Robotics
Avi: Action from Volumetric Inference
Harris Song and Long Le
We propose Avi, a novel 3D
Vision-
Language-Action (VLA) architecture that reframes robotic action generation as a problem of 3D perception and spatial
reasoning, rather than low-level policy learning. While existing VLA models primarily operate on 2D visual inputs and are trained end-to-end on task-specific action policies, Avi leverages 3D point clouds and
language-grounded scene understanding to compute actions through classical
geometric transformations. Most notably, Avi does not train on previous action
tokens, rather, we build upon a 3D Multi-modal Large
Language Model (MLLM) to generate the next point cloud and explicitly calculate the actions through classical transformations. This approach enables generalizable behaviors that are robust to occlusions, camera pose variations, and changes in viewpoint. By treating the robotic decision-making process as a structured
reasoning task over 3D representations, Avi bridges the gap between high-level
language instructions and low-level actuation without requiring opaque policy learning. Our preliminary results highlight the potential of 3D
vision-
language reasoning as a foundation for scalable, robust robotic systems. Check it out at https://avi-3drobot.github.io/.
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Robotics
FORGE-Tree: Diffusion-Forcing Tree Search for Long-Horizon Robot Manipulation
Yanjia Huang, Shuo Liu, Sheng Liu et al. (+4 more)
Long-horizon
robot manipulation tasks remain challenging for
Vision-
Language-Action (VLA) policies due to drift and exposure bias, often denoise the entire trajectory with fixed hyperparameters, causing small
geometric errors to compound across stages and offering no mechanism to allocate extra test-time compute where clearances are tight. To address these challenges, we introduce FORGE-Tree, a plug-in control layer that couples a stage-aligned
Diffusion Forcing (DF) head with test-time Monte Carlo Tree
Diffusion (MCTD). With a frozen VLA encoder, DF aligns timesteps to subtask stages; during inference we partially denoise only a target segment while keeping other
tokens frozen, turning trajectory refinement into a sequence of local edits. We then apply Monte Carlo Tree
Diffusion to select the next segment to refine. A scene graph supplies priors for expansion and geometry relation-aware scoring for rollouts, yielding tree-structured denoising whose performance scales with search budget while preserving the executed prefix. Evaluation on LIBERO, FORGE-Tree improves success rate by 13.4 to 17.2 pp over the native VLA baselines with both OpenVLA and Octo-Base. Gains remain consistent under comparable compute budgets, especially on long-horizon variants.
Videos available at: https://taco-group.github.io/FORGE-Tree/
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All authors Yanjia Huang, Shuo Liu, Sheng Liu, Qingxiao Xu, Mingyang Wu, Xiangbo Gao, Zhengzhong Tu
Robotics · Cross-listed from physics.ins-det
Bioinspired Tapered-Spring Turbulence Sensor for Underwater Flow Detection
Xiao Jin, Zhenhua Yu and Thrishantha Nanayakkara
This paper presents a bio-inspired
underwater whisker sensor for robust hydrodynamic disturbance detection and efficient signal analysis based on Physical Reservoir Computing (PRC). The design uses a tapered nylon spring with embedded accelerometers to achieve spatially distributed vibration sensing and frequency separation along the whisker. Towing-tank experiments and computational fluid dynamics simulations confirmed that the whisker effectively distinguishes
vortex regimes across different fin angles and maintains Strouhal scaling with flow velocity, where higher speeds increase vibration intensity without affecting the dominant frequencies. Frequency-domain analysis, Shannon
entropy, and machine learning further validated the sensing performance:
vortex shedding frequencies were identified with less than 10% error,
entropy captured the transition from coherent
vortex streets to
turbulence, and logistic regression achieved 86.0% classification accuracy with millisecond-level inference. These results demonstrate that structurally encoded whisker sensing provides a scalable and real-time solution for
underwater perception, wake tracking, and
turbulence-aware
navigation in autonomous marine
robots.
