Robotics
Reinforcement Learning based 6-DoF Maneuvers for Microgravity Intravehicular Docking: A Simulation Study with Int-Ball2 in ISS-JEM
Aman Arora, Matteo El-Hariry and Miguel Olivares-Mendez
Autonomous free-flyers play a critical role in intravehicular tasks aboard the International Space Station (ISS), where their precise docking under sensing noise, small actuation mismatches, and environmental variability remains a nontrivial challenge. This work presents a reinforcement learning (RL) framework for six-degree-of-freedom (6-DoF) docking of JAXA's Int-Ball2
robot inside a high-fidelity Isaac Sim model of the Japanese Experiment Module (JEM). Using Proximal Policy Optimization (PPO), we train and evaluate controllers under domain-randomized dynamics and bounded observation noise, while explicitly modeling propeller drag-torque effects and polarity structure. This enables a controlled study of how Int-Ball2's propulsion physics influence RL-based docking performance in constrained microgravity interiors. The learned policy achieves stable and reliable docking across varied conditions and lays the groundwork for future extensions pertaining to Int-Ball2 in collision-aware
navigation, safe RL, propulsion-accurate sim-to-real transfer, and
vision-based end-to-end docking.
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Robotics
Evaluating the Navigation Capabilities of a Modified COAST Guidewire Robot in an Anatomical Phantom Model
Timothy A. Brumfiel, Revanth Konda, Drew Elliott et al. (+1 more)
To address the issues that arise due to the manual
navigation of guidewires in endovascular interventions, research in medical robotics has taken a strong interest in developing robotically steerable guidewires, which offer the possibility of enhanced maneuverability and
navigation, as the tip of the guidewire can be actively steered. The COaxially Aligned STeerable (COAST) guidewire
robot has the ability to generate a wide variety of motions including bending motion with different bending lengths, follow-the-leader motion, and feedforward motion. In our past studies, we have explored different designs of the COAST guidewire
robot and developed modeling, control, and sensing strategies for the COAST guidewire
robot. In this study, the performance of a modified COAST guidewire
robot is evaluated by conducting
navigation experiments in an anatomical phantom model with pulsatile flow. The modified COAST guidewire
robot is a simplified version of the COAST guidewire
robot and consists of two tubes as opposed to three tubes. Through this study, we demonstrate the effectiveness of the modified COAST guidewire
robot in navigating the tortuous phantom vasculature.
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All authors Timothy A. Brumfiel, Revanth Konda, Drew Elliott, Jaydev P. Desai
Robotics
Universal Dexterous Functional Grasping via Demonstration-Editing Reinforcement Learning
Chuan Mao, Haoqi Yuan, Ziye Huang et al. (+3 more)
Reinforcement learning (RL) has achieved great success in dexterous grasping, significantly improving grasp performance and generalization from simulation to the real world. However, fine-grained functional grasping, which is essential for downstream manipulation tasks, remains underexplored and faces several challenges: the complexity of specifying goals and reward functions for functional grasps across diverse objects, the difficulty of multi-task RL exploration, and the challenge of sim-to-real transfer. In this work, we propose DemoFunGrasp for universal dexterous functional grasping. We factorize functional grasping conditions into two complementary components - grasping style and affordance - and integrate them into an RL framework that can learn to grasp any object with any functional grasping condition. To address the multi-task optimization challenge, we leverage a single grasping demonstration and reformulate the RL problem as one-step demonstration editing, substantially enhancing sample efficiency and performance. Experimental results in both simulation and the real world show that DemoFunGrasp generalizes to unseen combinations of objects, affordances, and grasping styles, outperforming baselines in both success rate and functional grasping accuracy. In addition to strong sim-to-real capability, by incorporating a
vision-
language model (VLM) for planning, our system achieves autonomous instruction-following grasp execution.
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All authors Chuan Mao, Haoqi Yuan, Ziye Huang, Chaoyi Xu, Kai Ma, Zongqing Lu
Robotics
Fast Policy Learning for 6-DOF Position Control of Underwater Vehicles
Sümer Tunçay, Alain Andres and Ignacio Carlucho
Autonomous
Underwater Vehicles (AUVs) require reliable six-degree-of-freedom (6-DOF) position control to operate effectively in complex and dynamic marine environments. Traditional controllers are effective under nominal conditions but exhibit degraded performance when faced with unmodeled dynamics or environmental disturbances. Reinforcement learning (RL) provides a powerful alternative but training is typically slow and sim-to-real transfer remains challenging. This work introduces a GPU accelerated RL training pipeline built in JAX and MuJoCo-XLA (MJX). By jointly JIT-compiling large-scale parallel physics simulation and learning updates, we achieve training times of under two minutes. Through systematic evaluation of multiple RL
algorithms, we show robust 6-DOF trajectory tracking and effective disturbance rejection in real
underwater experiments, with policies transferred zero-shot from simulation.
