Robotics
Bounded-Fidelity Sim-as-Demo-Stage: Mocap Handoff for Governance Benchmarks
Xue Qin, Simin Luan, Cong Yang et al. (+1 more)
Sim-to-real research pursues physics fidelity as a primary objective: simulators are judged by how closely they reproduce real-world contact dynamics. For governance benchmarking of LLM-driven
robots, where the simulator demonstrates that an admission/policy/contract/audit pipeline behaves correctly, contact fidelity at object handoffs (grasp, carry, place) becomes a liability: contact-force integration noise injects audit-chain divergence that is structurally unrelated to the governance property under test. We propose bounded-fidelity sim-as-demo-stage, a design pattern that suppresses contact physics within explicitly bracketed handoff envelopes while preserving full dynamics elsewhere. The construction uses MuJoCo's mocap-body primitive driven by a 220-line Python adapter that the governance bridge invokes via structured intents. We formalise audit-chain stability as byte-equality of the hashed event log across replays and identify two structural envelope properties that imply it. Across N=1000 replays per posture, the mocap variant produces one distinct audit-chain hash (1000/1000 byte-identical; Wilson 95% CI [0.997, 1.000]); the contact-force baseline produces 584 distinct hashes (993/1000 diverged; CI [0.987, 0.998]). A timestep sweep (1, 2, 5, 10 ms) shows the divergence is structural, not a tuning artefact: it stays at 0.985 at every timestep. Envelope-edge timing jitter (+/-10 simulation steps, 1,400 replays) produces 0 divergence, and audit chains remain byte-equal across K in {1, 2, 3} sequentially handed-off objects (1,500 replays) with sub-linear per-pick-and-place overhead. The pattern gives
benchmark designers audit-chain reproducibility at near-zero engineering cost; we also map where it is harmful (sim-to-real validation, policy training, contact-rich tasks) so it is not mis-deployed.
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All authors Xue Qin, Simin Luan, Cong Yang, Zhijun Li
Robotics · Cross-listed from cs.CR
Xue Qin, Simin Luan, Cong Yang et al. (+1 more)
Robot capability marketplaces, the "app store for
robot skills," are emerging as the deployment vector for LLM-driven
robot fleets. The default cloud-native answer to "is this package safe to install?" is centralised PKI: one certificate authority, one transparency log, one root of trust. We argue this is the wrong model for
embodied robot fleets, where operators face heterogeneous regulatory regimes, air-gapped deployments, tiny operator headcounts, and physical-world consequences for trusting the wrong publisher. We present
federated trust: each deployed bridge maintains its own local trust directory of acceptable signers; signers identify themselves with a public key embedded in a detached Ed25519 signature envelope; install-time verification is a local set-membership check rather than a network round trip to a certificate authority. The cryptographic primitives are deliberately standard (Ed25519 detached signatures and SSH-style trust files); the contribution is the architectural commitment that this composition fits
embodied robot fleets specifically. We implement the model in a runtime governance layer with a five-subcommand CLI, a registry server, a per-bridge install gate, and 80 tests. A multi-deployment evaluation shows the same registry stream producing divergent install verdicts on bridges with different trust directories, the load-bearing design property. Across 5000
adversarial trials, the strict-mode gate rejects 100% of rogue-publisher, tampered, forged, and revoked-signer attacks, and 96.6% of downgrade attempts under a minimum-version pin extension. A same-hardware comparison against Sigstore-Cosign and python-TUF locates
federated trust's per-verify cost between the two and its per-publisher storage footprint below both.
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All authors Xue Qin, Simin Luan, Cong Yang, Zhijun Li
Robotics
A Biomimetic Myoelectric Tentacle Prosthesis with Sensorless Object Detection and Vibrotactile Feedback
Gabrielle Marion, Olivier Lecompte, Amandine Gesta et al. (+2 more)
This paper presents the design and evaluation of a myoelectric tentacle-shaped prosthesis integrating electromyographic (EMG) control, sensorless object detection, and vibrotactile feedback. The objective was to develop a responsive and intuitive assistive device that adapts to various object shapes while providing sensory feedback to the user. The system relies on EMG signals to control the motion of a flexible, biomimetic structure whose curling geometry follows a logarithmic spiral, enabling it to coil around objects. To ensure stable control, the EMG signal is normalized and filtered, and a threshold-based method identifies user intention. Object contact is detected through a slope-based analysis of motor current, eliminating the need for external sensors, and a haptic feedback strategy based on cumulative vibrotactile stimulation conveys spatial information about the tentacle's configuration. The system was evaluated through quantitative and qualitative tests. The results demonstrate a low response time (77 ms on average), enabling smooth real-time interaction; an object-detection success rate above 90%, confirming robustness despite EMG variability; and an effective haptic feedback strategy that allowed users to reliably identify the folding zone of the tentacle. The proposed biomimetic design promotes further investigation of expressive artificial limbs by prioritizing expressive functionality over adherence to a predefined, anthropomorphic form factor.