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Robotics
Real-Time Glass Detection and Reprojection using Sensor Fusion Onboard Aerial Robots
Malakhi Hopkins, Varun Murali, Vijay Kumar et al. (+1 more)
Autonomous aerial
robots are increasingly being deployed in real-world scenarios, where transparent obstacles present significant challenges to reliable
navigation and mapping. These materials pose a unique problem for traditional perception systems because they lack discernible features and can cause conventional depth sensors to fail, leading to inaccurate maps and potential collisions. To ensure safe
navigation,
robots must be able to accurately detect and map these transparent obstacles. Existing methods often rely on large, expensive sensors or
algorithms that impose high computational burdens, making them unsuitable for low Size, Weight, and Power (SWaP)
robots. In this work, we propose a novel and computationally efficient framework for detecting and mapping transparent obstacles onboard a sub-300g quadrotor. Our method fuses data from a Time-of-Flight (ToF) camera and an ultrasonic sensor with a custom, lightweight 2D convolution model. This specialized approach accurately detects specular reflections and propagates their depth into corresponding empty regions of the depth map, effectively rendering transparent obstacles visible. The entire pipeline operates in real-time, utilizing only a small fraction of a CPU core on an embedded processor. We validate our system through a series of experiments in both controlled and real-world environments, demonstrating the utility of our method through experiments where the
robot maps indoor environments containing glass. Our work is, to our knowledge, the first of its kind to demonstrate a real-time, onboard transparent obstacle mapping system on a low-SWaP quadrotor using only the CPU.
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All authors Malakhi Hopkins, Varun Murali, Vijay Kumar, Camillo J Taylor
Robotics
How Well Do Latent World Models Understand Partially Observable Safety Constraints?
Matthew Kim, Kensuke Nakamura and Andrea Bajcsy
Latent world models are a promising approach for learning state representations and dynamics directly from high-dimensional observations, enabling
robot control in hard-to-model settings. However, control performance ultimately depends on the latent representation encoding the required information for the task. In this work, we study latent-space safe control problems and show how partial observability can induce control failures when safety-relevant information is not preserved in the latent state. Specifically, we identify two world model failure modes: estimation gaps, where current observations do not reveal safety-critical quantities (e.g., temperature in a cooking task), and prediction gaps, where failures are observable once they occur but cannot be reliably anticipated from available observations. We introduce two diagnostics for these gaps: a mutual-information-based measure of safety observability and a rollout-based measure of future safety predictability. Finally, we present mitigation strategies for each failure mode: privileged
multimodal supervision for estimation gaps and conformal risk calibration for prediction gaps. Across two hardware case studies – using unimodal RGB world models and
multimodal RGB+Tactile and RGB+Thermal variants – we show that these mitigation strategies improve the safety of a Franka Research 3 manipulator on challenging cooking tasks under partial observability, albeit with increased conservativeness. More broadly, our work raises the question of when world model state representations are sufficient for reliable
robot control
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Robotics
Active Next-Best-View Optimization for Risk-Averse Path Planning
Amirhossein Mollaei Khass, Guangyi Liu, Vivek Pandey et al. (+4 more)
Safe
navigation in uncertain environments requires planning methods that integrate risk aversion with active perception. In this work, we present a unified framework that refines a coarse reference path by constructing tail-sensitive risk maps from Average Value-at-Risk statistics on an online-updated 3D Gaussian-splat Radiance Field. These maps enable the generation of locally safe and feasible trajectories. In parallel, we formulate Next-Best-View (NBV) selection as an optimization problem on the SE(3) pose
manifold, where Riemannian gradient descent maximizes an expected information gain objective to reduce uncertainty most critical for imminent motion. Our approach advances the state-of-the-art by coupling risk-averse path refinement with NBV planning, while introducing scalable gradient decompositions that support efficient online updates in complex environments. We demonstrate the effectiveness of the proposed framework through extensive computational studies.