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Robotics
Control of a Twin Rotor using Twin Delayed Deep Deterministic Policy Gradient (TD3)
Zeyad Gamal, Youssef Mahran and Ayman El-Badawy
This paper proposes a reinforcement learning (RL) framework for controlling and stabilizing the Twin Rotor Aerodynamic System (TRAS) at specific pitch and azimuth angles and tracking a given trajectory. The complex dynamics and non-linear characteristics of the TRAS make it challenging to control using traditional control
algorithms. However, recent developments in RL have attracted interest due to their potential applications in the control of multirotors. The Twin Delayed Deep Deterministic Policy Gradient (TD3)
algorithm was used in this paper to train the RL
agent. This
algorithm is used for environments with continuous state and action spaces, similar to the TRAS, as it does not require a model of the system. The simulation results illustrated the effectiveness of the RL control method. Next, external disturbances in the form of wind disturbances were used to test the controller's effectiveness compared to conventional PID controllers. Lastly, experiments on a laboratory setup were carried out to confirm the controller's effectiveness in real-world applications.
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Robotics
Humanoid Robot Running Through Random Stepping Stones and Jumping Over Obstacles: Step Adaptation Using Spring-Mass Trajectories
Sait Sovukluk, Johannes Englsberger and Christian Ott
This study proposes a step adaptation framework for running through spring-mass trajectories and deadbeat control gain libraries. It includes four main parts: (1) Automatic spring-mass trajectory library generation; (2) Deadbeat control gain library generation through an actively controlled template model that resembles the whole-body dynamics well; (3) Trajectory selection policy development for step adaptation; (4) Mapping spring-mass trajectories to a
humanoid model through a whole-body control (WBC) framework also accounting for closed-kinematic chain systems, self collisions, and reactive limb swinging. We show the inclusiveness and the robustness of the proposed framework through various challenging and agile behaviors such as running through randomly generated stepping stones, jumping over random obstacles, performing slalom motions, changing the running direction suddenly with a random leg, and rejecting significant disturbances and uncertainties through the MuJoCo physics simulator. We also perform additional simulations under a comprehensive set of uncertainties and noise to better justify the proposed method's robustness to real-world challenges, including signal noise, imprecision, modeling errors, and delays. All the aforementioned behaviors are performed with a single library and the same set of WBC control parameters without additional tuning. The spring-mass and the deadbeat control gain library are automatically computed in 4.5 seconds in total for 315 different trajectories.
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Robotics
Intrinsic-Motivation Multi-Robot Social Formation Navigation with Coordinated Exploration
Hao Fu, Wei Liu and Shuai Zhou
This paper investigates the application of reinforcement learning (RL) to multi-
robot social formation
navigation, a critical capability for enabling seamless human-
robot coexistence. While RL offers a promising paradigm, the inherent unpredictability and often uncooperative dynamics of pedestrian behavior pose substantial challenges, particularly concerning the efficiency of coordinated exploration among
robots. To address this, we propose a novel coordinated-exploration multi-
robot RL
algorithm introducing an intrinsic motivation exploration. Its core component is a self-learning intrinsic reward mechanism designed to collectively alleviate policy conservatism. Moreover, this
algorithm incorporates a dual-sampling mode within the centralized training and decentralized execution framework to enhance the representation of both the
navigation policy and the intrinsic reward, leveraging a two-time-scale update rule to decouple parameter updates. Empirical results on social formation
navigation benchmarks demonstrate the proposed
algorithm's superior performance over existing state-of-the-art methods across crucial metrics. Our code and
video demos are available at: https://github.com/czxhunzi/CEMRRL.
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Robotics · Cross-listed from cs.SD
SAMAY: System for Acoustic Measurement and Analysis
Adheep Arya G R, Vaibhav Pratap Singh, Mayank Kumar et al. (+7 more)
This paper describes an automatic bird call recording system called SAMAY, which is developed to study bird species by creating a database of large amounts of bird acoustic data. By analysing the recorded bird call data, the system can also be used for automatic classification of bird species, monitoring bird populations and analysing the impact of environmental changes. The system is driven through a powerful STM32F407 series microcontroller, supports 4 microphones, is equipped with 128 GB of storage capacity, and is powered by a 10400 mAh battery pack interfaced with a
solar charger. In addition, the device is user-configurable over USB and Wi-Fi during runtime, ensuring user-friendly operation during field deployment.
