Robotics
Obstacle-Aware Autonomous Coverage and Navigation for Outdoor Robots
Leonardo Gargani, Matteo Frosi and Matteo Matteucci
Long-duration outdoor coverage with autonomous platforms remains challenging beyond classical planning: deployments face localization drift in open spaces, obstacles in cluttered sites, controller feasibility in turn-heavy maneuvers, and persistent autonomy with energy management. We propose a unified ROS 2 architecture for single-
robot outdoor coverage: a dual-
antenna RTK-GNSS fused in an EKF keeps both position and heading accurate across long missions; three controller-aware refinements extend a mature coverage planner; a Nav2-based behavior-tree mission executive coordinates multi-goal execution, layered recovery, cost-aware goal management, and autonomous docking for return-to-charge. In real-world trials across five outdoor areas with varying geometries and obstacle densities, the
robot completed every coverage route, sweeping 93.1% to 96.1% of the planned coverage area.
Read the full abstract ▾
Robotics
Scalable Rao-Blackwellized Online Planning for High-Dimensional POMDPs
Jiho Lee, Nisar Ahmed, Kyle Hollins Wray et al. (+1 more)
Online planning under uncertainty remains a fundamental challenge for robotic systems operating in partially observable environments with high-dimensional state spaces. While sampling-based POMDP solvers enable approximate decision-making in large or continuous domains, their performance degrades as belief dimensionality increases due to the high variance inherent in Monte Carlo-based estimation. In this work, we extend the Rao-Blackwellized online POMDP (RB-POMDP) framework to improve its generalizability in high-dimensional settings through hybrid continuous-discrete belief representations. By analytically propagating uncertainty associated with marginalized state components during tree-based planning, the proposed approach reduces sampling-induced variance in value estimation. We demonstrate the effectiveness of this framework in a robotic search-and-rescue task by integrating it with FastSLAM 2.0. Experimental results show that the proposed planner achieves higher cumulative rewards using significantly fewer particles and planning simulations than purely sampling-based methods under equivalent computational budgets. These results suggest that structured high-dimensional robotic problems admitting tractable sufficient statistics can be effectively leveraged within the RB-POMDP framework for computationally feasible online decision-making.
Read the full abstract ▾
All authors Jiho Lee, Nisar Ahmed, Kyle Hollins Wray, Zachary Sunberg
Robotics
Integrating Traffic Noise Emission Modelling into Variable Speed Limit Control
Jiawen Meng, John Pravin Arockiasamy and Alexey Vinel
Road traffic noise remains a major environmental challenge, yet most speed management strategies are static and do not respond to short-term variations in traffic noise emissions. Although variable speed limit (VSL) systems are widely deployed for safety and congestion mitigation, traffic noise is rarely treated as an explicit operational control objective. This paper proposes a noise-aware VSL framework that integrates aggregated traffic-state estimation with a simplified CNOSSOS-EU-based emission indicator. A stage-based controller with time-varying reference thresholds dynamically adjusts discrete speed-limit levels in response to estimated emission conditions. The framework is evaluated using microscopic traffic simulation calibrated with empirical motorway data and replicated across multiple
stochastic realisations. Over a 24-hour evaluation period, the adaptive strategy reduces the receiver-based equivalent sound level by 2.9 dB(A) relative to unrestricted traffic conditions, while maintaining an average vehicle speed approximately 11.3 km/h higher than a permanently imposed low-speed regime. Period-wise analysis shows that speed reductions are activated selectively when emission levels approach calibrated targets, rather than enforcing a constant intermediate limit. Traffic stability indicators reveal moderate increases in speed variability compared with unrestricted operation, but substantially lower braking intensity than under uniform low-speed enforcement. These results demonstrate the feasibility of integrating environmental performance indicators into operational speed control, providing a practical complement to conventional infrastructure-based noise mitigation measures.
Read the full abstract ▾
Robotics
EmbodiedSkills: World Action Model-augmented Cascaded Skills for Vision-Language-Action Agent
Wei Wang, Wenqiao Zhang, Yutong Lin et al. (+14 more)
Vision-
language-action (VLA) policies generate executable
robot actions, but plausible proposals can have different consequences for the task. We present EmbodiedSkills, a skill-based
agent framework that augments frozen VLA policies with world action models for consequence-aware action selection. The framework defines reusable skills for scene observation, context encoding, candidate generation, consequence prediction, outcome evaluation, candidate comparison, and action execution. Five stages organize their input-output dependencies in a shared feedback loop, accommodating both continuous action chunks and pick-and-place primitives. At each decision point, independently trained world action models predict the latent consequences of a common set of policy proposals. Selection combines their outcome scores and prediction disagreement with a lightweight comparator that estimates the benefit of an alternative from agreement in its predicted effects. The predictive and comparison modules learn from matched-state candidate branches, while the action policy and pretrained observation encoders remain frozen. On MetaWorld MT50 and CLIPort, EmbodiedSkills improves success rates from 76.28% to 78.64% and from 33.94% to 36.83%, respectively. An additional CALVIN evaluation with the frozen FLOWER policy increases five-task-chain success from 77.10% to 77.90%. These results demonstrate the applicability of consequence-aware selection across different policy backends and action representations.
