ニュース＆研究

arXiv:2606.03931v2 Announce Type: replace Abstract: This letter proposes a novel distributed pose estimator for multi-robot systems evolving on $\mathrm{SE}(3)$. The robots' positions are estimated in $\mathbb{R}^3$ using angles computed from body-frame bearings, without requiring orientation knowledge. The robots' orientations are then recovered in $\mathrm{SO}(3)$ from the estimated positions, together with bearing and bearing-rate measurements. The estimator accommodates directed sensing topologies and requires only infinitesimal angle rigidity (IAR), thereby relaxing the requirement, common in bearing-based approaches, that every robot acquire at least two bearings. Unlike existing angle-based schemes, the proposed method also estimates the robots' orientations. We prove local uniform exponential stability of the observer, assuming that a subset of robots executes persistently exciting motions. These theoretical results are corroborated through numerical simulations.

arXiv:2604.21541v2 Announce Type: replace Abstract: Wheel-legged robots combine the efficiency of wheeled locomotion with the versatility of legged systems, enabling rapid traversal over both continuous and discrete terrains. However, conventional designs typically employ fixed wheels as feet and limited degrees of freedom (DoFs) at the hips, resulting in reduced stability and mobility during legged locomotion compared to humanoids with flat feet. In addition, most existing platforms lack a full upper body with arms, which limits their ability to perform dexterous manipulation tasks. In this letter, we present X2-N, a high-DoF transformable robot with dual-mode locomotion and manipulation. X2-N can operate in both humanoid and wheel-legged forms and transform seamlessly between them through joint reconfiguration. We further propose a reinforcement learning (RL)-based whole-body control framework tailored to this morphology, enabling control across hybrid locomotion, transformation, and

arXiv:2604.18271v2 Announce Type: replace Abstract: As the world of agentic artificial intelligence applied to robotics evolves, the need for agents capable of building and retrieving memories and observations efficiently is increasing. Robots operating in complex environments must build memory structures to enable useful human-robot interactions by leveraging the mnemonic representation of the current operating context. People interacting with robots may expect the embodied agent to provide information about locations, events, or objects, which requires the agent to provide precise answers within human-like inference times to be perceived as responsive. We propose the Embodied Light Graph Retrieval Agent (EmbodiedLGR-Agent), a visual-language model (VLM)-driven agent architecture that constructs dense and efficient representations of robot operating environments. EmbodiedLGR-Agent directly addresses the need for an efficient memory representation of the environment by providing a hybr

arXiv:2603.25685v2 Announce Type: replace Abstract: Action-conditioned robot world models generate future video frames of the manipulated scene given a robot action sequence, offering a promising alternative for simulating tasks that are difficult to model with traditional physics engines. However, these models are optimized for short-term prediction and break down when deployed autoregressively: each predicted clip feeds back as context for the next, causing errors to compound and visual quality to rapidly degrade. We address this through the following contributions. First, we introduce a reinforcement learning (RL) post-training scheme that trains the world model on its own autoregressive rollouts rather than on ground-truth histories. We achieve this by adapting a recent contrastive RL objective for diffusion models to our setting and show that its convergence guarantees carry over exactly. Second, we design a training protocol that generates and compares multiple candidate variable

arXiv:2602.21203v2 Announce Type: replace Abstract: Visual reinforcement learning is appealing for robotics but expensive. Off-policy methods are sample-efficient yet slow while on-policy methods parallelize well but waste samples. Recent work has shown that off-policy methods can train faster than on-policy methods in wall-clock time for state-based control. Extending this to vision remains challenging, where high-dimensional input images complicate training dynamics and introduce substantial storage and encoding overhead. To address these challenges, we introduce Squint, a visual Soft Actor Critic method that achieves faster wall-clock training than prior visual off-policy and on-policy methods. Squint achieves this via parallel simulation, a distributional critic, resolution squinting, layer normalization, a tuned update-to-data ratio, and an optimized implementation. We evaluate on the SO-101 Task Set, a new suite of eight manipulation tasks in ManiSkill3 with heavy domain randomiz