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All authors Adheep Arya G R, Vaibhav Pratap Singh, Mayank Kumar, Niyathi Shenoy, Tejas Suryawanshi, Ruchi Juyal, Sangit Saha, Kaushik Nanda, Hari Babu Pasupuleti, S D Sudarsan
Robotics
Lightweight Dynamic Modeling of Cable-Driven Continuum Robots Based on Actuation-Space Energy Formulation
Fangju Yang, Hang Yang, Ibrahim Alsarraj et al. (+2 more)
Cable-driven continuum
robots (CDCRs) require accurate, real-time dynamic models for high-speed dynamics prediction or model-based control, making such capability an urgent need. In this paper, we propose the Lightweight Actuation-Space Energy Modeling (LASEM) framework for CDCRs, which formulates actuation potential energy directly in actuation space to enable lightweight yet accurate dynamic modeling. Through a unified variational derivation, the governing dynamics reduce to a single partial differential equation (PDE), requiring only the Euler moment balance while implicitly incorporating the Newton force balance. By also avoiding explicit computation of cable-backbone contact forces, the formulation simplifies the model structure and improves computational efficiency while preserving
geometric accuracy and physical consistency. Importantly, the proposed framework for dynamic modeling natively supports both force-input and displacement-input actuation modes, a capability seldom achieved in existing dynamic formulations. Leveraging this lightweight structure, a Galerkin space-time modal discretization with analytical time-domain derivatives of the reduced state further enables an average 62.3% computational speedup over state-of-the-art real-time dynamic modeling approaches.
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All authors Fangju Yang, Hang Yang, Ibrahim Alsarraj, Yuhao Wang, Ke Wu
Robotics
Post-Training and Test-Time Scaling of Generative Agent Behavior Models for Interactive Autonomous Driving
Hyunki Seong, Jeong-Kyun Lee, Heesoo Myeong et al. (+5 more)
Learning interactive motion behaviors among multiple
agents is a core challenge in autonomous driving. While imitation learning models generate realistic trajectories, they often inherit biases from
datasets dominated by safe demonstrations, limiting robustness in safety-critical cases. Moreover, most studies rely on open-loop evaluation, overlooking compounding errors in closed-loop execution. We address these limitations with two complementary strategies. First, we propose Group Relative Behavior Optimization (GRBO), a reinforcement learning post-training method that fine-tunes pretrained behavior models via group relative advantage maximization with human regularization. Using only 10% of the training
dataset, GRBO improves safety performance by over 40% while preserving behavioral realism. Second, we introduce Warm-K, a warm-started Top-K sampling strategy that balances consistency and diversity in motion selection. Our Warm-K method-based test-time scaling enhances behavioral consistency and reactivity at test time without retraining, mitigating
covariate shift and reducing performance discrepancies. Demo
videos are available in the supplementary material.
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All authors Hyunki Seong, Jeong-Kyun Lee, Heesoo Myeong, Yongho Shin, Hyun-Mook Cho, Duck Hoon Kim, Pranav Desai, Monu Surana
Robotics
A Unified Framework for Automated Assembly Sequence and Production Line Planning using Graph-based Optimization
Christoph Hartmann, Marios Demetriades, Kevin Prüfer et al. (+3 more)
This paper presents PyCAALP (Python-based Computer-Aided Assembly Line Planning), a framework for automated Assembly Sequence Planning (ASP) and Production Line Planning (PLP), employing a graph-based approach to model components and joints within production modules. The framework integrates kinematic boundary conditions, such as potential part collisions, to guarantee the feasibility of automated assembly planning. The developed
algorithm computes all feasible production sequences, integrating modules for detecting spatial relationships and formulating
geometric constraints. The
algorithm incorporates additional attributes, including handling feasibility, tolerance matching, and joint compatibility, to manage the high combinatorial complexity inherent in assembly sequence generation. Heuristics, such as Single-Piece Flow assembly and geometrical constraint enforcement, are utilized to further refine the solution space, facilitating more efficient planning for complex assemblies. The PLP stage is formulated as a Mixed-Integer Program (MIP), balancing the total times of a fixed number of manufacturing stations. To keep the MIP tractable for complex assemblies, a deterministic path-guided reduction solves it on a subgraph assembled from complete, high-quality assembly paths, which preserves feasibility by construction and reproduces the full-graph optimum, exactly or near-exactly, at a small fraction of the directed graph and with speedups of up to three orders of magnitude. Furthermore, the framework enables customization of engineering constraints and supports a flexible trade-off between ASP and PLP. The open-source nature of the framework, available at https://github.com/TUM-utg/PyCAALP, promotes further collaboration and adoption in both industrial and production research applications.
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All authors Christoph Hartmann, Marios Demetriades, Kevin Prüfer, Zichen Zhang, Klaus Spindler, Stefan Weltge
Robotics
Multi-directional Safe Rectangle Corridor-Based MPC for Nonholonomic Robots Navigation in Cluttered Environment
Yinsong Qu, Yunxiang Li and Shanlin Zhong
Autonomous Mobile
Robots (AMRs) have become indispensable in industrial applications due to their operational flexibility and efficiency.