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All authors Amirhossein Mollaei Khass, Guangyi Liu, Vivek Pandey, Wen Jiang, Boshu Lei, Kostas Daniilidis, Nader Motee
Robotics · Cross-listed from eess.SY
Terrain-Aided Navigation Without Raycasting: A Sliding Grid Approach for Point Cloud Processing
Abdülbaki Şanlan, Fatih Erol, Murad Abu-Khalaf et al. (+1 more)
We investigate the use of a point cloud measurement in terrain-aided
navigation. Our goal is to aid an inertial
navigation system, by exploring ways to generate a useful measurement innovation error for effective nonlinear state estimation. We compare two such measurement models that involve the scanning of a digital terrain elevation model: a) one that is based on typical ray-casting from a given pose, that returns the predicted point cloud measurement from that pose, and b) another computationally less intensive one that does not require raycasting and we refer to herein as a sliding grid. Besides requiring a pose, it requires the pattern of the point cloud measurement itself and returns a predicted point cloud measurement. We show that the sliding grid achieves comparable accuracy to raycasting, especially at higher altitudes, while being orders of magnitude faster computationally. By eliminating the need for raycasting, the sliding grid removes the dependency on dedicated GPU hardware or high-performance CPUs, making it well-suited for real-time terrain-aided
navigation on resource-constrained embedded platforms.
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All authors Abdülbaki Şanlan, Fatih Erol, Murad Abu-Khalaf, Emre Koyuncu
Robotics · Cross-listed from eess.SY
Three-dimensional Integrated Guidance and Control for Leader-Follower Flexible Formation of Fixed Wing UAVs
Praveen Kumar Ranjan, Abhinav Sinha and Yongcan Cao
This paper presents a nonlinear integrated guidance and control (IGC) approach for flexible leader-follower formation flight of fixed-wing unmanned aerial vehicles (
UAVs) while accounting for high-fidelity aerodynamics and thrust dynamics. Unlike conventional leader-follower schemes that fix the follower's position relative to the leader, the follower is steered to maintain range and bearing angles (which is the angle between its velocity vector and its line-of-sight (LOS) with respect to the leader) arbitrarily close to the prescribed values, enabling the follower to maintain formation on a hemispherical region behind the leader. The proposed IGC framework directly maps leader-follower relative range dynamics to throttle commands, and the follower's velocity orientation relative to the LOS to aerodynamic control surface deflections. This enables synergism between guidance and control subsystems. The control design uses a dynamic surface control-based backstepping approach to achieve convergence to the desired formation set, where Lyapunov barrier functions are incorporated to ensure the follower's bearing angle is constrained within specified bounds. Rigorous stability analysis guarantees uniform ultimate boundedness of all error states and strict constraint satisfaction in the presence of aerodynamic nonlinearities. The proposed flexible formation scheme allows the follower to have an orientation mismatch relative to the leader to execute anticipatory reconfiguration by transitioning between the relative positions in the admissible formation set when the leader aggressively maneuvers. The proposed IGC law relies only on relative information and onboard sensors without the information about the leader's maneuver, making it suitable for GPS-denied or non-cooperative scenarios. Finally, we present simulation results to vindicate the effectiveness and robustness of our approach.