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All authors Gabrielle Marion, Olivier Lecompte, Amandine Gesta, Parsa Maghsoudloo, Abolfazl Mohebbi
Robotics
A Comprehensive Survey and Systematic Real-World Evaluation of Embodied Vision-and-Language Navigation
Liuyi Wang, Kai Sheng, Zongtao He et al. (+8 more)
Navigation is a fundamental capability of autonomous systems, yet most existing approaches rely on highly structured models and strong prior assumptions, limiting their robustness in open and uncertain real-world environments.
Vision-and-
Language Navigation (VLN) offers a promising direction by enabling
robots to integrate natural
language understanding with visual perception in a data-driven manner. Although VLN has attracted increasing research
attention, systematic methodological taxonomy and real-world validation remain limited. This survey presents a comprehensive review of VLN research. Specifically, state-of-the-art methods are organized along two orthogonal dimensions: action paradigms, including hierarchical and monolithic frameworks, and model paradigms, including discriminative and generative approaches. A critical analysis of their respective strengths and limitations is provided. Additionally, we conduct a systematic real-world evaluation of representative VLN system configurations on a physical robotic platform. Experiments across ten diverse real-world scenes show a substantial performance gap between simulation and real-world deployment under the tested configurations: a representative monolithic RGB-only method achieves 61% success in simulation but drops to 22% in real-world deployment, while a hierarchical framework achieves a higher real-world success rate of 51%, suggesting stronger robustness in our evaluation setting. Finally, we highlight key challenges in perception, decision-making, and control that must be addressed in future research.
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All authors Liuyi Wang, Kai Sheng, Zongtao He, Jinlong Li, Yongrui Qin, Haojie Dai, Xiangyi Wang, Jingwei Yang, Qingqing Yan, Chengju Liu, Qijun Chen
Robotics
CLAP: Direct VLM-to-VLA Adaptation via Language-Action Grounding
Yuri Ishitoya, Jeremy Siburian, Masashi Hamaya et al. (+3 more)
Vision-
language-action models (VLAs) inherit semantic capabilities from pretrained VLMs, yet large-scale post-training on
robot data and architectural modifications can reshape the backbone so extensively that it becomes difficult to isolate what the VLM contributes to control. Directly converting pretrained VLMs into VLAs with minimal architectural change offers a more transparent path to understanding how VLM capabilities transfer across model scales. The core obstacle is output-distribution mismatch: predicting actions as bare numeric
token sequences moves generation away from the VLM's pretrained
language distribution, degrading the capabilities we seek to preserve. To address this, we propose CLAP (Causal
Language-Action Prediction), which prepends each numeric action sequence with a natural-
language action description, causally conditioning precise action-
token prediction on a
language-action plan without modifying the backbone architecture. With single-epoch fine-tuning alone, 2B CLAP achieves 90.8% on LIBERO (+14.9 pt over VLA-0) and improves robustness on LIBERO-PRO under
language, object, and spatial perturbations. We will release CLAP at 0.8B, 2B, and 4B as an open-weight, multi-scale compact VLA family from a single VLM lineage, enabling controlled analysis of VLM-to-VLA capability transfer.
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All authors Yuri Ishitoya, Jeremy Siburian, Masashi Hamaya, Kuniaki Saito, Cristian C. Beltran-Hernandez, Mai Nishimura
Robotics · Cross-listed from cs.CE
Impedance-Guided Programmable Transmission of Localized Deformation in Modular Soft Metamaterials
Weiyun Xu, Daewon Hong, Zhi Zhao et al. (+2 more)
Soft metamaterials provide a promising platform for robotics, biomedical devices, and flexible electronics. The localized mechanical responses by nonuniform excitation are ubiquitous in soft materials, yet their controlled transmission across assemblies remains largely overlooked in metamaterial design, which critically constrains nontrivial functionalities with end-to-end and long-range deformation transmission. Here, we introduce an impedance-guided design framework that enables programmable transmission of localized deformation in modular soft metamaterials, achieving behaviors unattainable by intuitive design. By establishing a nonlinear model considering position-dependent interactions and integrating the concept of mechanical impedance within metamaterials, we regulate assembly-level transmission solely through unit-
cell topology optimization. The resulting framework enables effective synthesis of module families, allowing both homogeneous and heterogeneous assemblies to be custom-built with markedly enhanced transmission characteristics. Leveraging the highly combinatorial and extensible design space, we physically realize diverse on-demand displacement manipulation architectures, including obstacle-bypassing modular soft-metamaterial assemblies, defect-tolerant soft gripping, and
embodied signal processing. Beyond deformation programming, the reconfigurability and reassemblability of these soft modules can embed electric logic signals, enabling energy-efficient and low-latency information processing through compliant-switch-controlled mechanical LED displays and wearable finger-motion-sensing controllers. Our method provides fundamental insights into localized deformation transmission in modular soft metamaterials and establishes a scalable route toward
embodied-intelligence material systems, particularly for soft-metamaterial-centric actuation, sensing, and collective computing.
