新闻与研究

arXiv:2611.00896v1 Announce Type: new Abstract: Simulation-based VLA evaluation often treats matching task, seed, and episode identifiers as sufficient evidence that two policies faced identical test conditions. Under specific vectorized reset and autoreset conditions, different termination timing can change subsequent initial-state assignment, so the same episode index need not mean the same test state. We present an evaluation-integrity framework for paired comparison - invariants I1-I5, provenance logging, optional scene fingerprints, and an automated checker - and a controlled LeRobot 0.6.1 LIBERO harness study with emulated controllers. Four evidence levels are kept separate: (A) mixed-horizon Sync wave-1 IDs [20, 5, 22, 7] vs [4, 5, 6, 7]; (B) homogeneous separate-job Sync control (16 cells, 0 ID/fingerprint mismatches); (C) Async+Freeze batch-size map differences (12 I3 mismatches; bs=8 incomplete; mechanism unisolated); (D) a synthetic McNemar illustration only. Controllers wer

arXiv:2611.00879v1 Announce Type: new Abstract: This paper describes the development of a bio-inspired ornithopter wing mechanism that uses soft pneumatic actuators, such as PneuNet structures, in place of revolute joints, inspired by the complicated kinematics of bat flight. The proposed design employs pneumatically driven air pockets within a flexible wing skeleton to achieve smooth, lifelike flapping motion. The actuation system is controlled by 3/2-way solenoid valves and an air pump, which manipulate airflow and actuator deformation. We demonstrate, using ANSYS FEA software, the bending behavior of the PneuNet actuators under varying pressures to inform the wing's structural design. The implementation of soft robotics in the flapping-wing system offers potential advantages over traditional mechanisms, such as reduced mechanical complexity, lower acoustic signatures, enhanced adaptability, and wing morphing. This work demonstrates the feasibility of replacing rigid revolute joints

arXiv:2611.00734v1 Announce Type: new Abstract: Long-horizon embodied manipulation requires policies to act on information that is no longer visible. Modified objects may leave the field of view, while completed subtasks continue to constrain future actions. Existing approaches extend temporal context through recurrent state updates, test-time training, or storage and retrieval of past observations. As horizons grow, these mechanisms can still leave the policy to infer task state from long, redundant, and viewpoint-dependent visual experience. We argue that long-horizon agents benefit not merely from remembering more, but from remembering task-relevant state changes and progress. We propose Memory of Object States and Task Progress (MOST), a policy-internal memory framework that compresses multi-view observations into temporal tokens. Historical and predictive future tokens are explicitly supervised with object states and subtask progress, structuring memory around what has changed in

arXiv:2611.00634v1 Announce Type: new Abstract: A robot's next action can depend on recent motion or earlier visual evidence absent from its current observation. A recurrent visual-memory architecture can retain this history in a visual state, but its usefulness depends on what the state represents and how it is updated with new observations. We introduce Prediction-Regulated Memory (PREM), a framework that uses action-conditioned future prediction to guide both memory learning and updating in vision-language-action policies. During training, predicting future visual features provides temporal supervision for memory learning. During execution, PREM compares current visual features with the prediction made at the preceding decision and uses the spatially normalized prediction discrepancy to regulate updates to the visual state at each spatial location. The updated visual state supplies temporal context for action generation alongside current observations. Built on $\pi_{0.5}$, PREM impr

arXiv:2611.00615v1 Announce Type: new Abstract: The MetaFine Open Competition (RoboFineMani workshop, CoRL 2026) evaluates fine-grained manipulation beyond binary success. Its five simulated tasks, from grasping a bottle by an instructed part to inserting letter-shaped pegs with 4 mm clearance, are scored on perception under lighting and camera changes, instruction understanding, and behavior. This report presents Team Friday's solution, which got second place with an overall score of 0.947, more than twice the $\pi_0$ baseline. Instead of fine-tuning a large vision-language-action (VLA) model, we train one 19M-parameter policy per task with a separate instruction classifier and focus on the data, all generated in the simulator by a privileged scripted teacher, including its corrections of the student's mistakes. By analyzing failed episodes, we find that a teacher that almost always succeeds can still teach a memoryless student badly: its pauses become stalls, its fixed habits become