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Robotics
Constrained Natural Language Action Planning for Resilient Embodied Systems
Grayson Byrd, Corban Rivera, Bethany Kemp et al. (+5 more)
Replicating human-level intelligence in the execution of
embodied tasks remains challenging due to the unconstrained nature of real-world environments. Novel use of large
language models (LLMs) for task planning seeks to address the previously intractable state/action space of complex planning tasks, but
hallucinations limit their reliability, and thus, viability beyond a research context. Additionally, the
prompt engineering required to achieve adequate system performance lacks transparency, and thus, repeatability. In contrast to LLM planning, symbolic planning methods offer strong reliability and repeatability guarantees, but struggle to scale to the complexity and ambiguity of real-world tasks. We introduce a new robotic planning method that augments LLM planners with symbolic planning oversight to improve reliability and repeatability, and provide a transparent approach to defining hard constraints with considerably stronger clarity than traditional
prompt engineering. Importantly, these augmentations preserve the
reasoning capabilities of LLMs and retain impressive generalization in open-world environments. We demonstrate our approach in simulated and real-world environments. On the ALFWorld planning
benchmark, our approach outperforms current state-of-the-art methods, achieving a near-perfect 99% success rate. Deployment of our method to a real-world quadruped
robot resulted in 100% task success compared to 50% and 30% for pure LLM and symbolic planners across
embodied pick and place tasks. Our approach presents an effective strategy to enhance the reliability, repeatability and transparency of LLM-based
robot planners while retaining their key strengths: flexibility and generalizability to complex real-world environments. We hope that this work will contribute to the broad goal of building resilient
embodied intelligent systems.
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All authors Grayson Byrd, Corban Rivera, Bethany Kemp, Meghan Booker, Aurora Schmidt, Celso M de Melo, Lalithkumar Seenivasan, Mathias Unberath
Robotics
A Formal gatekeeper Framework for Safe Dual Control with Active Exploration
Kaleb Ben Naveed, Devansh R. Agrawal and Dimitra Panagou
Planning safe trajectories under model uncertainty is a fundamental challenge. Robust planning ensures safety by considering worst-case realizations, yet ignores uncertainty reduction and leads to overly conservative behavior. Actively reducing uncertainty on-the-fly during a nominal mission defines the dual control problem. Most approaches address this by adding a weighted exploration term to the cost, tuned to trade off the nominal objective and uncertainty reduction, but without formal consideration of when exploration is beneficial. Moreover, safety is enforced in some methods but not in others. We propose a framework that integrates robust planning with active exploration under formal guarantees as follows: The key innovation and contribution is that exploration is pursued only when it provides a verifiable improvement without compromising safety. To achieve this, we utilize our earlier work on gatekeeper as an architecture for safety verification, and extend it so that it generates both safe and informative trajectories that reduce uncertainty and the cost of the mission, or keep it within a user-defined budget. The methodology is evaluated via simulation case studies on the online dual control of a quadrotor under parametric uncertainty.
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Robotics
Vi-TacMan: Articulated Object Manipulation via Vision and Touch
Leiyao Cui, Zihang Zhao, Sirui Xie et al. (+3 more)
Autonomous manipulation of articulated objects remains a fundamental challenge for
robots in human environments.
Vision-based methods can infer hidden kinematics but can yield imprecise estimates on unfamiliar objects. Tactile approaches achieve robust control through contact feedback but require accurate initialization. This suggests a natural synergy:
vision for global guidance, touch for local precision. Yet no framework systematically exploits this complementarity for generalized articulated manipulation. Here we present Vi-TacMan, which uses
vision to propose grasps and coarse directions that seed a tactile controller for precise execution. By incorporating surface normals as
geometric priors and modeling directions via von Mises-Fisher distributions, our approach achieves significant gains over baselines (all p<0.0001). Critically, manipulation succeeds without explicit kinematic models – the tactile controller refines coarse visual estimates through real-time contact regulation. Tests on more than 50,000 simulated and diverse real-world objects confirm robust cross-category generalization. This work establishes that coarse visual cues suffice for reliable manipulation when coupled with tactile feedback, offering a scalable paradigm for autonomous systems in unstructured environments.