Read the full abstract ▾
All authors Wei Wang, Wenqiao Zhang, Yutong Lin, Yuqian Yuan, Tianwei Lin, Jinhao Mao, Zhenxuan Fan, Mingjian Gao, Yang Dai, Wentong Li, Zheqi Lv, Zheng Dong, Yingjie Niu, Jiaqi Zhu, Jun Xiao, Chao Li, Yueting Zhuang
Robotics · Cross-listed from cs.AI
Dual Process Motion Planning
Jiayi Yan, Francesco Fabiano and Alessandro Abate
Robotic systems are deeply embedded in both industry and everyday life, where they are expected to act with speed, precision, and reliability. Classical control and planning methods have long delivered strong guarantees, but often at the cost of computational efficiency and adaptability. More recently, learning-based approaches have shown promise in overcoming these limitations, enabling
agents to leverage experience to accelerate decision-making and address previously intractable problems. In this work, we bridge these two approaches through a neuro-symbolic perspective on nonlinear motion planning. Inspired by the Thinking Fast and Slow paradigm, we introduce a dual-process architecture that combines the strengths of robust
reasoning and learning. Our framework integrates state-of-the-art symbolic solvers as a “System-2” component with experience-driven “System-1” modules. A metacognitive controller dynamically orchestrates their interaction, selecting when to rely on fast intuition versus slower, more precise
reasoning. By evaluating the framework across diverse nonlinear
benchmark environments, we demonstrate that this architecture yields consistent gains in planning efficiency, accuracy, and generalization, while promoting reuse across tasks. The results suggest that tightly coupling learning with structured
reasoning offers a scalable path toward more capable and adaptive robotic systems.
Read the full abstract ▾
Robotics · Cross-listed from cs.CV
MeRoPE: Metric Rotary Position Embedding for Camera-Controlled Video Generation
Zhijian Qiao, Xinjiang Wang, Jiajie Chen et al. (+5 more)
In camera-controlled
video generation, geometry-aware positional encodings condition
tokens on camera extrinsics and per-
token viewing rays. Existing schemes, however, have a scale-dependent failure mode on real-world metric camera trajectories: homogeneous projective encodings cause
attention logits and feature norms to grow unbounded with physical translation baselines. We propose MeRoPE (Metric Rotary Position
Embedding), a norm-preserving relative camera encoding for
attention. MeRoPE encodes relative orientations between calibrated viewing rays with orthogonal rotation blocks, maps raw metric displacements into multi-frequency rotary phases, and adds a disparity-anchored correspondence prior along the epipolar arc. This design strictly preserves feature norms, bounds pre-softmax
attention logits regardless of the physical translation scale, and maintains exact invariance to global rigid coordinate changes. Across nuScenes and PanShot, which cover large-baseline trajectories and diverse camera optics, respectively, MeRoPE achieves stronger camera control than prior encodings, with the best consistency between generated camera motion and conditioning poses in both rotation and translation. Code will be made publicly available.
Read the full abstract ▾
All authors Zhijian Qiao, Xinjiang Wang, Jiajie Chen, Haoming Huang, Meng Li, Chih-Chung Chou, Jing Wang, Shaojie Shen
Robotics · Cross-listed from cs.LG
REFACTOR-VLA: Unsupervised Library Learning of Typed Motor Programs
Riyaaz Shaik and Chandru Venkataraman
Most
vision-
language-action (VLA) models – OpenVLA, $\pi_0$, RT-2, RDT-1B – are monolithic: they emit raw motor commands or short action chunks without organizing behavior into reusable abstractions, so they degrade on long-horizon tasks and resist interpretation. Existing skill-discovery methods sidestep the core question of when two action sequences are behaviorally equivalent, either clustering contrastive
embeddings or delegating the judgment to a
language model uncalibrated to the
robot's dynamics. We introduce REFACTOR-VLA, a wake/sleep system for learning reusable skills. Its sleep phase clusters motor-program fragments under a Behavioral-Equivalence Kernel (BEK) computed from rollouts of a learned latent world model $M_\phi$; its wake phase emits typed lambda terms over a Hindley–Milner-inspired vocabulary, consumed by a library-conditioned rectified-flow action decoder. Abstractions are admitted only if they pass Minimum Description Length and return-preservation gates. On LIBERO we report two findings. First, enlarging the world model from 188M to 430M parameters worsened performance on 4 of 4 suites, so capacity alone does not help. Second, the training objective matters far more: adding an auxiliary supervised contrastive (InfoNCE) loss during world-model warmup substantially improves sleep-phase clustering, giving Normalized Mutual Information at $n=3$ seeds of $0.462 \pm 0.021$ (object), $0.867 \pm 0.025$ (spatial), $0.915 \pm 0.013$ (goal) and $0.754 \pm 0.010$ (LIBERO-10), and beating the strongest published baseline on all 4 suites by a mean $\Delta = +0.184$. Across providers ($n=12$) the 95% bootstrap confidence interval for mean pairwise NMI is $[0.683, 0.729]$ (mean $0.705$). The sleep phase also yields the first real-LIBERO task-
language library: the decoder uses 2 of 3 admitted abstractions and rewrites all 256 sampled demonstrations.
Read the full abstract ▾
Robotics
On Global Regulatability of Robot Manipulators by Classical PID
Cheng Zhao, Jingru Zhu and Lei Guo
A long-standing open problem in
robot manipulator control is whether global regulation can be achieved by classical PID control. This paper provides an answer to this question for classical PID controllers with triple parameters (k_p,k_i,k_d) in R^3. We find and prove that for one-degree-of-freedom manipulators, the classical PID control guarantees global stability and asymptotic regulation under standard structural assumptions, and further derive explicit quantitative design conditions for the PID gains. However, for multi-degree-of-freedom cases, we can construct a
robot manipulator satisfying the same structural assumptions for which no choice of PID gains (k_p,k_i,k_d) can achieve global asymptotic regulation. These results provide a fundamental understanding of the abovementioned open problem, revealing both the fundamental capability and intrinsic limitation of the classical PID control for
robot manipulator dynamics.