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All authors Weiyun Xu, Daewon Hong, Zhi Zhao, Rahul Dev Kundu, Xiaojia Shelly Zhang
Robotics
SplatCtrl: Perception-Action Coupling via Gaussian Scene Representations and Reactive Robot Control
Siddarth Jain and Ho Jin Choi
Robotic manipulators excel in structured environments but face substantial challenges in unstructured and dynamic settings. This paper presents SplatCtrl, a unified framework for real-time scene reconstruction and reactive
robot motion generation to enable collision-free robotic arm control in previously unseen and continuously changing environments. Building on 3D Gaussian Splatting (3D-GS), we introduce a hybrid voxel-based filtering and dynamic Gaussian relocation strategy that supports efficient scene reconstruction from RGB-D streams while accommodating environmental changes. For safe and reactive control, we further propose a method for deriving continuous signed distance functions from isotropic Gaussians, providing stable and differentiable collision probability estimates that bridge classical distance fields with the modern implicit representation. These continuous distance metrics are incorporated into control barrier functions, resulting in a unified perception-action coupling framework that supports smooth and reliable real-time motion generation in response to scene changes. Experimental validation in simulation, on physical
robot, and within shared human-
robot workspace demonstrates the framework's effectiveness, achieving integrated scene reconstruction and reactive control in uncertain, and dynamic environments.
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Robotics · Cross-listed from eess.SY
Adaptive MPPI with Online Disturbance Covariance Estimation: Provable Stability Tightening via Spatial Smoothing
Hyung-Jin Yoon and Hunmin Kim
We study Model Predictive Path Integral (MPPI) control for nonlinear systems with additive process disturbances whose covariance is unknown, spatially varying, and slowly time-varying. A mismatched disturbance covariance produces a persistent penalty in closed-loop stability certificates, while online estimation can reduce this penalty as data are collected. We propose a
cell-wise recursive covariance
estimator with spatial
diffusion and prove a finite-horizon error bound that separates
stochastic-approximation error, spatial-smoothing bias, and temporal-drift effects. The
diffusion kernel is chosen to be reversible with respect to the stationary visitation measure, making the
diffusion operator dissipative in the weighted Lyapunov analysis. We then substitute the resulting covariance estimate into the MPPI sampling distribution and derive an adaptive stability certificate with an explicit learning penalty. The main result is a payoff
theorem: after a computable crossover time, the adaptive controller achieves a strictly tighter certified stability bound than any fixed covariance choice whose mismatch exceeds the residual smoothing and drift allowance. Numerical experiments illustrate the
estimator convergence and the resulting stability-tightening effect.
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Robotics
FlowDAgger: Human-in-the-Loop Adaptation of Generative Robot Policies in Latent Space
Michael Murray, Daphne Chen, Simran Bagaria et al. (+7 more)
Pretrained generative
robot policies based on flow matching and
diffusion have achieved impressive results across a wide range of manipulation tasks. Yet real-world deployments routinely expose failure modes outside the pretraining distribution. Closing these gaps typically requires large-scale data collection or online reinforcement learning on physical hardware, which is impractical for rapid and safe adaptation. We present FlowDAgger, a sample- and compute-efficient method for adapting frozen generative
robot policies from human interventions in latent space. Our key idea is action inversion: each human expert action is mapped to the noise that would have produced it under the frozen base policy, using reverse-time integration followed by local refinement. The resulting inverted noise provides supervision for a lightweight latent policy that steers the base model at deployment time, enabling rapid skill acquisition while preserving its behavioral priors. We evaluate FlowDAgger in simulation and on real-world bimanual and single-arm manipulation, adapting both action-head VLAs and world-action models from a handful of interventions. FlowDAgger outperforms supervised fine-tuning and latent-space RL baselines and preserves pretrained skills on held-out tasks, offering a practical path for adapting
robot foundation models in the real world. Website: https://microsoft.github.io/FlowDAgger
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All authors Michael Murray, Daphne Chen, Simran Bagaria, Dean Fortier, Tess Hellebrekers, Galen Mullins, Harshavardhan Gajarla, Oier Mees, Maya Cakmak, Andrey Kolobov
Robotics
AgenticFocus: Object-Preserving Mixed Reality Synthesis from Human FPV Video for Dexterous Humanoid Learning
Iaroslav Kolomiets, Miguel Altamirano Cabrera, Artem Lykov et al. (+6 more)
Human
egocentric video is a scalable supervision source for
humanoid policy learning, but current pipelines struggle with hand-object occlusion, oversimplified motion, or specialized capture hardware. We introduce AgenticFocus, a Mixed Reality synthesis pipeline that converts ordinary first-person-view human
videos into
robot-trainable demonstrations by restoring occluded object geometry, reconstructing full-hand motion, and retargeting it to a
humanoid embodiment through camera-relative
alignment and layered compositing. The resulting
dataset pairs focused visual observations with synchronized
robot actions and states. AgenticFocus achieves lower trajectory error and smoother wrist motion than cross-embodiment baselines, with SPARC scores of -5.18 versus -5.56 and -6.05.