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All authors Leiyao Cui, Zihang Zhao, Sirui Xie, Wenhuan Zhang, Zhi Han, Yixin Zhu
Robotics · Cross-listed from cs.CV
Dropping the D: RGB-D SLAM Without the Depth Sensor
Mert Kiray, Alican Karaomer and Benjamin Busam
We present DropD-SLAM, a real-time
monocular SLAM system that achieves RGB-D-level accuracy without relying on depth sensors. The system replaces active depth input with three pretrained
vision modules: a
monocular metric depth
estimator, a learned keypoint detector, and an instance
segmentation network. Dynamic objects are suppressed using dilated instance masks, while static keypoints are assigned predicted depth values and backprojected into 3D to form metrically scaled features. These are processed by an unmodified RGB-D SLAM back end for tracking and mapping. On the TUM RGB-D
benchmark, DropD-SLAM attains 7.4 cm mean ATE on static sequences and 1.8 cm on dynamic sequences, matching or surpassing state-of-the-art RGB-D methods while operating at 22 FPS on a single GPU. These results suggest that modern pretrained
vision models can replace active depth sensors as reliable, real-time sources of metric scale, marking a step toward simpler and more cost-effective SLAM systems.
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Robotics
EmbodiedCoder: Parameterized Embodied Mobile Manipulation via Modern Coding Model
Zefu Lin, Rongxu Cui, Chen Hanning et al. (+8 more)
Recent advances in control
robot methods, from end-to-end
vision-
language-action frameworks to modular systems with predefined primitives, have advanced
robots' ability to follow natural
language instructions. Nonetheless, many approaches still struggle to scale to diverse environments, as they often rely on large annotated
datasets and offer limited interpretability.In this work, we introduce EmbodiedCoder, a training-free framework for open-world mobile
robot manipulation that leverages coding models to directly generate executable
robot trajectories. By grounding high-level instructions in code, EmbodiedCoder enables flexible object geometry parameterization and manipulation trajectory synthesis without additional data collection or fine-tuning.This coding-based paradigm provides a transparent and generalizable way to connect perception with manipulation. Experiments on real mobile
robots show that EmbodiedCoder achieves robust performance across diverse long-term tasks and generalizes effectively to novel objects and environments.Our results demonstrate an interpretable approach for bridging high-level
reasoning and low-level control, moving beyond fixed primitives toward versatile
robot intelligence. See the project page at: https://embodiedcoder.github.io/EmbodiedCoder/
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All authors Zefu Lin, Rongxu Cui, Chen Hanning, Xiangyu Wang, Junjia Xu, Xiaojuan Jin, Chen Wenbo, Hui Zhou, Lue Fan, Wenling Li, Zhaoxiang Zhang
Robotics
DYMO-Hair: Generalizable Volumetric Dynamics Modeling for Robot Hair Manipulation
Chengyang Zhao, Uksang Yoo, Arkadeep Narayan Chaudhury et al. (+4 more)
Hair care is an essential daily activity, yet it remains inaccessible to individuals with limited mobility and challenging for autonomous
robot systems due to the fine-grained physical structure and complex dynamics of hair. In this work, we present DYMO-Hair, a model-based
robot hair care system. We introduce a novel dynamics learning paradigm that is suited for volumetric quantities such as hair, relying on an action-conditioned latent state editing mechanism, coupled with a compact 3D latent space of diverse hairstyles to improve generalizability. This latent space is pre-trained at scale using a novel hair physics simulator, enabling generalization across previously unseen hairstyles. Using the dynamics model with a Model Predictive Path Integral (MPPI) planner, DYMO-Hair is able to perform visual goal-conditioned hair styling. Experiments in simulation demonstrate that DYMO-Hair's dynamics model outperforms baselines on capturing local deformation for diverse, unseen hairstyles. DYMO-Hair further outperforms baselines in closed-loop hair styling tasks on unseen hairstyles, with an average of 22% lower final
geometric error and 42% higher success rate than the state-of-the-art system. Real-world experiments exhibit zero-shot transferability of our system to wigs, achieving consistent success on challenging unseen hairstyles where the state-of-the-art system fails. Together, these results introduce a foundation for model-based
robot hair care, advancing toward more generalizable, flexible, and accessible
robot hair styling in unconstrained physical environments. More details are available on our project page: https://dymohair.github.io/.