Read the full abstract ▾
Robotics
DNC-IMM: Early Lane-Change Intention Recognition via Neural Calibration Based on Driving Context Information
Woong-Chan Byun and Seung-Hyun Kong
Early recognition of lane-change intention is essential for proactive decision-making in autonomous driving and advanced driver assistance systems. This paper proposes a Dual Neural-Calibrated Interacting Multiple Model (DNC-IMM) that improves adaptability to driving context while preserving the probabilistic structure and interpretability of a conventional IMM. The proposed method encodes driving-context information, including target-vehicle motion, gaps to surrounding vehicles, and relative velocities, with a neural network that calibrates both the transition-probability
matrix and measurement likelihoods. The final intention is determined from the calibrated IMM mode posterior rather than from a separate direct classifier. Experiments on the highD
dataset demonstrate that the proposed method reliably recognizes lane-change intentions before lane crossing and provides particularly strong performance at the earlier 2-3 s prediction horizons.
Read the full abstract ▾
Robotics
Adaptive Depth-Map-Guided Bundle Adjustment for Correspondence-Free Multi-View Point Cloud Registration
Yiran Zhou, Yingyu Wang, Shoudong Huang et al. (+1 more)
Robotic processing of irregular steel scrap requires dense 3-D measurement to replace manual visual assessment in hazardous cutting workcells. The reconstructed map is used to estimate piece dimensions, boundary geometry, feasible preheating and cutting regions, and collision-aware torch paths. The reconstruction errors therefore propagate directly to downstream measurement and planning. Existing multi-view registration methods commonly rely on feature extraction and data association to establish correspondences between views. In workcells with smooth metallic surfaces, repeated structures, occlusions, and partial overlaps, however, wrong correspondences may be established, leading to inaccurate pose estimation and distorted reconstruction. This paper presents an adaptive layered depth-map-guided bundle adjustment framework for correspondence-free multi-view point cloud registration. The scene is represented by a global 2.5-D grid, where each
cell can adaptively maintain multiple depth hypotheses. Raw depth observations are directly projected into the global map to form depth constraints without explicit feature correspondences. At grid
cells where multiple surfaces produce conflicting depths, a softmax-based layer assignment links each observation to compatible depth hypotheses. The resulting nonlinear least-squares formulation jointly refines sensor poses and the layered depth map, with correspondences implicitly induced by the depth-map representation and projection model. Experiments on self-collected industrial
datasets show that the proposed method achieves consistently competitive reconstruction accuracy while maintaining robustness and low computational cost in challenging industrial scenarios. We release the open-source code implementation at: https://github.com/YiranZhou-Robotics/ADM-BA.git
Read the full abstract ▾
All authors Yiran Zhou, Yingyu Wang, Shoudong Huang, Liang Zhao
Robotics
Accelerating Reinforcement Learning via MPC Solver-Gradient Guidance for Weights-varying MPC
Baha Zarrouki, Arslan Thobani, Jasper Hoffmann et al. (+6 more)
In Model Predictive Control (MPC), cost-function weights shape closed-loop behavior, yet changing conditions often make fixed parametrizations suboptimal and motivate context-dependent online adaptation. Learning such policies is difficult because behavior depends implicitly on numerical MPC solutions, producing nonlinear, potentially nonsmooth, long-horizon dependencies on policy parameters. This creates a bias-variance tradeoff: Reinforcement Learning (RL) optimizes realized closed-loop return from environment samples but is sample-inefficient, whereas Gradient-Based Policy Learning (GB-PL) uses low-variance solver gradients from differentiable MPC to optimize surrogate losses on predicted trajectories but can be biased under model mismatch. We propose Solver-Gradient Guided Reinforcement Learning (SG-RL), a solver-sensitivity augmentation for RL-based online MPC cost-weight adaptation. SG-RL keeps sampled closed-loop return as the objective and uses bounded solver-derived gradients as auxiliary guidance to improve stability and sample efficiency. We instantiate SG-RL in Proximal Policy Optimization (PPO) with four modular
algorithms that inject solver-gradient guidance into actor-update scaling, policy loss, advantage estimation, and value-function learning. On two full-scale autonomous racing platforms with intentional model mismatch, SG-RL reaches PPO's best closed-loop return with up to 70.6% fewer samples, outperforms GB-PL baselines by at least 54% in closed-loop return, and generalizes zero-shot to unseen environments.
Read the full abstract ▾
All authors Baha Zarrouki, Arslan Thobani, Jasper Hoffmann, Mattia Piccinini, Rudolf Reiter, Felix Jahncke, Sébastien Gros, Davide Scaramuzza, Johannes Betz
Robotics
HitMem: Hierarchical Temporal 3D Memory with Multi-Modal Context-Aware Retrieval for Dynamic Environments
Ruijie Tang, Chenye Zou, Guoquan Wu et al. (+3 more)
Executing long-term tasks in dynamic environments requires embodied
agents to maintain robust and adaptive 3D scene representations. However, most existing 3D
memory frameworks rely on static world assumptions. When objects are displaced by human activities or unobserved events,
agents encounter
memory-observation conflicts and often require costly
geometric recomputations or inefficient global re-exploration. To address this, we propose HitMem, a hierarchical temporal 3D
memory framework with a multi-modal context-aware
retrieval mechanism. Through continuous perception, HitMem unifies semantic and spatial information into a lightweight topological graph that captures support relationships, while a temporal decay mechanism dynamically regulates
memory activeness to mitigate the impact of stale representations. In addition, the multi-modal context-aware
retrieval mechanism defaults to filtering candidates using integrated semantic, spatial, and temporal
memory features, and activates a specialized two-stage
retrieval process when object displacement is detected. This process combines spatial constraints inferred from external
agent trajectories with semantic common sense grounded in class affinities, efficiently identifying high-probability candidate regions. Extensive evaluations on our constructed Dyna-THOR
benchmark demonstrate that HitMem significantly improves object relocation accuracy, reduces exploration costs, and enhances task execution performance in dynamic environments.