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All authors Iaroslav Kolomiets, Miguel Altamirano Cabrera, Artem Lykov, Jeffrin Sam, Dmitrii Iarchuk, Yara Mahmoud, Daniia Zinniatullina, Mikhail Konenkov, Dzmitry Tsetserukou
Robotics · Cross-listed from cs.GR
Programming-by-Example for Batch-Editing Collision Meshes in 3D Software
Gengyang Xu, Dongwei Xiao, Hengcheng Zhu et al. (+4 more)
As 3D software proliferates, software artifacts now extend beyond code and 2D user interfaces to include 3D assets. Among these assets, collision meshes are critical as they define the geometry used by physics engines for collision detection and physical interaction. Although existing tools can automatically generate collision meshes from visual meshes, they often fail to capture the intended interaction behavior. As a result, developers need to manually edit many heterogeneous collision meshes, a process that is time-consuming and challenging to scale. To address this problem, we present a neuro-symbolic program synthesis approach for batch-editing collision meshes. We formulate the task as a programming-by-example problem: given a family of collision meshes with the same editing intent and a small number of user demonstrations, our approach synthesizes a reusable program that captures the editing intent and applies it to non-demonstration meshes. We implement this in a tool named MeshForge, and evaluate it across 24 tasks on 600 collision meshes. MeshForge successfully synthesizes 23/24 tasks, requiring 2.2 demonstrations and 3.5 seconds of synthesis time on average.
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All authors Gengyang Xu, Dongwei Xiao, Hengcheng Zhu, Yiteng Peng, Wei Meng, Shuai Wang, Shing-Chi Cheung
Robotics
DexVerse: A Modular Benchmark for Multi-Task, Multi-Embodiment Dexterous Manipulation
Yunchao Yao, Zhuxiu Xu, Tianqi Zhang et al. (+12 more)
Building general-purpose dexterous manipulation policies requires
benchmarks that go beyond isolated tasks to systematically evaluate policies across diverse interaction modes, sensory conditions, and
robot embodiments. However, existing
benchmarks remain limited in task and data diversity, embodiment coverage, or controllable visual variation, hindering studies of cross-task and cross-embodiment generalization. We present DexVerse, a large-scale and modular
benchmark for dexterous manipulation. DexVerse includes 100 tasks spanning a broad range of manipulation skills, including object grasping and relocation, articulated-object interaction, functional tool use, bimanual coordination, non-prehensile control, contact-rich behaviors, multi-goal execution, and long-horizon multi-stage task completion. It supports 3
robot arms and 6 dexterous hands, and is extensible to new tasks, assets, and embodiments. To evaluate visuomotor generalization, DexVerse provides configurable visual variations in textures, background, lighting, and camera viewpoints. We further provide a VR-based teleoperation interface and 3,180 demonstrations with synchronized proprioceptive, RGB, depth, point-cloud, and state observations. We
benchmark representative methods, including
Diffusion Policy, DP3, OpenVLA, and $\pi_{0.5}$, across 19 tasks. Results reveal substantial challenges in task generalization and visuomotor robustness, establishing DexVerse as a promising testbed for general-purpose dexterous manipulation. Project page: https://ycyao216.github.io/DexVerse.site
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All authors Yunchao Yao, Zhuxiu Xu, Tianqi Zhang, Zixian Liu, Sikai Li, Zhenyu Wei, Feng Chen, Dihong Huang, Kechang Wan, Chenyang Ma, Shuqi Zhao, Shenghua Gao, Masayoshi Tomizuka, Yi Ma, Mingyu Ding
Robotics
ContactMimic: Humanoid Object Interaction via Contact Control
Xinyao Li, Xialin He, Runpei Dong et al. (+1 more)
Keypoint tracking alone is insufficient for object interaction tasks such as sitting on a chair, wiping a board, or pushing furniture, where the
robot can reach the correct pose without making meaningful physical contact with the object. We present CONTACTMIMIC, a learning framework that tracks explicit partlevel binary contact commands alongside keypoint trajectories. CONTACTMIMIC is made possible through the use of contact-following rewards and a trajectory augmentation scheme aimed at breaking the correlations between keypoint trajectories and contact labels. The resulting policy successfully decouples contact behavior from keypoint geometry, and achieves precise physical contact as well as contact-controllability (produce or suppress contact during deployment as desired). Simulation experiments across 10 diverse human-object interaction motions confirm that CONTACTMIMIC exhibits contact controllability that enables it to complete manipulation tasks without task-specific rewards, while also outperforming keypoint-only trackers on contact-relevant tasks. Ablations confirm the necessity of the proposed trajectory augmentation scheme and sim2real deployment validates contact controllability in the real world across 5 different motions.