arXiv:2611.00612v1 Announce Type: new Abstract: State of the art latent dynamics world models achieve impressive performance for robot planning. But do they actually encode the physics of the world? In this paper, we show that popular world models today achieve strong results on tasks that reduce to image similarity but forget key details about objects in the scene like mass, deformation, friction, and bounciness. We achieve this by defining precisely what ought to be encoded in such a world model and introducing two new evaluation methods that can directly tease physical properties and easily scale to new ones. Our evaluation, which we call Roz, includes 10 contrastive tasks and 3 continuous tasks, totaling 1734 individual scenarios. We validate our eval on control tasks. On our own model, AnonWM, we find that performance roughly improves with training steps, while not necessarily being correlated with reconstruction MSE. We argue current model failures are a symptom of evaluation str

arXiv:2611.00582v1 Announce Type: new Abstract: Accurate posture feedback is essential for precise control of continuum robots. Off-the-shelf bend sensors measure curvature about only one axis; without a suitable mounting arrangement, they can restrict robot motion and provide incomplete posture information. SaddleSense introduces a 3D-printed thermoplastic polyurethane (TPU) saddle-shaped lattice that holds one commercial bend sensor along each curved edge. Because each sensor flexes within its own channel rather than spanning the segment axially, the lattice converts multi-axis body deformation into sensor curvature without tying segment length to sensor length. Each four-sensor module accommodates two-axis bending, contraction, and extension while quantifying all three deformation modes across its four readings. A hybrid calibration maps the sensor readings to piecewise constant curvature (PCC) parameters using a learned model paired with a physics-based fallback for out-of-distribu

arXiv:2611.00524v1 Announce Type: new Abstract: Modern robot policies often perform expensive inference to generate action chunks, making serving a latency bottleneck for real-time control and a throughput bottleneck for multi-robot serving. Building on the success of caching in image, video, and audio generation, recent robot-policy advances have reported promising speedups by reusing intermediate denoising states to avoid redundant computation. Surprisingly, we find that such reuse often offers little advantage over simply running fewer denoising steps from fresh noise, without any cache. Moreover, accelerating denoising leaves the expensive policy backbone intact, which can account for 70% of GPU time. We present Reflex, a serving layer that turns past policy executions into a backbone-free path for future decisions. Rather than reusing intermediate computations, Reflex caches complete action chunks to bypass both the backbone and action head. This introduces two challenges: similar

arXiv:2611.00513v1 Announce Type: new Abstract: Most agentic robotic systems use vision-language models (VLMs) either to orchestrate predefined skills or to directly generate actions, constraints, and control programs. The former is limited by the available skill library, while the latter requires VLMs to reason about precise geometry and contact. In this paper, we propose WorldSketch, a new paradigm for agentic robotics in which a VLM constructs a world model rather than generating robot actions. The VLM acts as an agent that actively queries perception tools to build and visually validate a minimal kinematic world model, capturing only task-relevant objects, their interaction geometry, and possible motions. A geometric planner then jointly optimizes contact and whole-body motion on the resulting model. Moreover, the world model is self-improving: execution feedback verifies outcomes, updates uncertain hypotheses, and enables the agent to revise the model when its predictions are cont

arXiv:2611.00509v1 Announce Type: new Abstract: Wearable robots such as supernumerary robotic limbs (SRLs) can augment human capability, but their effectiveness depends strongly on the operator's sense of embodiment, comprising the sense of agency (SoA) and the sense of ownership (SoO). When an SRL is detached from the body so that the operator can act in two places at once, both the sense of agency and task performance tend to degrade. This paper presents the Multi-modal Embodiment Stimulation Shoulder Pad (MESSPa), a wearable interface that combines one-to-one motion transfer with spatially distributed pneumatic (balloon) haptic feedback on the shoulder, with the goal of preserving the sense of agency across attached and detached states. We describe the system design, the control mapping, and a controlled experiment using a $2\times2$ mixed design that crosses the control interface between groups (MESSPa, $N=13$, vs. keyboard control, $N=12$ from our previous study) and attachment (o