Read the full abstract ▾
All authors Ruijie Tang, Chenye Zou, Guoquan Wu, Jun Wei, Wei Chen, Jiaxin Zhu
Robotics
ProxPI: Proximal Prior Injection for Sampling-Based MPC under Learned-Prior Mismatch
Euncheol Im, Myotaeg Lim and Yisoo Lee
Combining learned policies with model predictive control can leverage learned task priors while retaining online adaptation to new objectives and constraints, but performance degrades when the policy is out of distribution. In policy-guided model predictive path integral (MPPI) control, a policy-centered warm-start approach centers the sampling distribution on the policy output. When the prior is mismatched, centering the sampling distribution on the policy output restricts exploration around an unsuitable solution and prevents recovery toward the task optimum. We propose Proximal Prior Injection (ProxPI), which retains nominal-centered MPPI sampling and incorporates the policy through a soft proximity cost. This matches the in-distribution performance of existing prior-injection schemes while enabling the optimizer to escape an inaccurate policy and recover vanilla MPPI-level performance. We theoretically show that re-centering on the prior discards the optimizer's correction at every update, whereas nominal-centered sampling retains it and converges to a solution set by both the task cost and the prior, and that this failure is not removed by a larger rollout budget. Simulations and real-
robot experiments demonstrate robust performance under both in-distribution and out-of-distribution tasks.
Read the full abstract ▾
Robotics
VerNav: Verifier-First Low-Latency Vision-and-Language Navigation
Zhixin Wang, Chengzheyi Yao, Leyuan Liu et al. (+2 more)
Vision-and-
Language Navigation (VLN) requires an
agent to navigate through unseen 3D environments according to natural-
language instructions. Explicit
reasoning can improve instruction understanding and semantic grounding, but autoregressive generation at every step accumulates large decision-stage latency over multi-step navigation. We propose VerNav, a verifier-first framework for low-latency LLM-based VLN. The verifier reduces decision-stage latency by replacing per-step autoregressive generation with batched action verification, while an
entropy-based adaptive generator is invoked only for uncertain decisions to produce compact state evidence. To further improve navigation performance with the verifier, we introduce a two-stage
alignment scheme: (i) VPO improves local action-preference
alignment in static verifier training, and (ii) step-level reinforcement fine-tuning provides dense progress rewards over multi-step navigation rollouts during dynamic task execution. Experiments on the Room-to-Room (R2R)
benchmark show that the verifier-only decision path of VerNav achieves competitive navigation performance among representative LLM-based VLN
agents while reducing average decision-stage LLM latency per step by more than $10\times$ compared with autoregressive methods.
Read the full abstract ▾
All authors Zhixin Wang, Chengzheyi Yao, Leyuan Liu, Xiaosong Zhang, Yongzhao Zhang
Robotics
Knowing When to Stop: Adaptive Action Chunking via Internal Cross-Attention Dynamics in VLAs
Runze Xu, Xiaolong Shan, Shuang Dai et al. (+2 more)
Action chunking is a standard execution strategy in modern
Vision-
Language-Action (VLA) frameworks, but fixed execution horizons impose a trade-off between efficiency and accuracy. Short chunks require frequent inference and may cause oscillatory behavior, whereas long chunks can become misaligned with newly observed states. We address this limitation with an adaptive action chunking approach based on internal cross-
attention dynamics in the action expert. We observe that, as the prediction horizon extends, action-to-observation cross-
attention becomes increasingly dispersed and its
entropy rises toward a plateau. This pattern is associated with higher action prediction error and provides an online signal that the current observation offers limited grounding for further open-loop execution. Based on this observation, we introduce a training-free truncation mechanism that detects sustained high-
entropy plateaus and dynamically selects the execution horizon during inference. The method uses
attention weights already computed by the policy and introduces negligible additional overhead. Evaluations on $\pi_{0.5}$ and X-VLA across RoboTwin 2.0, LIBERO, and three real-world manipulation tasks show improved average task success over fixed-horizon and adaptive chunking baselines, while preserving efficient closed-loop control. These results show that cross-
attention dynamics can provide a practical internal signal for adaptive action execution in VLAs.
Read the full abstract ▾
All authors Runze Xu, Xiaolong Shan, Shuang Dai, Yu Wang, Jincheng Yu
Robotics
Peg-in-Bench: A Modular Benchmark for High-Precision Robotic Insertion
Yosel Delgado, José G. Buenaventura-Carreón, Floris Erich et al. (+4 more)
High-precision insertion remains a fundamental challenge in robotic manipulation due to the strict
alignment requirements and contact-rich interactions involved. Although peg-in-hole tasks are widely used for evaluation, existing bench- marks often rely on fixed task configurations, limiting their ability to assess robustness and generalization across different insertion scenarios. This paper introduces a reconfigurable peg-in-hole
benchmark designed to evaluate task generalization in high-precision insertion. The
benchmark consists of a set of fully 3D-printable modular components, including multiple peg geometries, tolerance levels, and configurable base structures that can be combined to generate a large variety of insertion and assembly tasks. By varying object layouts, orientations, and task structures while maintaining controlled physical conditions, the
benchmark enables systematic evaluation of adaptation to unseen scenarios. To support reproducibility, we additionally provide a scenario generation tool capable of producing standardized task configurations and machine-readable task descriptions. The scenario generation tool and the STL files of the
benchmark pieces are available through the project repository: https://github.com/aistairc/peg-in-bench.
