Robotics
BagelVLA: Enhancing Long-Horizon Manipulation via Interleaved Vision-Language-Action Generation
Yucheng Hu, Jianke Zhang, Yuanfei Luo et al. (+9 more)
Equipping
embodied agents with the ability to reason about tasks, foresee physical outcomes, and generate precise actions is essential for general-purpose manipulation. While recent
Vision-
Language-Action (VLA) models have leveraged pre-trained foundation models, they typically focus on either linguistic planning or visual
forecasting in isolation. These methods rarely integrate both capabilities simultaneously to guide action generation, leading to suboptimal performance in complex, long-horizon manipulation tasks. To bridge this gap, we propose BagelVLA, a unified model that integrates linguistic planning, visual
forecasting, and action generation within a single framework. Initialized from a pretrained unified understanding and generative model, BagelVLA is trained to interleave textual
reasoning and visual prediction directly into the action execution loop. To efficiently couple these modalities, we introduce Residual Flow Guidance (RFG), which initializes from current observation and leverages single-step denoising to extract predictive visual features, guiding action generation with minimal latency. Extensive experiments demonstrate that BagelVLA outperforms existing baselines by a significant margin on multiple simulated and real-world
benchmarks, particularly in tasks requiring multi-stage
reasoning.
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All authors Yucheng Hu, Jianke Zhang, Yuanfei Luo, Yanjiang Guo, Xiaoyu Chen, Xinshu Sun, Kun Feng, Qingzhou Lu, Sheng Chen, Yangang Zhang, Wei Li, Jianyu Chen
Robotics
Design and Evaluation of an Assisted Programming Interface for Behavior Trees in Robotics
Jonathan Styrud, Matteo Iovino, Rebecca Stower et al. (+4 more)
The possibility to create reactive
robot programs faster without the need for extensively trained programmers is becoming increasingly important. So far, it has not been explored how various techniques for creating Behavior Tree (BT) program representations could be combined with complete graphical user interfaces (GUIs) to allow a human user to validate and edit trees suggested by automated methods. In this paper, we introduce BEhavior TRee GUI (BETR-GUI) for creating BTs with the help of an AI assistant that combines methods using large
language models, planning, genetic programming, and
Bayesian optimization with a drag-and-drop editor. A user study with 60 participants shows that by combining different assistive methods, BETR-GUI enables users to perform better at solving the
robot programming tasks. The results also show that humans using the full variant of BETR-GUI perform better than the AI assistant running on its own.
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All authors Jonathan Styrud, Matteo Iovino, Rebecca Stower, Mart Kartašev, Mikael Norrlöf, Mårten Björkman, Christian Smith
Robotics
Diverse Skill Discovery for Quadruped Robots via Unsupervised Learning
Ruopeng Cui, Yifei Bi, Haojie Luo et al. (+1 more)
Reinforcement learning necessitates meticulous reward shaping by specialists to elicit target behaviors, while imitation learning relies on costly task-specific data. In contrast, unsupervised skill discovery can potentially reduce these burdens by learning a diverse repertoire of useful skills driven by intrinsic motivation. However, existing methods exhibit two key limitations: they typically rely on a single policy to master a versatile repertoire of behaviors without modeling the shared structure or distinctions among them, which results in low learning efficiency; moreover, they are susceptible to reward hacking, where the reward signal increases and converges rapidly while the learned skills display insufficient actual diversity. In this work, we introduce an Orthogonal Mixture-of-Experts (OMoE) architecture that prevents diverse behaviors from collapsing into overlapping representations, enabling a single policy to master a wide spectrum of locomotion skills. In addition, we design a multi-discriminator framework in which different discriminators operate on distinct observation spaces, effectively mitigating reward hacking. We evaluated our method on the 12-DOF Unitree A1 quadruped
robot, demonstrating a diverse set of locomotion skills. Our experiments demonstrate that the proposed framework boosts training efficiency and yields an 18.3% expansion in state-space coverage compared to the baseline.