Video results are available on https://lixinyao11.github.io/contactmimic-page/.
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All authors Xinyao Li, Xialin He, Runpei Dong, Saurabh Gupta
Robotics · Cross-listed from cs.GR
ARDY: Autoregressive Diffusion with Hybrid Representation for Interactive Human Motion Generation
Kaifeng Zhao, Mathis Petrovich, Haotian Zhang et al. (+3 more)
Generating realistic 3D human motions in real-time within interactive applications is key for animation, simulation, and
humanoid robotics. While recent offline motion generation approaches offer precise control via text and kinematic constraints, they lack the inference speed required for interactive settings. Conversely, existing online methods enable real-time synthesis but often sacrifice controllability or struggle with complex text semantics and long-horizon goals due to limited context windows. In this work, we introduce ARDY, a streaming generation framework that bridges this gap by enabling high-fidelity motion generation controllable via online text
prompts and flexible kinematic constraints. ARDY employs a hybrid representation that combines explicit root features with a latent body
embedding, balancing precise trajectory control with efficient generative learning. We propose a two-stage autoregressive
transformer denoiser that features variable history context and supports conditioning on flexible, long-horizon kinematic constraints. By training on a large-scale motion capture
dataset and being directly conditioned on text labels and kinematic constraints sampled from ground truth poses, ARDY natively learns controllable generation that supports online prompting and flexible long-horizon goals. Extensive evaluations on the HumanML3D
benchmark and the large-scale, high-fidelity Bones Rigplay
dataset demonstrate ARDY's high motion quality and constraint adherence, validating the efficacy of our key architectural decisions. Finally, we demonstrate the method's practical versatility through an interactive demo featuring dynamic text control, diverse keyframe pose constraints, path following, and interactive locomotion control via mouse and keyboard. Supplementary
video results, code, and model releases can be found at https://research.nvidia.com/labs/sil/projects/ardy/.
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All authors Kaifeng Zhao, Mathis Petrovich, Haotian Zhang, Tingwu Wang, Siyu Tang, Davis Rempe
Robotics
Learning Adaptive Solvers for Distributed Factor Graph Optimization on Matrix Lie Groups
Jaeho Shin, Maani Ghaffari and Yulun Tian
Modern robotic perception increasingly involves large-scale
geometric optimization problems distributed across multiple
robots or sessions. However, existing distributed solvers often depend on brittle hand tuning and primarily target rigid body pose graphs. To address this, we present DeepCORD, a learning-augmented framework for distributed factor graph optimization on general
matrix Lie groups. By unfolding a parallel and accelerated Riemannian optimizer into differentiable iterations, DeepCORD learns a self-supervised feedback policy that dynamically adapts solver parameters according to the optimization phase and communication status. The resulting method enables adaptive distributed optimization over
matrix Lie groups under both synchronous and asynchronous communication regimes. Extensive experiments on real-world $\mathrm{SE}$(3) pose graph optimization and $\mathrm{SL}$(4) projective submap
alignment show that our method achieves lower objective values than existing distributed baselines on most
benchmarks across realistic operating scenarios.
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Robotics · Cross-listed from cs.LG
Latent Memory Palace: Reasoning for Control as Autoregressive Variational Inference
Chuning Zhu, Eva Xu, Jose Barreiros et al. (+3 more)
Human decision-making is highly flexible – some actions are taken immediately; others require longer deliberation.