Read the full abstract ▾
All authors Yosel Delgado, José G. Buenaventura-Carreón, Floris Erich, Roman Mykhailyshyn, Tomohiro Motoda, Koshi Makihara, Yukiyasu Domae
Robotics
Connectivity-Aware Graph Extension for Decentralized Multi-Robot Exploration
Béatrice Garcia Cegarra, Elena Vanneaux, Quentin Picard et al. (+1 more)
Exploring unknown environments with multiple
UAVs requires coordination under intermittent communication, making decentralized operation a baseline assumption. We propose, within a decentralized framework, a novel exploration graph extension strategy based on frontier connectivity to extend exploration plans and maintain area partitioning among
agents stable and robust to disconnections and changes in spatial layout. The proposed extension method is applied to two state-of-the-art area partitioning methods and evaluated in simulation. Experiments show improved performance over existing graph extension approaches with higher exploration efficiency under low communication rate.
Read the full abstract ▾
All authors Béatrice Garcia Cegarra, Elena Vanneaux, Quentin Picard, David Filliat
Robotics
Non-Prehensile Throwing: A Reinforcement Learning Perspective
Abdullah Mustafa, Ryo Hanai, Ixchel G. Ramirez-Alpizar et al. (+4 more)
Robotic throwing enables fast object transport and extends a
robot's reachable workspace beyond traditional pick-and-place. While prehensile (grasp-based) throwing works well for graspable items, non-prehensile (grasp-free) throwing is better suited for large, heavy, and/or deformable objects. Existing approaches rely on model-based optimization with simplified contact models (e.g., dynamic grasping) and low-dimensional trajectory parameterizations, which limit solution quality and reachable workspace. We propose a reinforcement learning approach that additionally leverages sliding and rolling contact modes and directly optimizes joint-space trajectories without analytical contact models or custom parameterizations. The Markov Decision Process (MDP) is formulated as a dynamical system that evolves the
robot's joint state conditioned on the throwing target, object model, and initial configuration. Joint-jerk trajectories are planned offline at a low control rate and upsampled into smooth, high-rate velocity commands for deployment. For sim-to-real transfer, we minimize the
robot-dynamics gap through minimum-jerk system identification and train uncertainty-aware policies to mitigate object-modeling errors, particularly sensitivity to dynamic friction. In simulation, the policy achieves 99% success across thousands of configurations and generalizes to unseen objects. Sensitivity analysis shows robustness to mass uncertainty but high sensitivity to dynamic friction, consistent with the sliding-based release mechanism. Deployed zero-shot on a UR5e operating near its physical limits (5 m/s end-effector velocity), our method throws diverse objects including heavy (790 g) and large (20x20x28 cm) items to targets up to 350 cm distance or 180 cm elevation, achieving a 97% real-world success rate.
Read the full abstract ▾
All authors Abdullah Mustafa, Ryo Hanai, Ixchel G. Ramirez-Alpizar, Floris Erich, Ryoichi Nakajo, Yukiyasu Domae, Tetsuya Ogata
Robotics
A Compact Robotic Finger with 2-DoF MCP Joint Embedding DoF-Selective Passive Continuously Variable Transmission for Wide Force-Speed Operating Range
JaeHyung Jang and Jee-Hwan Ryu
This letter presents a compact two-degree-of-freedom (DoF) robotic finger with a flexion-selective passive continuously variable transmission (CVT) to achieve a wide force-speed operating range. Inspired by the functional differentiation of the human metacarpophalangeal (MCP) joint, the proposed mechanism realizes DoF-specific transmission differentiation by selectively assigning passive CVT to the flexion-extension DoF while preserving direct transmission for abduction-adduction. For a wide force-speed operating range, a force-responsive passive CVT is embedded in the flexion pathway, while direct transmission is preserved for the abduction-adduction pathway. To selectively realize transmission adaptation within a multi-DoF MCP mechanism, an output-side passive CVT employing a moving-pulley-inspired wire-routing structure is introduced. The resultant force generated by the wire tensions acting on the pulley that passively increases the flexion moment arm and transmission ratio according to the applied load without additional actuators, sensors, or control. Experimental results demonstrate a maximum output-force amplification of 4.19-fold and a mean amplification of 3.6-fold across the tested flexion angles ranging from 15 degrees to 75 degrees through moment-arm adaptation, thereby substantially expanding the achievable force-speed operating range. Furthermore, dexterous ball-rolling experiments verify that passive transmission adaptation can be achieved while preserving abduction-adduction functionality. These results demonstrate a scalable transmission design strategy for compact multi-DoF robotic hands.