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All authors Ruopeng Cui, Yifei Bi, Haojie Luo, Wei Li
Robotics
NavDreamer: Video Models as Zero-Shot 3D Navigators
Xijie Huang, Weiqi Gai, Tianyue Wu et al. (+5 more)
Previous
Vision-
Language-Action models face critical limitations in
navigation: scarce, diverse data from labor-intensive collection and static representations that fail to capture temporal dynamics and physical laws. We propose NavDreamer, a
video-based framework for 3D
navigation that leverages generative
video models as a universal interface between
language instructions and
navigation trajectories. Our main hypothesis is that
video's ability to encode spatiotemporal information and physical dynamics, combined with internet-scale availability, enables strong zero-shot generalization in
navigation. To mitigate the stochasticity of generative predictions, we introduce a sampling-based optimization method that utilizes a VLM for trajectory scoring and selection. An inverse dynamics model is employed to decode executable waypoints from generated
video plans for
navigation. To systematically evaluate this paradigm in several
video model backbones, we introduce a comprehensive
benchmark covering object
navigation, precise
navigation, spatial grounding,
language control, and scene
reasoning. Extensive experiments demonstrate robust generalization across novel objects and unseen environments, with ablation studies revealing that
navigation's high-level decision-making nature makes it particularly suited for
video-based planning.
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All authors Xijie Huang, Weiqi Gai, Tianyue Wu, Congyu Wang, Zhiyang Liu, Xin Zhou, Yuze Wu, Fei Gao
Robotics
Rethinking Visual-Language-Action Model Scaling: Alignment, Mixture, and Regularization
Ye Wang, Sipeng Zheng, Hao Luo et al. (+9 more)
While
Vision-
Language-Action (VLA) models show strong promise for generalist
robot control, it remains unclear whether – and under what conditions – the standard "scale data" recipe translates to robotics, where training data is inherently heterogeneous across embodiments, sensors, and action spaces. We present a systematic, controlled study of VLA scaling that revisits core training choices for pretraining across diverse
robots. Using a representative VLA framework that combines a
vision-
language backbone with flow-matching, we ablate key design decisions under matched conditions and evaluate in extensive simulation and real-
robot experiments. To improve the reliability of real-world results, we introduce a Grouped Blind Ensemble protocol that blinds operators to model identity and separates policy execution from outcome judgment, reducing experimenter bias. Our analysis targets three dimensions of VLA scaling. (1) Physical
alignment: we show that a unified end-effector (EEF)-relative action representation is critical for robust cross-embodiment transfer. (2) Embodiment mixture: we find that naively pooling heterogeneous
robot datasets often induces negative transfer rather than gains, underscoring the fragility of indiscriminate data scaling. (3) Training regularization: we observe that intuitive strategies, such as sensory dropout and multi-stage fine-tuning, do not consistently improve performance at scale. Together, this study challenge some common assumptions about
embodied scaling and provide practical guidance for training large-scale VLA policies from diverse robotic data. Project website: https://research.beingbeyond.com/rethink_vla
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All authors Ye Wang, Sipeng Zheng, Hao Luo, Wanpeng Zhang, Haoqi Yuan, Chaoyi Xu, Haiweng Xu, Yicheng Feng, Mingyang Yu, Zhiyu Kang, Zongqing Lu, Qin Jin
Robotics
Fast Motion Planning for Non-Holonomic Mobile Robots via a Rectangular Corridor Representation of Structured Environments
Alejandro Gonzalez-Garcia, Sebastiaan Wyns, Sonia De Santis et al. (+2 more)
We present a complete framework for fast motion planning of non-holonomic autonomous mobile
robots in highly complex but structured environments. Conventional grid-based planners struggle with scalability, while many kinematically-feasible planners impose a significant computational burden due to their search space complexity. To overcome these limitations, our approach introduces a deterministic free-space decomposition that creates a compact graph of overlapping rectangular corridors. This method enables a significant reduction in the search space, without sacrificing path resolution. The framework then performs online motion planning by finding a sequence of rectangles and generating a near-time-optimal, kinematically-feasible trajectory using an analytical planner. The result is a highly efficient solution for large-scale
navigation. We validate our framework through extensive simulations and on a physical
robot. The implementation is publicly available as open-source software.
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All authors Alejandro Gonzalez-Garcia, Sebastiaan Wyns, Sonia De Santis, Jan Swevers, Wilm Decré
Robotics
RANT: Ant-Inspired Multi-Robot Rainforest Exploration Using Particle Filter Localisation and Virtual Pheromone Coordination
Ameer Alhashemi, Layan Abdulhadi, Karam Abuodeh et al. (+2 more)
This paper presents RANT, an ant-inspired multi-
robot exploration framework for noisy, uncertain environments. A team of differential-drive
robots navigates a 10 x 10 m terrain, collects noisy probe measurements of a hidden richness field, and builds local probabilistic maps while the supervisor maintains a global evaluation. RANT combines particle-filter localisation, a behaviour-based controller with gradient-driven hotspot exploitation, and a lightweight no-revisit coordination mechanism based on virtual pheromone blocking. We experimentally analyse how team size, localisation fidelity, and coordination influence coverage, hotspot recall, and redundancy. Results show that particle filtering is essential for reliable hotspot engagement, coordination substantially reduces overlap, and increasing team size improves coverage but yields diminishing returns due to interference.