Language models have exhibited a similar capacity for adaptive "
reasoning." However, transferring this capability to continuous control policies has been challenging, as directly
reasoning in
language space may lack the granularity for spatial understanding and precise motions. In this work, we show that
reasoning for control policies can emerge by organizing information in an autoregressive latent space reminiscent of a
memory palace, where
retrieval is iterative and adaptive. Our method, Latent
Memory Palace (LMP), formulates
reasoning as variational inference with an autoregressive latent distribution. We derive a latent-space reinforcement learning technique to tractably optimize its variational lower bound. The resulting policy, LMP-$\pi$, achieves strong empirical performance in simulation and real-world domains while exhibiting interpretable, adaptive allocation of test-time compute. We further show that the same framework yields a variable-length action tokenizer, LMP-$\texttt{tok}$, which significantly improves the performance of downstream autoregressive policies. Together, these results present a new perspective on latent
reasoning for control through the lens of variational inference.
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All authors Chuning Zhu, Eva Xu, Jose Barreiros, Krishnan Srinivasan, Paarth Shah, Abhishek Gupta
Robotics
Native Video-Action Pretraining for Generalizable Robot Control
Qihang Zhang, Lin Li, Luyao Zhang et al. (+26 more)
The advent of
video-action models offers a promising path for
robot control. Nevertheless, we argue that repurposing
video generative models designed for digital content creation is inherently inadequate for physical environments. To bridge this gap, we present LingBot-VA 2.0, a
video-action foundation model built from the ground up for embodiment. Four core design principles showcase its
evolution from LingBot-VA. (1) Departing from traditional reconstruction-focused VAEs, we introduce a semantic visual-action tokenizer, which aligns visual representations with both semantics and actions, improving instruction following and action precision in subsequent policy learning. (2) Given the strictly causal nature of temporal dynamics, we adopt a causal pretraining paradigm, training from scratch to circumvent the catastrophic forgetting that frequently occurs when adapting bidirectional architectures. (3) To meet the demands of high-frequency inference, our model employs a sparse MoE backbone, expanding model capacity without compromising efficiency. (4) Real-time closed-loop control is realized through an enhanced asynchronous inference scheme, which predicts future latents in parallel with action execution while re-grounding each rollout on the latest observation via learned forward dynamics. Real-world deployment validates LingBot-VA 2.0 as a robust foundation model, as evidenced by its few-shot generalization across complex manipulation tasks.
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All authors Qihang Zhang, Lin Li, Luyao Zhang, Shuai Yang, Yiming Luo, Shuaiting Li, Ruilin Wang, Junke Wang, Jiahao Shao, Gangwei Xu, Jiaming Zhou, Yishu Shen, Yudong Jin, Fangyi Xu, Shuailei Ma, Jiaqi Liao, Guanxing Lu, Zifan Shi, Yongkun Wen, Yujie Zhao, Weixuan Tang, Xinyang Wang, Chaojian Li, Jiapeng Zhu, Ka Leong Cheng, Nan Xue, Xing Zhu, Yujun Shen, Yinghao Xu
Robotics
A New Human-Likeness and Comfort Index for Robot Movements Along Prescribed Paths
Rosanna Coccaro, Enrico Ferrentino, Antonio Parziale et al. (+2 more)
As human-
robot interaction rapidly spreads in numerous fields, the subject of
robot acceptance gains increasing importance. Visual similarity to the human body, as occurs for
humanoids, is generally not enough to ensure acceptance in physical interaction, as acceptance directly links to comfort and ergonomics, which are measured in terms of the quality of the
robot movement perceived by the human. This paper discusses the connection between comfort and similarity of the
robot movement to the human one. By considering the kinematic characterization of human movement, this paper focuses on the time laws of such movements, wherein the end-effector path is prescribed. Based on the lognormality principle for modeling human movements, a human-likeness index is defined and used to provide an a priori characterization of trajectories. Such an index can be used to evaluate the performance of trajectory generation
algorithms in producing human-like movements before they are actually executed. For validation purposes, 68 subjects are required to judge their comfort. The results of three experimental campaigns involving a physical interaction with a
robot demonstrate a globally consistent trend between the preference in terms of perceived comfort and the distribution of the suggested human-likeness index.