Read the full abstract ▾
Robotics
One Print, Many Moves: Monolithic Origami-inspired Folding Actuator for Composable Soft Multi-DoF Systems
Jaehyung Jang, Zhenish Zhakypov, Jasmin Elena Palmer et al. (+3 more)
Conventional soft
robot actuators excel in compliance, but their uncontrolled deformations compromise accuracy and hinder scaling to multi-degree-of-freedom (DoF) systems. We introduce a MONOlithic ORIGAMI-inspired soft folding actuator design (MONORIGAMI) that establishes a design strategy based on spatially programmed stiffness anisotropy to preserve material compliance along desired folding directions while selectively restricting deformation in unwanted directions. The actuator leverages stiffness tiers based on material thickness, patterned in an origami-inspired geometry with facets and creases, converting unconstrained soft deformation into accurate, repeatable, and composable folding motions without additional reinforcements. The design is fully 3D-printable through a single-material, single-print process that requires no assembly. Each actuator serves as a scalable motion primitive, and linking and orienting multiple actuators mechanically programs multi-DoF trajectories. Using the same fundamental module, we demonstrate three 3D-printed soft multi-DoF robotic systems spanning distinct application domains: (1) a compact 4-DoF wearable haptic device for high-fidelity cutaneous feedback in virtual reality (VR), (2) a 3-DoF joystick for kinesthetic feedback in teleoperation, and (3) a modular robotic gripper capable of underwater operation with geometry-encoded grasp trajectories. These systems demonstrate the module's capabilities for compact multi-axis integration, controlled physical interaction, and geometry-programmed operation across different environments. Together, these results show that MONORIGAMI provides a general, composable, accessible, reliable, and scalable platform for high-precision soft multi-DoF robotics, addressing long-standing limitations in both soft actuator design and fabrication.
Read the full abstract ▾
All authors Jaehyung Jang, Zhenish Zhakypov, Jasmin Elena Palmer, Melissa Klein, Jee-Hwan Ryu, Allison Mariko Okamura
Robotics · Cross-listed from cs.CV
Design and Implementation of a Kalman Filter-Infused Algorithm for Tilt Estimation
Yuehan Ma and Hongji Dai
Accurate tilt angle estimation is important in many engineering applications, such as robotics, motion tracking, and embedded control systems. However, measurements from low-cost inertial sensors are often degraded by noise and drift. This paper presents a single-axis tilt angle estimation system based on the MPU6050 inertial measurement unit, implemented on an RP2040 microcontroller platform, with sensor fusion achieved through a Kalman filter. The accelerometer provides a direct estimate of tilt angle from
gravity but is sensitive to noise and short-term fluctuations. The gyroscope provides smooth angular rate measurements, but integration over time introduces drift. To overcome these limitations, a Kalman filter is used to combine measurements from both sensors, leveraging the long-term stability of the accelerometer and the short-term smoothness of the gyroscope. Both simulation and hardware experiments are performed. In simulation, sensor noise and drift are modeled to evaluate the filter performance under control conditions. In the hardware implementation, real-time MPU6050 data is acquired and processed by the RP2040 platform, and the estimated tilt angle is compared with accelerometer-only and gyroscope-only outputs. The results show that the proposed method effectively reduces noise measurements and suppresses long-term drift while preserving good dynamic response. Overall, the system provides more stable and accurate tilt estimation than either sensor alone, demonstrating a practical and accessible approach for Kalman filter based sensor fusion in embedded application. This manuscript is a preprint version of the work. Keywords: Kalman Filter, Accelerometer, Gyroscope, Noise Reduction, Angle Tracking
Read the full abstract ▾
Robotics
Context-Aware Intelligent Vehicles
Liangkai Liu, Shuyao Shi, Mingke Wang et al. (+4 more)
Intelligent vehicles increasingly support adaptive applications beyond driving themselves, ranging from context-aware ADAS and automated driving to in-cabin monitoring and fleet management, all under tight requirements on accuracy, latency, cost, and reliability. Meeting these requirements is challenging because vehicles operate in complex, uncertain, and rapidly changing environments while running on resource-constrained computing platforms. This paper argues that context-situational factors that give meaning to sensor signals and constrain decisions-should be treated as a first-class principle for next-generation vehicle systems, and operationalized as a unified, shared state for learning, risk assessment, and closed-loop control across the software stack. We systematically review state-of-the- art (SOTA) context-aware methods spanning (i) environment understanding, (ii) planning and control, (iii) safety and security, and (iv) connected vehicles. Based on a trend analysis of context-aware design, we identify four key technical challenges in building a general contextual engine for future intelligent vehicles: multi-modal context fusion, temporal context modeling, handling rare events, and collaborative context sharing. We hope this survey will motivate the development of robust and efficient context-aware vehicle applications.
Read the full abstract ▾
All authors Liangkai Liu, Shuyao Shi, Mingke Wang, Noah T. Curran, Chuan Li, Fan Bai, Kang G. Shin
Robotics
ADAPT: Agile Diffusion Action Priors for Robust and Steerable Online Text-Driven Humanoid Control
Yan Wu, Chenhao Li, Kaifeng Zhao et al. (+3 more)
We present ADAPT, an end-to-end framework for interactive, text-conditioned humanoid whole-body control. Unlike dominant text-to-motion pipelines that generate kinematic motions for a separate tracker, ADAPT solves
language control with an end-to-end closed-loop control framework, where the
robot must continuously respond to changing commands while maintaining balance, natural motion, and smooth transitions. ADAPT learns a
diffusion-based action prior from text-labeled humanoid state-action trajectories, enabling diverse motion skills to be directly executed from
language commands. To improve long-horizon robustness and smooth
prompt switching, we train a lightweight residual reinforcement learning policy on top of the frozen
diffusion controller. We further show that the same
diffusion policy can be reused as a steerable text-conditioned motion prior for downstream task adaptation. Experiments demonstrate robust
language-grounded skill execution, smooth interactive transitions, and style-preserving downstream control.