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All authors Chengyang Zhao, Uksang Yoo, Arkadeep Narayan Chaudhury, Giljoo Nam, Jonathan Francis, Jeffrey Ichnowski, Jean Oh
Robotics
A Preview of HoloOcean 2.0
Blake Romrell, Abigail Austin, Braden Meyers et al. (+3 more)
Marine robotics simulators play a fundamental role in the development of marine robotic systems. With increased focus on the marine robotics field in recent years, there has been significant interest in developing higher fidelitysimulation of marine sensors, physics, and visual rendering capabilities to support autonomous marine
robot development and validation. HoloOcean 2.0, the next major release of HoloOcean, brings state-of-the-art features under a general marine simulator capable of supporting a variety of tasks. New features in HoloOcean 2.0 include migration to Unreal Engine (UE) 5.3, advanced vehicle dynamics using models from Fossen, and support for ROS2 using a custom bridge. Additional features are currently in development, including significantly more efficient ray tracing-based sidescan, forward-looking, and bathymetric sonar implementations; semantic sensors; environment generation tools; volumetric environmental effects; and realistic waves.
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All authors Blake Romrell, Abigail Austin, Braden Meyers, Ryan Anderson, Carter Noh, Joshua G. Mangelson
Robotics
Vision-Guided Targeted Grasping and Vibration for Robotic Pollination in Controlled Environments
Jaehwan Jeong, Tuan-Anh Vu, Radha Lahoti et al. (+4 more)
Robotic pollination offers a promising alternative to manual labor and bumblebee-assisted methods in controlled agriculture, where wind-driven pollination is absent and regulatory restrictions limit the use of commercial pollinators. In this work, we present and validate a
vision-guided robotic framework that uses data from an end-effector mounted RGB-D sensor and combines 3D plant reconstruction, targeted grasp planning, and physics-based vibration modeling to enable precise pollination. First, the plant is reconstructed in 3D and registered to the
robot coordinate frame to identify obstacle-free grasp poses along the main stem. Second, a discrete elastic rod model predicts the relationship between actuation parameters and flower dynamics, guiding the selection of optimal pollination strategies. Finally, a manipulator with soft grippers grasps the stem and applies controlled vibrations to induce pollen release. End-to-end experiments demonstrate a 92.5% main-stem grasping success rate, and simulation-guided optimization of vibration parameters further validates the feasibility of our approach, ensuring that the
robot can safely and effectively perform pollination without damaging the flower. To our knowledge, this is the first robotic system to jointly integrate
vision-based grasping and vibration modeling for automated precision pollination.
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All authors Jaehwan Jeong, Tuan-Anh Vu, Radha Lahoti, Jiawen Wang, Vivek Alumootil, Sangpil Kim, M. Khalid Jawed
Robotics
Towards Autonomous Tape Handling for Robotic Wound Redressing
Xiao Liang, Lu Shen, Peihan Zhang et al. (+3 more)
Chronic wounds, such as diabetic, pressure, and venous ulcers, affect over 6.5 million patients in the United States alone and generate an annual cost exceeding \$25 billion. Despite this burden, chronic wound care remains a routine yet manual process performed exclusively by trained clinicians due to its critical safety demands. We envision a future in which robotics and automation support wound care to lower costs and enhance patient outcomes. This paper introduces an autonomous framework for one of the most fundamental yet challenging subtasks in wound redressing: adhesive tape manipulation. Specifically, we address two critical capabilities: tape initial detachment (TID) and secure tape placement. To handle the complex adhesive dynamics of detachment, we propose a force-feedback imitation learning approach trained from human teleoperation demonstrations. For tape placement, we develop a numerical trajectory optimization method based to ensure smooth adhesion and wrinkle-free application across diverse anatomical surfaces. We validate these methods through extensive experiments, demonstrating reliable performance in both quantitative evaluations and integrated wound redressing pipelines. Our results establish tape manipulation as an essential step toward practical robotic wound care automation.