Navigation serves as a crucial technical foundation for accomplishing complex tasks. However, navigating AMRs in dense, cluttered, and semi-structured environments remains challenging, primarily due to nonholonomic vehicle dynamics, interactions with mixed static/dynamic obstacles, and the non-convex constrained nature of such operational spaces. To solve these problems, this paper proposes an Improved Sequential Model Predictive Control (ISMPC)
navigation framework that systematically reformulates
navigation tasks as sequential switched optimal control problems. The framework addresses the aforementioned challenges through two key innovations: 1) Implementation of a Multi-Directional Safety Rectangular Corridor (MDSRC)
algorithm, which encodes the free space through rectangular convex regions to avoid collision with static obstacles, eliminating redundant computational burdens and accelerating solver convergence; 2) A sequential MPC
navigation framework that integrates corridor constraints with barrier function constraints is proposed to achieve static and dynamic obstacle avoidance. The ISMPC
navigation framework enables direct velocity generation for AMRs, simplifying traditional
navigation algorithm architectures. Comparative experiments demonstrate the framework's superiority in free-space utilization ( an increase of 41.05$\%$ in the average corridor area) while maintaining real-time computational performance (average corridors generation latency of 3 ms).
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Robotics
Differentiable Material Point Method for the Control of Deformable Objects
Diego Bolliger, Gabriele Fadini, Markus Bambach et al. (+1 more)
Controlling the deformation of flexible objects is challenging due to their non-linear dynamics and high-dimensional configuration space. This work presents a differentiable Material Point Method (MPM) simulator targeted at control applications. We exploit the differentiability of the simulator to optimize a control trajectory in an active damping problem for a hyperelastic rope. The simulator effectively minimizes the kinetic energy of the rope around 2$\times$ faster than a baseline MPPI method and to a 20% lower energy level, while using about 3% of the computation time.
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All authors Diego Bolliger, Gabriele Fadini, Markus Bambach, Alisa Rupenyan
Robotics
ALBATROSS: A robotised system for high-throughput electrolyte screening via automated electrolyte formulation, coin-cell fabrication, and electrochemical evaluation
Hyun-Gi Lee, Jaekyeong Han, Minjun Kwon et al. (+4 more)
As battery technologies advance toward higher stability and energy density, the need for extensive
cell-level testing across various component configurations becomes critical. To evaluate performance and understand the operating principles of batteries in laboratory scale, fabrication and evaluation of coin
cells are essential processes. However, the conventional coin-
cell assembly and testing processes require significant time and labor from researchers, posing challenges to high-throughput screening research. In this study, we introduce an Automated Li-ion BAttery Testing
RObot SyStem (ALBATROSS), an automated system capable of electrolyte formulation, coin-
cell assembly, and electrochemical evaluation. The system, integrated within a argon-filled glovebox, enables fully automated assembly and testing of up to 48
cells without researcher intervention. By incorporating custom-designed
robot gripper and 3D-printed structures optimized for precise
cell handling, ALBATROSS achieved high assembly reliability, yielding a relative standard deviation (RSD) of less than 1.2% in discharge capacity and a standard deviation of less than 3 {\Omega} in EIS measurements for NCM811||Li half
cells. Owing to its high reliability and automation capability, ALBATROSS allows for the acquisition of high-quality coin-
cell datasets, which are expected to accelerate the development of next-generation electrolytes.
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All authors Hyun-Gi Lee, Jaekyeong Han, Minjun Kwon, Hyeonuk Kwon, Jooha Park, Hoe Jin Ha, Dong-Hwa Seo
Robotics
Efficient Path Generation with Curvature Guarantees by Mollification
Alfredo González-Calvin, Juan F.Jiménez and Héctor García de Marina
Path generation, the process of converting high-level mission specifications, such as sequences of waypoints from a path planner, into smooth, executable paths, is a fundamental challenge in mobile robotics. Most path following and trajectory tracking
algorithms require the desired path to be defined by at least twice continuously differentiable functions to guarantee key properties such as global convergence, especially for nonholonomic
robots like unicycles with speed constraints. Consequently, path generation methods must bridge the gap between convenient but non-differentiable planning outputs, such as piecewise linear segments, and the differentiability requirements imposed by downstream control
algorithms. While techniques such as spline interpolation or optimization-based methods are commonly used to smooth non-differentiable paths or create feasible ones from sequences of waypoints, they either produce unnecessarily complex trajectories or are computationally expensive. In this work, we present a method to regularize non-differentiable functions and generate feasible paths through mollification. Specifically, we approximate an arbitrary path with a differentiable function that can converge to it with arbitrary precision. Additionally, we provide a systematic method for bounding the
curvature of generated paths, which we demonstrate by applying it to paths resulting from linking a sequence of waypoints with segments. The proposed approach is analytically shown to be computationally more efficient than standard interpolation methods, enabling real-time implementation on microcontrollers, while remaining compatible with standard trajectory tracking and path following
algorithms.