Read the full abstract ▾
All authors Yan Wu, Chenhao Li, Kaifeng Zhao, Gen Li, Marco Hutter, Siyu Tang
Robotics
Behavior–Realization Separation for Constrained Physical Human–Robot Interaction
Yongyan Cao
Physical human–
robot interaction software often couples desired-behavior specification with constrained realization; we treat these as separate layers. A behavior layer supplies a desired contact-port acceleration $a_k^{\mathrm{id}}=f_\theta(e_k,\dot e_k,F_{h,k})$. A realization layer converts it into constrained
robot commands and reports total desired-versus-realized acceleration error instead of hiding it in saturation. A same-objective unconstrained counterfactual separates regularization from constraint intervention, while plant data expose model error. This paper implements a receding-horizon quadratic program realizing memoryless affine behaviors. Changing the behavior modifies objective coefficients through $(C_\theta,G_\theta)$ while the
robot-command variable and feasible set remain unchanged. A planar study instantiates impedance and admittance; the same running layer accepts an impedance–admittance–impedance reassignment without reconstruction, under its existing rate limit. On a torque-controlled 7-DOF Franka FR3 in MuJoCo, the runtime freezes task-space dynamics per solve and enforces torque feasibility across its horizon. Under a sustained 20~N push, it holds a slack-relaxed workspace boundary to within approximately 0.1–0.2~mm, versus 4.4~cm (impedance) and 4.7~cm (admittance) overshoot from instantaneous clipping. A derated actuator budget then activates the torque constraint: horizon-wide enforcement keeps its frozen-model plan feasible to $2.1\times10^{-4}$~N$\cdot$m, whereas a first-step-only ablation plans up to 11.329~N$\cdot$m beyond budget; on the executed nonlinear plant, where both share the same local-model error, the gap is smaller but still favors horizon-wide enforcement (0.161 vs.\ 0.380~N$\cdot$m). These results are a focused proof of behavior–realization separation.
Read the full abstract ▾
Robotics
Fleets Need a Context Plane: Rethinking Cooperative Perception for Autonomous Drones
Liangkai Liu and Xiaoxiao Wu
Cooperative perception allows a drone fleet to combine observations from multiple viewpoints. However, existing systems typically fix their feature-sharing policies at design time or adapt to only one context signal. This is a poor fit for aerial fleets, whose missions, bandwidth, formation geometry, and scene coverage can change during flight. We quantify the cost of context-blind sharing on
UAV3D by controlling feature exchange at evaluation time using a released DiscoNet checkpoint, without retraining. Mission-aware sharing matches full-sharing accuracy while using only 5-10% of the bytes. The best tested peer selection policy changes with the byte budget, and choosing the wrong policy loses up to 7.7 AP. Moreover, under a constrained budget, two policies with the same full-scene accuracy differ by 5.9 AP within the mission region, showing that multiple context axes must be considered jointly. We therefore propose the context plane, a bounded, structured interface for runtime context. Each drone publishes a descriptor of at most 1 KB at 10 Hz, and lightweight, replaceable policies use the fleet context to decide what each drone computes, shares, and fuses. Existing sharing schemes become fixed policies within this interface. In our ROS 2 prototype on a Jetson AGX Orin, the context plane uses approximately 0.01% of the data-plane bandwidth, and each policy decision takes 0.10 ms. These results show that an explicit context interface can support low-overhead runtime adaptation without modifying or retraining the perception model.
Read the full abstract ▾
Robotics
Human-robot conversation with multiple participants in noisy public spaces
Divesh Lala, Yogeeswaran Muthukumaran, Vincent Fernandes et al. (+10 more)
For noisy real-world environments such as those in open public spaces, spoken dialogue systems for both autonomous
robots and avatars should be carefully designed to provide enhanced
speech signals. These signals can be used either for
speech recognition or, in the case of an avatar system, transmitted as clean
speech to a remote operator. This work proposes an audio system that can be used for both these scenarios and was demonstrated as a proof-of-concept at the 2025 World Expo in Osaka. The first scenario is an attentive listening system with the android ERICA, and the second is a conversation support system with mobile Teleco
robots, with one of them acting as an avatar for a remote operator. Both systems feature multi-party conversation and use a single multi-channel microphone array. We describe how our audio system not only enhances the
speech of multiple speakers in a noisy environment, but provides a form of spatial audio which allows for more immersiveness in avatar-based conversational interactions.
Read the full abstract ▾
All authors Divesh Lala, Yogeeswaran Muthukumaran, Vincent Fernandes, Kazushi Kato, Shota Fujiki, Zihao Chi, Masaya Iwasaki, Taiken Shintani, Megumi Kawata, Kazuki Sakai, Koji Inoue, Yuicihiro Yoshikawa, Tatsuya Kawahara
Robotics
AM-Bench: A Modular Simulation Suite and Benchmark for Aerial Manipulation Policy Learning
Yutong Wang, Dongjae Lee, Xiaofeng Guo et al. (+9 more)
Standardized
benchmarks have played a central role in advancing
robot manipulation learning, yet most focus on ground-supported manipulation systems, which limits their applicability to dynamics-critical domains such as aerial manipulation (AM). AM presents distinct system-level challenges, including environmental disturbances, coupled dynamics between the manipulator and floating base, and constrained degrees of freedom. Consequently, task performance depends jointly on
robot embodiment, low-level control, and high-level policy design. We introduce AM-Bench, a modular simulation suite and
benchmark for multirotor-based AM policy learning. AM-Bench includes representative embodiments spanning underactuated, fully actuated, and overactuated systems, 12 tasks across contact, transport, and constrained interaction, configurable aerodynamic disturbances and actuator saturation, standard low-level controllers, and baseline policy-learning
algorithms. Unlike prior manipulation
benchmarks that primarily emphasize end-to-end policy performance, AM-Bench enables system-level evaluation of how embodiment, control, disturbances, and policy choices interact. We demonstrate its diagnostic value through three simulation studies spanning high-level policies, policy–control interfaces, and embodiments, together with real-world validation of modeled effects and a hardware test of the learning pipeline.