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All authors Xiao Liang, Lu Shen, Peihan Zhang, Soofiyan Atar, Florian Richter, Michael Yip
Robotics
Multi-Robot Distributed Optimization for Exploration and Mapping of Unknown Environments using Bioinspired Tactile-Sensor
Roman Ibrahimov and Jannik Matthias Heinen
This project proposes a bioinspired multi-
robot system using Distributed Optimization for efficient exploration and mapping of unknown environments. Each
robot explores its environment and creates a map, which is afterwards put together to form a global 2D map of the environment. Inspired by wall-following behaviors, each
robot autonomously explores its neighborhood based on a tactile sensor, similar to the
antenna of a cockroach, mounted on the surface of the
robot. Instead of avoiding obstacles,
robots log collision points when they touch obstacles. This decentralized control strategy ensures effective task allocation and efficient exploration of unknown terrains, with applications in search and rescue, industrial inspection, and environmental monitoring. The approach was validated through experiments using e-puck
robots in a simulated 1.5 x 1.5 m environment with three obstacles. The results demonstrated the system's effectiveness in achieving high coverage, minimizing collisions, and constructing accurate 2D maps.
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Robotics
MachaGrasp: Morphology-Aware Cross-Embodiment Dexterous Hand Articulation Generation for Grasping
Heng Zhang, Kevin Yuchen Ma, Mike Zheng Shou et al. (+2 more)
Dexterous grasping with multi-fingered hands remains challenging due to high-dimensional articulations and the cost of optimization-based pipelines. Existing end-to-end methods require training on large-scale
datasets for specific hands, limiting their ability to generalize across different embodiments. We propose MachaGrasp, an eigengrasp-based, end-to-end framework for cross-embodiment grasp generation. From a hand's morphology description, we derive a morphology
embedding and an eigengrasp set. Conditioned on these, together with the object point cloud and wrist pose, an amplitude predictor regresses articulation coefficients in a low-dimensional space, which are decoded into full joint articulations. Articulation learning is supervised with a Kinematic-Aware Articulation Loss (KAL) that emphasizes fingertip-relevant motions and injects morphology-specific structure. In simulation on unseen objects across three dexterous hands, MachaGrasp attains a 91.9% average grasp success rate with less than 0.4 seconds inference per grasp. With few-shot adaptation to an unseen hand, it achieves 85.6% success on unseen objects in simulation, and real-world experiments on this few-shot-generalized hand achieve an 87% success rate. The code and additional materials are available on our project website https://connor-zh.github.io/MachaGrasp.
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All authors Heng Zhang, Kevin Yuchen Ma, Mike Zheng Shou, Weisi Lin, Yan Wu
Robotics · Cross-listed from quant-ph
Hybrid Quantum-Classical Policy Gradient for Adaptive Control of Cyber-Physical Systems: A Comparative Study of VQC vs. MLP
Aueaphum Aueawatthanaphisut and Nyi Wunna Tun
The comparative evaluation between classical and
quantum reinforcement learning (QRL) paradigms was conducted to investigate their convergence behavior, robustness under observational noise, and computational efficiency in a
benchmark control environment. The study employed a multilayer perceptron (MLP)
agent as a classical baseline and a parameterized variational
quantum circuit (VQC) as a
quantum counterpart, both trained on the CartPole-v1 environment over 500 episodes. Empirical results demonstrated that the classical MLP achieved near-optimal policy convergence with a mean return of 498.7 +/- 3.2, maintaining stable
equilibrium throughout training. In contrast, the VQC exhibited limited learning capability, with an average return of 14.6 +/- 4.8, primarily constrained by
circuit depth and
qubit connectivity. Noise robustness analysis further revealed that the MLP policy deteriorated gracefully under Gaussian perturbations, while the VQC displayed higher sensitivity at equivalent noise levels. Despite the lower asymptotic performance, the VQC exhibited significantly lower parameter count and marginally increased training time, highlighting its potential scalability for low-resource
quantum processors. The results suggest that while classical neural policies remain dominant in current control
benchmarks,
quantum-enhanced architectures could offer promising efficiency advantages once hardware noise and expressivity limitations are mitigated.