arXiv:2601.21712v3 Announce Type: replace Abstract: Vision Language Action (VLA) models enable instruction-following manipulation, yet their deployment on coordinated dual-arm platforms remains severely constrained by under-modeled self-collisions between manipulators and grasped objects. To address this critical safety gap, we propose CoFreeVLA, a novel framework that augments end-to-end VLA policies with a lightweight, short-horizon self-collision risk estimator. The estimator predicts collision likelihoods directly from proprioceptive states, visual embeddings, and candidate action sequences. Deeply integrated into the closed-loop control system, this estimator proactively gates risky commands, autonomously synthesizes recovery trajectories to safe states via risk-guided adjustments, and biases policy refinement for safer rollouts. To ensure robust calibration, the estimator utilizes a two-stage training pipeline, pre-training with model-based synthetic collision labels, followed by

arXiv:2509.20219v2 Announce Type: replace Abstract: Robotic tails can enhance the stability and maneuverability of mobile robots, but current designs face a trade-off between the power of rigid systems and the safety of soft ones. Rigid tails generate large inertial effects but pose risks in unstructured environments, while soft tails lack sufficient speed and force. We present a Biomimetic Vertebraic Soft Robotic (BVSR) tail that resolves this challenge through a compliant pneumatic body reinforced by a passively jointed vertebral column inspired by musculoskeletal structures. This hybrid design decouples load-bearing and actuation, enabling high-pressure actuation (up to 6 bar) for superior dynamics while preserving compliance. A dedicated kinematic and dynamic model incorporating vertebral constraints is developed and validated experimentally. The BVSR tail achieves angular velocities above 670 deg/s and generates inertial forces and torques up to 23.6 N and 2.48 Nm with a 500 g tip

arXiv:2508.06207v3 Announce Type: replace Abstract: Back-support exoskeletons mitigate musculoskeletal strain, yet current systems rely on reactive sensing and lack context-aware assistance modulation. This paper presents a population-derived optimization framework and a predictive vision-based adaptive control strategy. First, we construct a multi-metric optimization space combining electromyography reduction, perceived discomfort, and user preference, revealing a non-linear relationship between payload and optimal assistance from experiments with 12 subjects. Second, we develop a computer vision-based adaptive control leveraging a fine-tuned vision transformer (DINOv2) and depth sensing to estimate payloads prior to lifting, eliminating actuation latency. Validation with an additional 12 subjects demonstrates robust payload estimation (82.41% accuracy). The proposed adaptive strategy reduces peak back muscle activation by up to 23% and improves average offloading by 8.15% over static

arXiv:2609.05161v1 Announce Type: cross Abstract: Rigid Body Dynamics (RBD) forms the computational core of real-time robotic control, but its immense computational complexity creates a performance bottleneck that necessitates dedicated hardware accelerators. However, the substantial hardware resource and power costs of these accelerators make their deployment on resource-constrained edge platforms highly challenging. While quantization offers a promising path to optimize RBD hardware for edge computing, existing uniform-precision approaches remain inefficient by ignoring the diverse quantization sensitivities of different variables. Although mixed-precision offers a superior alternative, its exploration is intractable due to a vast search space and the prohibitive cost of closed-loop simulation for motion accuracy evaluation. To address these challenges, we introduce APEX-RBD, an automated framework that makes mixed-precision exploration computationally tractable while effectively ide

arXiv:2609.04958v1 Announce Type: cross Abstract: Recovering camera and hand motion in world coordinates from egocentric video is a key capability for activity understanding, robot learning, and augmented reality. Existing systems typically decompose this problem into separate stages for camera motion, depth, hand reconstruction, and trajectory refinement, resulting in substantial computational overhead and preventing the joint modeling of camera and hand motion. We introduce MINT (Minting IN-the-Wild Trajectories), the first foundation model that directly produces complete world-space two-hand trajectories from ego-centric RGB video. From a single shared spatiotemporal video representation, MINT jointly predicts the camera trajectory, camera-frame hand states, and per-frame hand presence, and then produces world-space hand motion via explicit coordinate transformations. Training such a model at scale is challenging, since paired world-space camera and hand annotations are scarce. We t