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All authors Rosanna Coccaro, Enrico Ferrentino, Antonio Parziale, Angelo Marcelli, Pasquale Chiacchio
Robotics
FabriVLA: A Lightweight Vision-Language-Action Model with Conformal Action Chunk Uncertainty
Shiyuan Yang, Borong Zhang, Jizheng Zhang et al. (+5 more)
Vision-
Language-Action (VLA) models have become a leading paradigm for general purpose robotic manipulation, but their computational cost and limited uncertainty awareness hinder practical deployment. We present FabriVLA, a lightweight VLA that fuses shallow and intermediate VLM layers to preserve fine-grained visual features, and gates self-
attention among action
tokens so that its flow matching head admits inter step structure only as far as training warrants. Trained end-to-end in a single stage, FabriVLA reaches a state-of-the-art 90.0% average success on Meta-World MT50 with only 0.88B parameters. We further introduce Joint Conformal Action Chunk Calibration (JCAC), a post-training method that augments a frozen policy with a lightweight residual scale head. From a single policy query, JCAC turns a learned elementwise error scale into a set that covers the whole executed action prefix at a user chosen confidence level, 3.3$\times$ tighter in mean radius than an unconditional conformal set. On LIBERO-Safety, these bounds rank rollouts by risk before execution, supporting risk ranked review. Together, FabriVLA and JCAC provide a lightweight and auditable framework for multi-task manipulation with calibrated action uncertainty.
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All authors Shiyuan Yang, Borong Zhang, Jizheng Zhang, Zhijia Tao, Junfei Guo, Donglai Ran, Xu Bian, Qingbiao Li
Robotics · Cross-listed from cs.MA
Early to Share, Late to Save: Synchronisation-Driven Communication Gating in Bandwidth-Constrained Cooperative VLN
Arav Gupta, Nivedan Yakolli and Avinash Gautam
Most cooperative
Vision-
Language Navigation (VLN) methods assume unlimited communication, not considering real-world applications where bandwidth is restricted and information efficiency is critical. We introduce bandwidth-constrained cooperative VLN and propose hindsight gating: a lightweight supervised gate that labels communication-critical steps post-hoc from
navigation failures, avoiding the high variance of REINFORCE. Contrary to the intuition that
agents should communicate when uncertain, we observe a consistent counter-intuitive pattern: trained gates fire predominantly in early episode steps and more often when
agents are confident, across all budget levels ($B \in \{1,3,5\}$). We explain this through recurrent hidden-state
alignment: early communication injects grounded trajectory representations that persist and compound through subsequent Gated Recurrent Unit (GRU) updates, achieving $+0.072$ cumulative
alignment gain with $B{=}3$ transmissions, approaching unconstrained communication ($+0.078$) at 260% greater
alignment efficiency than random gating ($+0.020$) and 320% greater efficiency than
entropy-based gating ($+0.017$). Our results establish a new communication regime for bandwidth-limited
embodied agents: synchronise representations early, navigate independently later. Our codebase is available at: https://github.com/AravG13/bandwidth-constrained-cooperative-vln
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Robotics
Harness VLA: Steering Frozen VLAs into Reliable Manipulation Primitives via Memory-Guided Agents
Yixian Zhang, Huanming Zhang, Feng Gao et al. (+14 more)
Language-conditioned manipulation requires both precise contact-rich control and robust
reasoning over
language, scenes, and long horizons. End-to-end
Vision-
Language-Action (VLA) models provide strong local visuomotor skills, but they are trained on in-distribution task trajectories and often fail under deployment perturbations such as semantic retargeting, goal re-binding, spatial-layout shifts, and unstable local contacts. LLM coding
agents provide complementary semantic and compositional
reasoning, but purely analytic primitives struggle with irregular grasping, constrained placement, and articulated-object interaction. We present Harness VLA, a
memory-augmented agentic framework that exposes a frozen VLA as a retryable contact-rich primitive and composes it with a small fixed library of analytic primitives for grounding, staging, transport,
navigation, and release. Rather than expanding the skill library, the harness learns the operating range of these fixed primitives from task-specific execution traces, global success rules, and failure models. By lifting semantic re-grounding, non-contact execution, and VLA re-staging to the planner while reserving the frozen VLA for local contact-rich phases, Harness VLA extends pretrained VLAs beyond their original trajectory distribution without finetuning. Across perturbed tabletop, household kitchen, and clean-to-randomized bimanual manipulation, Harness VLA improves over the strongest relevant baselines by 38.6 and 25.4 percentage points on LIBERO-Pro and RoboCasa365, respectively, and reaches 58.4% on RoboTwin C2R. Real-world demonstrations on dual-Franka
robots further show target redirection, grasp recovery, and new task compositions with the same frozen VLA. Code is available at https://github.com/RLinf/RPent.