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Robotics · Cross-listed from cs.CV
MMDrive: Interactive Scene Understanding Beyond Vision with Multi-representational Fusion
Minghui Hou, Wei-Hsing Huang, Shaofeng Liang et al. (+5 more)
Vision-
language models enable the understanding and
reasoning of complex traffic scenarios through multi-source information fusion, establishing it as a core technology for autonomous driving. However, existing
vision-
language models are constrained by the image understanding paradigm in 2D plane, which restricts their capability to perceive 3D spatial information and perform deep semantic fusion, resulting in suboptimal performance in complex autonomous driving environments. This study proposes MMDrive, an
multimodal vision-
language model framework that extends traditional image understanding to a generalized 3D scene understanding framework. MMDrive incorporates three complementary modalities, including occupancy maps,
LiDAR point clouds, and textual scene descriptions. To this end, it introduces two novel components for adaptive cross-modal fusion and key information extraction. Specifically, the Text-oriented
Multimodal Modulator dynamically weights the contributions of each modality based on the semantic cues in the question, guiding context-aware feature integration. The Cross-Modal Abstractor employs learnable abstract
tokens to generate compact, cross-modal summaries that highlight key regions and essential semantics. Comprehensive evaluations on the DriveLM and NuScenes-QA
benchmarks demonstrate that MMDrive achieves significant performance gains over existing
vision-
language models for autonomous driving, with a BLEU-4 score of 54.56 and METEOR of 41.78 on DriveLM, and an accuracy score of 62.7% on NuScenes-QA. MMDrive effectively breaks the traditional image-only understanding barrier, enabling robust
multimodal reasoning in complex driving environments and providing a new foundation for interpretable autonomous driving scene understanding.
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All authors Minghui Hou, Wei-Hsing Huang, Shaofeng Liang, Daizong Liu, Tai-Hao Wen, Gang Wang, Runwei Guan, Weiping Ding
Robotics
Iterative Tuning of Nonlinear Model Predictive Control for Robotic Manufacturing Tasks
Deepak Ingole, Valentin Bhend, Shiva Ganesh Murali et al. (+2 more)
Manufacturing processes are often perturbed by drifts in the environment and wear in the system, requiring control re-tuning even in the presence of repetitive operations. This paper presents an iterative learning framework for automatic tuning of Nonlinear Model Predictive Control (NMPC) weighting
matrices based on task-level performance feedback. Inspired by norm-optimal Iterative Learning Control (ILC), the proposed method adaptively adjusts NMPC weights Q and R across task repetitions to minimize key performance indicators (KPIs) related to tracking accuracy, control effort, and saturation. Unlike gradient-based approaches that require differentiating through the NMPC solver, we construct an empirical sensitivity
matrix, enabling structured weight updates without analytic derivatives. The framework is validated through simulation on a UR10e
robot performing carbon fiber winding on a tetrahedral core. Results demonstrate that the proposed approach converges to near-optimal tracking performance (RMSE within 0.3% of offline
Bayesian Optimization (BO)) in just 4 online repetitions, compared to 100 offline evaluations required by BO
algorithm. The method offers a practical solution for adaptive NMPC tuning in repetitive robotic tasks, combining the precision of carefully optimized controllers with the flexibility of online adaptation.
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All authors Deepak Ingole, Valentin Bhend, Shiva Ganesh Murali, Oliver Dobrich, Alisa Rupenyan
Robotics
START: Traversing Sparse Footholds with Terrain Reconstruction
Ruiqi Yu, Qianshi Wang, Hongyi Li et al. (+4 more)
Traversing terrains with sparse footholds like legged animals presents a promising yet challenging task for quadruped
robots, as it requires precise environmental perception and agile control to secure safe foot placement while maintaining dynamic stability. Model-based hierarchical controllers excel in laboratory settings, but suffer from limited generalization and overly conservative behaviors. End-to-end learning-based approaches unlock greater flexibility and adaptability, but existing state-of-the-art methods either rely on heightmaps that introduce noise and complex, costly pipelines, or implicitly infer terrain features from
egocentric depth images, often missing accurate critical
geometric cues and leading to inefficient learning and rigid gaits. To overcome these limitations, we propose START, a single-stage learning framework that enables agile, stable locomotion on highly sparse and randomized footholds. START leverages only low-cost onboard
vision and proprioception to accurately reconstruct local terrain heightmap, providing an explicit intermediate representation to convey essential features relevant to sparse foothold regions. This supports comprehensive environmental understanding and precise terrain assessment, reducing exploration cost and accelerating skill acquisition. Experimental results demonstrate that START achieves zero-shot transfer across diverse real-world scenarios, showcasing superior adaptability, precise foothold placement, and robust locomotion.