arXiv:2611.00499v1 Announce Type: new Abstract: When searching for an object, humans naturally combine semantic intuition - reasoning about where the object is likely to be stored - with geometric awareness of what parts of the space are visible, occluded, or unexplored. Effective robot object search requires the same joint reasoning, yet existing methods rely on only one: geometric methods maintain principled spatial beliefs but lack deep semantic understanding, while language-model based methods leverage commonsense knowledge but maintain no explicit belief over space. We argue that jointly reasoning over semantic and geometric evidence in an explicit 3D belief enables human-like interactive object search that outperforms either modality alone. To test this claim, we build a system that accumulates per-object VLM plausibility scores and geometric occupancy evidence via Bayesian log-odds filtering into a unified 3D voxel belief field. High-belief regions in the combined belief field d

arXiv:2611.00455v1 Announce Type: new Abstract: Tactile sensing provides robots with direct observations of contact physics, local geometry, and interaction dynamics, making it a vital source of feedback for contact-rich manipulation. However, existing tactile manipulation studies are often developed and evaluated within specific task settings, making it difficult to systematically compare how tactile information contributes under different interaction conditions. In this work, we develop a benchmark in simulation comprising 128 tasks organized around three complementary evaluation regimes: Precise, Recognition, and Dynamic. The benchmark further includes controlled static and dynamic task variants and 12 perturbation settings for evaluating robustness under changes in visual and physical conditions. These settings motivate studying tactile information as a temporally evolving interaction representation rather than only as an instantaneous observation. We therefore propose an action-co

arXiv:2611.00405v1 Announce Type: new Abstract: Data flywheels promise continuous improvement via insights from deployment data. Understanding how the deployed model succeeds and fails can steer subsequent data collection towards patching failures and reinforcing successes. However, robot deployments create large volumes of high-dimensional rollout data, which is challenging to understand and confidently ground data collection decisions in. To make deployment data easier to analyze and act on, we propose behavior graphs, which compress policy rollouts into a single interpretable, directed graph. We construct the graphs by learning to identify similar behavior modes across rollouts, connecting the behaviors temporally, and finally linking them to outcomes. In a user study on three real-world tasks, where participants analyzed rollouts to choose a data collection strategy, 16 of 18 preferred behavior graphs to rollout videos, reaching comparable conclusions with 40.3% lower perceived tem

arXiv:2611.00392v1 Announce Type: new Abstract: Before foraging, desert ants perform learning walks (LWs) to acquire visual memories around their nest. With experience, these walks expand, become straighter, and contain fewer visual scans. We previously proposed a model in which these behaviours emerge from the angular mismatch between path integration (PI) and a mushroom-body-inspired visual compass (VC). Here, we test whether this mechanism can generate learning walks on a physical robot, despite the sensory and motor noise inherent to real-world conditions. We implement the model on a four-wheeled robot equipped with a panoramic camera, an IMU and wheel encoders providing compass and stride information for PI, and test it outdoors using a replicated desert-ant experimental setup. The PI--VC mismatch regulates walking speed, turning, pirouettes, and returns to the nest, while PI provides a teaching signal for visual learning. The robot autonomously decides when to acquire visual memo

arXiv:2611.00297v1 Announce Type: new Abstract: Collaborative robots (cobots) are designed to be safer and more interactive than traditional industrial robots, share the same workspace as humans, and assist them in a range of shop-floor tasks such as palletization, joint assembly, and part inspection. Many of these tasks involve repetitive workflows that may evolve with changes in production environments and task specifications. These requirements motivate the need for intuitive interfaces that allow workers to modify workflows on the shop floor and generalise these routines for future use. This work introduces the Teach-Abstract-Apply (TAA) framework, which enables human operators to (a) teach new workflows to cobots in natural language, (b) abstract the taught workflows into skills for later use, and (c) apply these skills in different settings. As a case study, we apply the TAA framework to a collaborative structure-building task and evaluate it with both open-weight (Qwen3-Coder) a