arXiv:2609.04394v1 Announce Type: cross Abstract: This technical report is a study of the use of differential game (DG) theory to solve the target-assignment and midcourse guidance problems of drone swarms tasked with intercepting opposing swarms in defense of high-value assets. The game-theoretic tactics---which treat the intruder swarm as a rational agent and seek a Nash equilibrium between defenders and intruders---are compared against baseline tactics that model the defense problem as a unilateral optimization of the defenders' maneuvers. Monte Carlo simulation and Bayesian analysis show that the game-theoretic approach has a higher probability of successfully intercepting all intruders than the baseline techniques. This improvement in successful defense probability is most pronounced when the intruder swarm is capable of evasive maneuvers: relative to baseline optimization tactics, differential-game tactics increase estimated defense success from 94.6% to 96.8%, closing approximat

arXiv:2609.04381v1 Announce Type: cross Abstract: Recognizing specific objects onboarded without a labeled training set recurs across manufacturing and service robotics, yet the conventional renderable prior, a computer-aided-design (CAD) model, is often unavailable. Two-dimensional capture supplies no shape prior, and frozen foundation features fail on geometrically similar, low-texture industrial parts. We ask what a short object-centric scan buys for recognition beyond the captured images themselves: each object is reconstructed with 3D Gaussian Splatting (3DGS), summarized into a per-class shape prototype, and fused with frozen DINOv2 image features. First, the scan recovers the recognition value of CAD without CAD: geometry from RGB-D depth (on T-LESS), 3DGS, and CAD gives comparable recognition (tied on HOPE, within 1.6 points on T-LESS); 3DGS is only a convenient route to a point cloud. Second, the payoff is governed by how recognizable the shape is: on shape-distinctive househo

arXiv:2609.05401v1 Announce Type: new Abstract: Vision-language models are increasingly used as reward functions for robotic learning, but this role requires paraphrase invariance: the same trajectory should receive the same reward under semantically equivalent goal descriptions. We show that current VLM reward models often violate this property. Paraphrasing the instruction alone can substantially change predicted progress scores, and can even flip identical robot behavior between failure and success. To measure this failure mode, we introduce ROBORMBENCH, a benchmark with 2,390 real-robot trajectories, ground-truth progress labels, and 21,673 verified paraphrases spanning lexical, syntactic, and action-goal rewrites. Across proprietary and open-source VLMs, paraphrase-induced instability is widespread and severe, grows under more divergent rewrites, and is not reliably reduced by scale or explicit reasoning. Dedicated reward models trained with trajectory-grounded supervision are sub

arXiv:2609.05376v1 Announce Type: new Abstract: Visuomotor imitation policies can achieve high performance under in-distribution visual conditions yet fail when visually similar objects or receptacles are introduced. We study this behavior as a problem of conditional visual grounding: the visual target required for successful control changes with the manipulation phase and, in more complex tasks, with the observed task state. Using Action Chunking with Transformers (ACT), we systematically introduce distractor objects and receptacles with controlled color and shape similarity and localize failures to picking and placement. We find that distractor sensitivity is specific to both the type of visual similarity and the manipulation stage. Guided by this diagnosis, we evaluate distractor augmentation, phase-dependent attention regularization, and appearance-based visual prompting as complementary interventions for improving target selection while preserving spatial information required for

arXiv:2609.05369v1 Announce Type: new Abstract: Vision-language-action (VLA) models can execute short manipulation skills, but remain brittle in long-horizon procedures requiring persistent task state, dependency-aware reasoning, conditional decisions, and reliable grounding. We investigate a neuro-symbolic framework that combines learned VLA control with explicit task graphs and multimodal procedural memory. Task graphs encode action dependencies, valid transitions, and branch conditions, while memory maintains the active step, completed actions, textual context, and task-relevant visual evidence. Together, these structures guide object selection, destination grounding, subgoal dispatch, and verification of expected state transitions. Human demonstrations provide additional spatial and temporal guidance through gaze or saliency cues. To isolate their effect on policy learning, our initial study bypasses cross-view gaze transfer and directly annotates pseudo-gaze in robot-view teleoper