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All authors Ruiqi Yu, Qianshi Wang, Hongyi Li, Zheng Jun, Zhicheng Wang, Jun Wu, Qiuguo Zhu
Robotics
OXE-AugE: A Large-Scale Robot Augmentation of OXE for Scaling Cross-Embodiment Policy Learning
Guanhua Ji, Harsha Polavaram, Lawrence Yunliang Chen et al. (+5 more)
Large and diverse
datasets are needed for training generalist
robot policies that have potential to control a variety of
robot embodiments –
robot arm and gripper combinations – across diverse tasks and environments. As re-collecting demonstrations and retraining for each new hardware platform are prohibitively costly, we show that existing
robot data can be augmented for transfer and generalization. The Open X-Embodiment (OXE)
dataset, which aggregates demonstrations from over 60
robot datasets, has been widely used as the foundation for training generalist policies. However, it is highly imbalanced: the top four
robot types account for over 85% of its real data, which risks overfitting to
robot-scene combinations. We present AugE-Toolkit, a scalable
robot augmentation pipeline, and OXE-AugE, a high-quality open-source
dataset that augments OXE with 9 different
robot embodiments. OXE-AugE provides over 4.4 million trajectories, more than triple the size of the original OXE. We conduct a systematic study of how scaling
robot augmentation impacts cross-embodiment learning. Results suggest that augmenting
datasets with diverse arms and grippers improves policy performance not only on the augmented
robots, but also on unseen
robots and even the original
robots under distribution shifts. In physical experiments, we demonstrate that state-of-the-art generalist policies such as OpenVLA and $\pi_0$ benefit from fine-tuning on OXE-AugE, improving success rates by 24-45% on previously unseen
robot-gripper combinations across four real-world manipulation tasks. Project website: https://OXE-AugE.github.io/.
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All authors Guanhua Ji, Harsha Polavaram, Lawrence Yunliang Chen, Sandeep Bajamahal, Zehan Ma, Simeon Adebola, Chenfeng Xu, Ken Goldberg
Robotics
Sequence of Expert: Boosting Imitation Planners for Autonomous Driving through Temporal Alternation
Xiang Li, Gang Liu, Weitao Zhou et al. (+2 more)
Imitation learning (IL) has emerged as a central paradigm in autonomous driving. While IL excels in matching expert behavior in open-loop settings by minimizing per-step prediction errors, its performance degrades unexpectedly in closed-loop due to the gradual accumulation of small, often imperceptible errors over time.Over successive planning cycles, these errors compound, potentially resulting in severe failures.Current research efforts predominantly rely on increasingly sophisticated network architectures or high-fidelity training
datasets to enhance the robustness of IL planners against error accumulation, focusing on the state-level robustness at a single time point. However, autonomous driving is inherently a continuous-time process, and leveraging the temporal scale to enhance robustness may provide a new perspective for addressing this issue.To this end, we propose a method termed Sequence of Experts (SoE), a temporal alternation policy that enhances closed-loop performance without increasing model size or data requirements. Our experiments on large-scale autonomous driving
benchmarks nuPlan demonstrate that SoE method consistently and significantly improves the performance of all the evaluated models, and achieves state-of-the-art performance.This module may provide a key and widely applicable support for improving the training efficiency of autonomous driving models.
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All authors Xiang Li, Gang Liu, Weitao Zhou, Hongyi Zhu, Zhong Cao
Robotics
PvP: Data-Efficient Humanoid Robot Learning with Proprioceptive-Privileged Contrastive Representations
Mingqi Yuan, Tao Yu, Haolin Song et al. (+4 more)
Achieving efficient and robust whole-body control (WBC) is essential for enabling
humanoid robots to perform complex tasks in dynamic environments. Despite the success of reinforcement learning (RL) in this domain, its sample inefficiency remains a significant challenge due to the intricate dynamics and partial observability of
humanoid robots. To address this limitation, we propose PvP, a Proprioceptive-Privileged contrastive learning framework that leverages the intrinsic complementarity between proprioceptive and privileged states. PvP learns compact and task-relevant latent representations without requiring hand-crafted data augmentations, enabling faster and more stable policy learning. To support systematic evaluation, we develop SRL4
Humanoid, the first unified and modular framework that provides high-quality implementations of representative state representation learning (SRL) methods for
humanoid robot learning. Extensive experiments on the LimX Oli
robot across velocity tracking and motion imitation tasks demonstrate that PvP significantly improves sample efficiency and final performance compared to baseline SRL methods. Our study further provides practical insights into integrating SRL with RL for
humanoid WBC, offering valuable guidance for data-efficient
humanoid robot learning.
