulation without target-task training demonstrations. Qualitative real-robot rollouts illustrate sim-to-real transfer. Across diffusion, vision-language-action, world-action, and prompted VLM-based methods, the best trained multi-task policy achieves 29.1% Core-50 success. Without benchmark finetuning, GPT-6 using move_eef for palm-and-finger cont

arXiv:2610.26762v1 Announce Type: new Abstract: We introduce DexOAK, a learning-based object-aware kinematic retargeting framework for generating contact-consistent robot hand references from human motion-capture data. DexOAK first minimizes an interaction-graph Laplacian objective over a joint hand-object graph, so the robot reproduces the demonstrated object-relative interaction geometry rather than the human hand pose alone. These per-frame solutions form seed retargeted references, from which a feed-forward network conditioned on human keypoints and object point clouds learns the retargeting function. Object-aware finetuning then refines the network under contact, non-penetration, and self-collision losses that position the fingers on the demonstrated contacts and reduce artifacts destabilizing physics-based refinement. Once trained, the network transfers to unseen data by inference alone, without per-frame optimization. DexOAK references improve dynamic retargeting over the state-

arXiv:2610.26759v1 Announce Type: new Abstract: Continuous in-hand rotation requires coordinated contact maintenance and renewal, linking manipulation performance to both hand geometry and tactile feedback. We investigate these relationships through rotational progress, finger participation, and fingertip loading on two versions of the low-cost LEAP Hand with anthropomorphic and symmetric finger arrangements. Both hands are integrated with TacTip vision-based tactile sensors and use a shared reinforcement learning framework. Simulation evaluates full tactile, binary touch, and proprioception-only observations on 12 unseen objects; hardware experiments evaluate full tactile policies on ten objects under multiple hand orientations. In simulation, full tactile observations increase mean normalized rotation per episode over proprioception alone by 94.8% for the anthropomorphic hand and 66.9% for the symmetric hand, with longer mean episode durations and lower mean total fingertip loads. Ha

arXiv:2610.26717v1 Announce Type: new Abstract: Robotic manipulation in agricultural environments remains challenging due to variations in plant geometry, occlusion, and camera viewpoints. Pretrained vision-language-action (VLA) and generalist robot policies offer a promising approach to addressing these challenges by learning manipulation behaviors from multimodal observations and language instructions. However, adapting these policies to agricultural tasks requires domain-specific demonstrations, which are costly and time-consuming to collect on physical robots. In this work, we develop a simulation-to-real robot-learning framework for agricultural reaching and grasping that integrates task-specific demonstration collection and pretrained policy adaptation. A MuJoCo-based agricultural environment is developed with diverse tomato plant structures to support demonstration collection and controlled evaluation. Expert demonstrations are collected through joystick teleoperation, with a lo