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All authors Yixian Zhang, Huanming Zhang, Feng Gao, Xiao Li, Zhihao Liu, Yi Nie, Chunyang Zhu, Jiaxing Qiu, Yuchen Yan, Jiyuan Liu, Wenhao Tang, Jiaji Rao, Zhengru Fang, Changxu Wei, Yu Wang, Wenbo Ding, Chao Yu
Robotics · Cross-listed from cs.CV
Swapping Faces, Saving Features: A Dual-Purpose Pipeline for Pedestrian Privacy in ITS
Roba H. Farouk and Catherine M. Elias
Large-scale and diverse
datasets are needed to train AI models to take real-time decisions for autonomous vehicles (AVs), an intelligent transportation system (ITS) application. Pedestrian intention and trajectory prediction are critical models used in AVs, requiring
datasets involving diverse pedestrian images. Unrestricted access to these
datasets imposes serious security risks, like identity theft and pedestrian tracking. The challenge is to apply
privacy preservation procedures while maintaining the image attributes needed to train the models. Existing
privacy methods may preserve the pedestrian's
privacy, but degrade the image usability, which hinders the models' effectiveness. This work's focus is to implement a five-stage pipeline to protect pedestrians'
privacy through face swapping while keeping the essential facial attributes intact. It should be tailored to satisfy the
privacy needs of the Egy-DRiVeS
dataset. Moreover, Roop and Ghost-v2 face-swapping models are evaluated. Provenly, Roop outperforms Ghost-v2 in various aspects, as will be discussed. Consequently, Roop is the face-swapping model to be used in the pipeline to strike the balance between pedestrian
privacy via identity concealment and data usability via facial attribute preservation.
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Robotics
On Exploring Input Resolution Scaling For Anytime LiDAR Object Detection
Ahmet Soyyigit, Shuochao Yao and Heechul Yun
Making tradeoffs between execution latency and result utility (i.e., anytime computing) for adapting to dynamic operational requirements has been shown to enhance the performance of cyber-physical systems. In this work, we focus on enabling anytime computing for deep neural networks (DNNs) that process
LiDAR point clouds for 3D object detection. We propose a novel method that enables multi-resolution inference for models that process point clouds as pillars or voxels, allowing the input to be dynamically scaled and processed at the resolution needed to meet timing requirements. Importantly, our
memory-efficient approach requires the deployment of only a single DNN model, avoiding the need to deploy multiple models, each trained for a different input resolution. We also introduce a deadline-aware scheduler that selects the highest possible resolution for any given input by accurately predicting the execution time for all possible resolutions at runtime, which is challenging due to the irregularity of
LiDAR point clouds. Experimental results on the nuScenes autonomous driving
dataset demonstrate that our method significantly outperforms existing anytime computing approaches for
LiDAR object detection. Finally, we deploy our approach in a simulated autonomous driving system, where it consistently enables collision-free
navigation while avoiding unnecessary stalls caused by environmental complexity.
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Robotics · Cross-listed from cs.CL
Large-Language-Models-as-a-Judge in Theory-Agnostic Adaptive Metric-Alignment for Prototypical Networks in Personality Recognition
Jing Jie Tan, Ban-Hoe Kwan, Danny Wee-Kiat Ng et al. (+6 more)
Personality recognition has traditionally been constrained by theory-dependent formulations, where models are trained to fit predefined psychological taxonomies rather than uncovering shared underlying behavioral structure. This limits generalization, as personality itself is better understood as theory-invariant, while existing annotations reflect only partial and sometimes inconsistent views of the same latent traits. In this work, we introduce JAM ((J)udge for (A)daptive (M)etric-
Alignment), a theory-agnostic framework that shifts learning from adapting to predefined personality theories toward discovering unified latent pseudo-facets that capture shared psychological structure. Rather than constraining the model to any personality taxonomy during training or inference, the framework learns generalizable psychological representations and can infer an individual's latent psychological profile directly from the textual samples, without requiring theory-specific labels. JAM achieves this through an
Attention-Pooled Graph Prototypical Network that learns structured representations via clustering in
embedding space, together with a Cross-Theory Harmonization (CTH) approach that integrates (i) Human-Guided Linkage and (ii) Machine-Induced Consensus to unify heterogeneous
datasets without relying on predefined labels. To further improve robustness and data quality, we incorporate an LLM-as-a-Judge mechanism operating in two configurations, (i) LLM-before-the-loop and (ii) LLM-in-the-loop which identifies ambiguous samples to guide adaptive metric learning. Experiments show that JAM improves cross-framework generalization and performance, establishing a strong step toward theory-agnostic personality inference and supporting low-resource personality theories. The related code repository, model weights, and artifacts are available at https://research.jingjietan.com/JAM
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All authors Jing Jie Tan, Ban-Hoe Kwan, Danny Wee-Kiat Ng, Yan-Chai Hum, Shih-Yu Lo, Po-An Chen, Noriyuki Kawarazaki, Kosuke Takano, Anissa Mokraoui