Read the full abstract ▾
All authors Yutong Wang, Dongjae Lee, Xiaofeng Guo, Yuanzhu Zhan, Yufei Jiang, Bavin Saravanan, Muqing Cao, Jia Xie, Chenyang Mao, Sebastian Scherer, Junyi Geng, Guanya Shi
Robotics
DSG: Dynamic 3D Scene Graph Construction for Embodied Agents in Changing Indoor Environments
Ming Liao, Chao Ye, Jianing Fei et al. (+1 more)
In indoor environments, object positions frequently change due to human activities or embodied-
agent interactions, causing previously constructed scene graphs to become inconsistent with the current scene. To address this issue, we propose DSG, a dynamic 3D scene graph construction framework that detects object changes and performs spatial relationship
reasoning. First, we construct a semantic-aware 3D Gaussian scene representation and develop a dual-view rendering-based object change detection method to enable reliable scene graph node updates. Second, we propose a spatial relationship
reasoning method that incorporates multi-granularity visual context, enabling a large
language model to identify a richer set of interobject spatial relationships. Furthermore, we introduce DynTHOR, a dynamic indoor scene graph
benchmark built on the AI2-THOR simulation platform for evaluating scene graph construction in dynamic environments. Extensive experiments on Dyn-THOR, 3RScan, and real-world scenes demonstrate that DSG consistently outperforms existing methods in both object node construction and spatial relationship
reasoning, significantly improving the accuracy of dynamic scene graph construction.
Read the full abstract ▾
All authors Ming Liao, Chao Ye, Jianing Fei, Weiyang Lin
Robotics
A Wearable Pneumatic Device for Continuous, Closed-Loop, Bidirectional Tactile Interaction
Cosima du Pasquier, Aliyah Smith, Serin Huber et al. (+5 more)
We present a system of two wearable pneumatic haptic devices that supports continuous, closed-loop, bidirectional tactile interaction at perceptually relevant force and temporal scales. A single device can contain up to twelve pressure sensing channels connected to textile-based pneumatic pouches. Each channel in a device can be used as a sensor, an actuator, or both. As an actuator with integrated sensing, the channel generates stable skin indentation through local closed-loop control. As a sensor, a channel can be mounted (or worn) on any surface, including on a
robot gripper or on the human body, and used to measure touch interactions with the environment or a human user. A distributed architecture supports sustained pressure output, rapid dynamic response, and wireless pairing of identical devices in a system to transmit and reproduce tactile pressure signals in real time. Device-level characterization demonstrates force bandwidth exceeding 30 Hz, rapid and well-damped step responses, and extended pressure retention compared to prior compact pneumatic platforms. Human studies show that pressure-based fingertip feedback enables discrimination of force and stiffness, improves teleoperated manipulation by reducing applied pressures by up to 23.1% and task duration by up to 27.4%, and lowers subjective mental workload by 18.8%, particularly under visually constrained conditions. By unifying tactile sensing and haptic feedback within a single pneumatic modality, the device provides a practical foundation for bidirectional touch interaction in teleoperation.
Read the full abstract ▾
All authors Cosima du Pasquier, Aliyah Smith, Serin Huber, Joshua Phelps, Ilana A. Cohen, Ava Chen, Monroe Kennedy III, Allison M. Okamura
Robotics · Cross-listed from physics.bio-ph
Mudskippers use tail thrusting to help crutching to move on mud of various wetness
Divya Ramesh, Gargi Sadalgekar, Jiangqi Tan et al. (+1 more)
At the water-land interface, amphibious fishes encounter wet flowable substrates made of granular solid-water mixtures, which can stay solid or flow like a fluid. As these substrates become wetter or drier, their yield strength (at which solid-fluid transition occurs) and cohesion (how sticky they are) both change, challenging locomotion. Despite substantial understanding of tetrapod locomotion on flowable substrates (mostly dry sand), we know little about how amphibious fishes cope with wet flowable substrates of various wetness. Here, we studied mudskippers on clay mud of controlled, variable wetness over the range where solid-fluid transition occurs. As mud became wetter, its strength decreased by 100-fold, leading the animal to sink deeper, with larger areas of body and fins contacting mud. By contrast, mud stuck most easily at intermediate wetness. The increased sinkage and contact and stickiness change caused more mud to stick to and pull against the animal on wetter mud. We also tested dry mud, which stuck to animal fins as its mucus dried. Despite these challenges, the mudskipper predominately used a conserved crutching gait on all except the wettest mud tested, with a modest performance reduction. When normal crutching became less effective, the animal assisted it with tail thrusting, by bending and straightening it to push downward and backward to generate additional thrust and lift, or even thrusting the tail to jump. These observations suggest that mudskipper's crutching motor program is well adapted to its native muddy substrates but inflexible, with most novelty in tail use.
Read the full abstract ▾
All authors Divya Ramesh, Gargi Sadalgekar, Jiangqi Tan, Chen Li