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All authors Ameer Alhashemi, Layan Abdulhadi, Karam Abuodeh, Tala Baghdadi, Suryanarayana Datla
Robotics
Xiaolou Sun, Wufei Si, Wenhui Ni et al. (+10 more)
Vision-
language navigation (VLN) requires intelligent
agents to navigate environments by interpreting linguistic instructions alongside visual observations, serving as a cornerstone task in
Embodied AI. Current VLN research for unmanned aerial vehicles (
UAVs) relies on detailed, pre-specified instructions to guide the
UAV along predetermined routes. However, real-world outdoor exploration typically occurs in unknown environments where detailed
navigation instructions are unavailable. Instead, only coarse-grained positional or directional guidance can be provided, requiring
UAVs to autonomously navigate through continuous planning and obstacle avoidance. To bridge this gap, we propose AutoFly, an end-to-end
Vision-
Language-Action (VLA) model for autonomous
UAV navigation. AutoFly incorporates a pseudo-depth encoder that derives depth-aware features from RGB inputs to enhance spatial
reasoning, coupled with a progressive two-stage training strategy that effectively aligns visual, depth, and linguistic representations with action policies. Moreover, existing VLN
datasets have fundamental limitations for real-world autonomous
navigation, stemming from their heavy reliance on explicit instruction-following over autonomous decision-making and insufficient real-world data. To address these issues, we construct a novel autonomous
navigation dataset that shifts the paradigm from instruction-following to autonomous behavior modeling through: (1) trajectory collection emphasizing continuous obstacle avoidance, autonomous planning, and recognition workflows; (2) comprehensive real-world data integration. Experimental results demonstrate that AutoFly achieves a 3.9% higher success rate compared to state-of-the-art VLA baselines, with consistent performance across simulated and real environments.
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All authors Xiaolou Sun, Wufei Si, Wenhui Ni, Yuntian Li, Dongming Wu, Fei Xie, Runwei Guan, He-Yang Xu, Henghui Ding, Yuan Wu, Yutao Yue, Yongming Huang, Hui Xiong
Robotics
TeleGate: Whole-Body Humanoid Teleoperation via Gated Expert Selection with Motion Prior
Jie Li, Bing Tang and Feng Wu
Real-time whole-body teleoperation is a critical method for
humanoid robots to perform complex tasks in unstructured environments. However, developing a unified controller that robustly supports diverse human motions remains a significant challenge. Existing methods typically distill multiple expert policies into a single general policy, which often inevitably leads to performance degradation, particularly on highly dynamic motions. This paper presents TeleGate, a unified whole-body teleoperation framework for
humanoid robots that achieves high-precision tracking across various motions while avoiding the performance loss inherent in knowledge
distillation. Our key idea is to preserve the full capability of domain-specific expert policies by training a lightweight gating network, which dynamically activates experts in real-time based on proprioceptive states and reference trajectories. Furthermore, to compensate for the absence of future reference trajectories in real-time teleoperation, we introduce a VAE-based motion prior module that extracts implicit future motion intent from historical observations, enabling anticipatory control for motions requiring prediction such as jumping and standing up. We conducted empirical evaluations in simulation and also deployed our technique on the Unitree G1
humanoid robot. Using only 2.5 hours of motion capture data for training, our TeleGate achieves high-precision real-time teleoperation across diverse dynamic motions (e.g., running, fall recovery, and jumping), significantly outperforming the baseline methods in both tracking accuracy and success rate.