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All authors Mingqi Yuan, Tao Yu, Haolin Song, Bo Li, Xin Jin, Hua Chen, Wenjun Zeng
Robotics
Jebeom Chae, Junwoo Chang, Seungho Yeom et al. (+2 more)
Diffusion models have recently emerged as powerful tools for
robot motion planning by capturing the multi-modal distribution of feasible trajectories. However, their extension to multi-
robot settings with flexible,
language-conditioned task specifications remains limited. Furthermore, current
diffusion-based approaches incur high computational cost during inference and struggle with generalization because they require explicit construction of environment representations and lack mechanisms for
reasoning about
geometric reachability. To address these limitations, we present
Language-conditioned Heat-inspired
Diffusion (LHD), an end-to-end
vision-based framework that generates
language-conditioned, collision-free trajectories. LHD integrates semantic priors from CLIP, a
vision-
language model (VLM), with a collision-avoiding
diffusion kernel serving as a physical inductive bias that enables the planner to interpret
language commands strictly within the reachable workspace. This naturally handles out-of-distribution (OOD) scenarios – in terms of reachability – by guiding
robots toward accessible alternatives that match the semantic intent, while eliminating the need for explicit obstacle information at inference time. Extensive evaluations on diverse real-world-inspired maps, along with real-
robot experiments, show that LHD consistently outperforms prior
diffusion-based planners in success rate, while reducing planning latency. Project page is available at: https://jebeom.github.io/lhd_project_page/
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All authors Jebeom Chae, Junwoo Chang, Seungho Yeom, Yujin Kim, Jongeun Choi
Robotics
Spatial-Aware VLA Pretraining through Visual-Physical Alignment from Human Videos
Yicheng Feng, Wanpeng Zhang, Ye Wang et al. (+4 more)
Vision-
Language-Action (VLA) models provide a promising paradigm for
robot learning by integrating visual perception with
language-guided policy learning. However, most existing approaches rely on 2D visual inputs to perform actions in 3D physical environments, creating a significant gap between perception and action grounding. To bridge this gap, we propose a Spatial-Aware VLA Pretraining paradigm that performs explicit
alignment between visual space and physical space during pretraining, enabling models to acquire 3D spatial understanding before
robot policy learning. Starting from pretrained
vision-
language models, we leverage large-scale human demonstration
videos to extract 3D visual and 3D action annotations, forming a new source of supervision that aligns 2D visual observations with 3D spatial
reasoning. We instantiate this paradigm with VIPA-VLA, a dual-encoder architecture that incorporates a 3D visual encoder to augment semantic visual representations with 3D-aware features. When adapted to downstream
robot tasks, VIPA-VLA achieves significantly improved grounding between 2D
vision and 3D action, resulting in more robust and generalizable robotic policies.
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All authors Yicheng Feng, Wanpeng Zhang, Ye Wang, Hao Luo, Haoqi Yuan, Sipeng Zheng, Zongqing Lu
Robotics · Cross-listed from cs.CV
Motus: A Unified Latent Action World Model
Hongzhe Bi, Hengkai Tan, Shenghao Xie et al. (+13 more)
While a general
embodied agent must function as a unified system, current methods are built on isolated models for understanding, world modeling, and control. This fragmentation prevents unifying
multimodal generative capabilities and hinders learning from large-scale, heterogeneous data. In this paper, we propose Motus, a unified latent action world model that leverages existing general pretrained models and rich, sharable motion information. Motus introduces a Mixture-of-
Transformer (MoT) architecture to integrate three experts (i.e., understanding,
video generation, and action) and adopts a UniDiffuser-style scheduler to enable flexible switching between different modeling modes (i.e., world models,
vision-
language-action models, inverse dynamics models,
video generation models, and
video-action joint prediction models). Motus further leverages the optical flow to learn latent actions and adopts a recipe with three-phase training pipeline and six-layer data pyramid, thereby extracting pixel-level "delta action" and enabling large-scale action pretraining. Experiments show that Motus achieves superior performance against state-of-the-art methods in both simulation (a +15% improvement over X-VLA and a +45% improvement over Pi0.5) and real-world scenarios(improved by +11~48%), demonstrating unified modeling of all functionalities and priors significantly benefits downstream robotic tasks.
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All authors Hongzhe Bi, Hengkai Tan, Shenghao Xie, Zeyuan Wang, Shuhe Huang, Haitian Liu, Ruowen Zhao, Yao Feng, Chendong Xiang, Yinze Rong, Hongyan Zhao, Hanyu Liu, Zhizhong Su, Lei Ma, Hang Su, Jun Zhu
Robotics
K-VARK: Kernelized Variance-Aware Residual Kalman Filter for Sensorless Force Estimation in Collaborative Robots
Oğuzhan Akb{\i}y{\i}k, Naseem Alhousani and Fares J. Abu-Dakka
Reliable estimation of contact forces is crucial for ensuring safe and precise interaction of
robots with unstructured environments. However, accurate sensorless force estimation remains challenging due to inherent modeling errors and complex residual dynamics and friction. To address this challenge, in this paper, we propose K-VARK (Kernelized Variance-Aware Residual Kalman filter), a novel approach that integrates a kernelized, probabilistic model of joint residual torques into an adaptive Kalman filter framework. Through Kernelized Movement Primitives trained on optimized excitation trajectories, K-VARK captures both the predictive mean and input-dependent heteroscedastic variance of residual torques, reflecting data variability and distance-to-training effects. These statistics inform a variance-aware virtual measurement update by augmenting the measurement noise covariance, while the process noise covariance adapts online via variational
Bayesian optimization to handle dynamic disturbances. Experimental validation on a 6-DoF collaborative manipulator demonstrates that K-VARK achieves over 20% reduction in RMSE compared to state-of-the-art sensorless force estimation methods, yielding robust and accurate external force/torque estimation suitable for advanced tasks such as polishing and assembly.