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Robotics · Cross-listed from cs.AI
Information-Theoretic Policy Pre-Training with Empowerment
Moritz Schneider, Robert Krug, Narunas Vaskevicius et al. (+3 more)
Empowerment, an information-theoretic measure of an
agent's potential influence on its environment, has emerged as a powerful intrinsic motivation and exploration framework for reinforcement learning (RL). Besides for unsupervised RL and skill learning
algorithms, the specific use of empowerment as a pre-training signal has received limited
attention in the literature. We show that empowerment can be used as a pre-training signal for data-efficient downstream task adaptation. For this we extend the traditional notion of empowerment by introducing discounted empowerment, which balances the
agent's control over the environment across short- and long-term horizons. Leveraging this formulation, we propose a novel pre-training paradigm that initializes policies to maximize discounted empowerment, enabling
agents to acquire a robust understanding of environmental dynamics. We analyze empowerment-based pre-training for various existing RL
algorithms and empirically demonstrate its potential as a general-purpose initialization strategy: empowerment-maximizing policies with long horizons are data-efficient and effective, leading to improved adaptability in downstream tasks. Our findings pave the way for future research to scale this framework to high-dimensional and complex tasks, further advancing the field of RL.
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All authors Moritz Schneider, Robert Krug, Narunas Vaskevicius, Luigi Palmieri, Michael Volpp, Joschka Boedecker
Robotics
Coordinate-Consistent Localization via Continuous-Time Calibration and Fusion of UWB and SLAM Observations
Tien-Dat Nguyen, Thien-Minh Nguyen and Vinh-Hao Nguyen
Onboard simultaneous localization and mapping (SLAM) methods are commonly used to provide accurate localization information for autonomous
robots. However, the coordinate origin of SLAM estimate often resets for each run. On the other hand, UWB-based localization with fixed anchors can ensure a consistent coordinate reference across sessions; however, it requires an accurate assignment of the anchor nodes' coordinates. To this end, we propose a two-stage approach that calibrates and fuses UWB data and SLAM data to achieve coordinate-wise consistent and accurate localization in the same environment. In the first stage, we solve a continuous-time batch optimization problem by using the range and odometry data from one full run, incorporating height priors and anchor-to-anchor distance factors to recover the anchors' 3D positions. For the subsequent runs in the second stage, a sliding-window optimization scheme fuses the UWB and SLAM data, which facilitates accurate localization in the same coordinate system. Experiments are carried out on the NTU VIRAL
dataset with six scenarios of
UAV flight, and we show that calibration using data in one run is sufficient to enable accurate localization in the remaining runs. We release our source code to benefit the community at https://github.com/ntdathp/slam-uwb-calibration.
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Robotics
AI-Enabled Capabilities to Facilitate Next-Generation Rover Surface Operations
Cristina Luna, Robert Field and Steven Kay
Current planetary rovers operate at traverse speeds of approximately 10 cm/s, fundamentally limiting exploration efficiency. This work presents integrated AI systems which significantly improve autonomy through three components: (i) the FASTNAV Far Obstacle Detector (FOD), capable of facilitating sustained 1.0 m/s speeds via computer
vision-based obstacle detection; (ii) CISRU, a multi-
robot coordination framework enabling human-
robot collaboration for in-situ resource utilisation; and (iii) the ViBEKO and AIAXR deep learning-based terrain classification studies. Field validation in Mars analogue environments demonstrated these systems at Technology Readiness Level 4, providing measurable improvements in traverse speed, classification accuracy, and operational safety for next-generation planetary missions.
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