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Robotics
Learning Terrain Aware Bipedal Locomotion via Reduced Dimensional Perceptual Representations
Guillermo A. Castillo, Himanshu Lodha and Ayonga Hereid
This work introduces a hierarchical strategy for terrain-aware bipedal locomotion that integrates reduced-dimensional perceptual representations to enhance reinforcement learning (RL)-based high-level (HL) policies for real-time gait generation. Unlike end-to-end approaches, our framework leverages latent terrain encodings via a Convolutional Variational Autoencoder (CNN-VAE) alongside reduced-order
robot dynamics, optimizing the locomotion decision process with a compact state. We systematically analyze the impact of latent space dimensionality on learning efficiency and policy robustness. Additionally, we extend our method to be history-aware, incorporating sequences of recent terrain observations into the latent representation to improve robustness. To address real-world feasibility, we introduce a
distillation method to learn the latent representation directly from depth camera images and provide preliminary hardware validation by comparing simulated and real sensor data. We further validate our framework using the high-fidelity Agility Robotics (AR) simulator, incorporating realistic sensor noise, state estimation, and actuator dynamics. The results confirm the robustness and adaptability of our method, underscoring its potential for hardware deployment.
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Robotics
Tackling Snow-Induced Challenges: Safe Autonomous Lane-Keeping with Robust Reinforcement Learning
Amin Jalal Aghdasian, Farzaneh Abdollahi and Ali Kamali Iglie
This paper proposes two new
algorithms for the lane keeping system (LKS) in autonomous vehicles (AVs) operating under snowy road conditions. These
algorithms use deep reinforcement learning (DRL) to handle uncertainties and slippage. They include Action-Robust Recurrent Deep Deterministic Policy Gradient (AR-RDPG) and end-to-end Action-Robust convolutional neural network
Attention Deterministic Policy Gradient (AR-CADPG), two action-robust approaches for decision-making. In the AR-RDPG method, within the perception layer, camera images are first denoised using multi-scale neural networks. Then, the centerline coefficients are extracted by a pre-trained deep convolutional neural network (DCNN). These coefficients, concatenated with the driving characteristics, are used as input to the control layer. The AR-CADPG method presents an end-to-end approach in which a convolutional neural network (CNN) and an
attention mechanism are integrated within a DRL framework. Both methods are first trained in the CARLA simulator and validated under various snowy scenarios. Real-world experiments on a Jetson Nano-based autonomous vehicle confirm the feasibility and stability of the learned policies. Among the two models, the AR-CADPG approach demonstrates superior path-tracking accuracy and robustness, highlighting the effectiveness of combining temporal
memory,
adversarial resilience, and
attention mechanisms in AVs.
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Robotics
SLIM-VDB: A Real-Time 3D Probabilistic Semantic Mapping Framework
Anja Sheppard, Parker Ewen, Joey Wilson et al. (+6 more)
This paper introduces SLIM-VDB, a new lightweight semantic mapping system with probabilistic semantic fusion for closed-set or open-set dictionaries. Advances in data structures from the computer graphics community, such as OpenVDB, have demonstrated significantly improved computational and
memory efficiency in volumetric scene representation. Although OpenVDB has been used for
geometric mapping in robotics applications, semantic mapping for scene understanding with OpenVDB remains unexplored. In addition, existing semantic mapping systems lack support for integrating both fixed-category and open-
language label predictions within a single framework. In this paper, we propose a novel 3D semantic mapping system that leverages the OpenVDB data structure and integrates a unified
Bayesian update framework for both closed- and open-set semantic fusion. Our proposed framework, SLIM-VDB, achieves significant reduction in both
memory and integration times compared to current state-of-the-art semantic mapping approaches, while maintaining comparable mapping accuracy. An open-source C++ codebase with a Python interface is available at https://github.com/umfieldrobotics/slim-vdb.
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All authors Anja Sheppard, Parker Ewen, Joey Wilson, Advaith V. Sethuraman, Benard Adewole, Anran Li, Yuzhen Chen, Ram Vasudevan, Katherine A. Skinner