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Robotics
AnyTouch 2: General Optical Tactile Representation Learning For Dynamic Tactile Perception
Ruoxuan Feng, Yuxuan Zhou, Siyu Mei et al. (+6 more)
Real-world contact-rich manipulation demands
robots to perceive temporal tactile feedback, capture subtle surface deformations, and reason about object properties as well as force dynamics. Although optical tactile sensors are uniquely capable of providing such rich information, existing tactile
datasets and models remain limited. These resources primarily focus on object-level attributes (e.g., material) while largely overlooking fine-grained tactile temporal dynamics during physical interactions. We consider that advancing dynamic tactile perception requires a systematic hierarchy of dynamic perception capabilities to guide both data collection and model design. To address the lack of tactile data with rich dynamic information, we present ToucHD, a large-scale hierarchical tactile
dataset spanning tactile atomic actions, real-world manipulations, and touch-force paired data. Beyond scale, ToucHD establishes a comprehensive tactile dynamic data ecosystem that explicitly supports hierarchical perception capabilities from the data perspective. Building on it, we propose AnyTouch 2, a general tactile representation learning framework for diverse optical tactile sensors that unifies object-level understanding with fine-grained, force-aware dynamic perception. The framework captures both pixel-level and action-specific deformations across frames, while explicitly modeling physical force dynamics, thereby learning multi-level dynamic perception capabilities from the model perspective. We evaluate our model on
benchmarks that covers static object properties and dynamic physical attributes, as well as real-world manipulation tasks spanning multiple tiers of dynamic perception capabilities-from basic object-level understanding to force-aware dexterous manipulation. Experimental results demonstrate consistent and strong performance across sensors and tasks.
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All authors Ruoxuan Feng, Yuxuan Zhou, Siyu Mei, Dongzhan Zhou, Pengwei Wang, Shaowei Cui, Bin Fang, Guocai Yao, Di Hu
Robotics
Preference Aligned Visuomotor Diffusion Policies for Deformable Object Manipulation
Marco Moletta, Michael C. Welle and Danica Kragic
Humans naturally develop preferences for how manipulation tasks should be performed, which are often subtle, personal, and difficult to articulate. Although it is important for
robots to account for these preferences to increase personalization and user satisfaction, they remain largely underexplored in robotic manipulation, particularly in the context of deformable objects like garments and fabrics. In this work, we study how to adapt pretrained visuomotor
diffusion policies to reflect preferred behaviors using limited demonstrations. We introduce RKO, a novel preference-
alignment method that combines the benefits of two recent frameworks: RPO and KTO. We evaluate RKO against common preference learning frameworks, including these two, as well as a baseline vanilla
diffusion policy, on real-world cloth-folding tasks spanning multiple garments and preference settings. We show that preference-aligned policies (particularly RKO) achieve superior performance and sample efficiency compared to standard
diffusion policy fine-tuning. These results highlight the importance and feasibility of structured preference learning for scaling personalized
robot behavior in complex deformable object manipulation tasks.
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Robotics
SERNF: Sample-Efficient Real-World Dexterous Policy Fine-Tuning via Action-Chunked Critics and Normalizing Flows
Chenyu Yang, Denis Tarasov, Davide Liconti et al. (+3 more)
Real-world fine-tuning of dexterous manipulation policies remains challenging due to limited real-world interaction budgets and highly
multimodal action distributions.
Diffusion-based policies, while expressive, do not permit conservative likelihood-based updates during fine-tuning because action probabilities are intractable. In contrast, conventional Gaussian policies collapse under multimodality, particularly when actions are executed in chunks, and standard per-step critics fail to align with chunked execution, leading to poor credit assignment. We present SERFN, a sample-efficient off-policy fine-tuning framework with normalizing flow (NF) to address these challenges. The normalizing flow policy yields exact likelihoods for
multimodal action chunks, allowing conservative, stable policy updates through likelihood regularization and thereby improving sample efficiency. An action-chunked critic evaluates entire action sequences, aligning value estimation with the policy's temporal structure and improving long-horizon credit assignment. To our knowledge, this is the first demonstration of a likelihood-based,
multimodal generative policy combined with chunk-level value learning on real robotic hardware. We evaluate SERFN on two challenging dexterous manipulation tasks in the real world: cutting tape with scissors retrieved from a case, and in-hand cube rotation with a palm-down grasp – both of which require precise, dexterous control over long horizons. On these tasks, SERFN achieves stable, sample-efficient adaptation where standard methods struggle.
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All authors Chenyu Yang, Denis Tarasov, Davide Liconti, Romain Guntz, Hehui Zheng, Robert K. Katzschmann
Robotics
Optimal Control of Microswimmers for Trajectory Tracking Using Bayesian Optimization
Lucas Palazzolo, Mickaël Binois and Laëtitia Giraldi
Trajectory tracking for microswimmers remains a key challenge in microrobotics, where low-Reynolds-number dynamics make control design particularly complex. In this work, we formulate the trajectory tracking problem as an optimal control problem and solve it using a combination of B-spline parametrization with
Bayesian optimization, allowing the treatment of high computational costs without requiring complex gradient computations. Applied to a flagellated
magnetic swimmer, the proposed method reproduces a variety of target trajectories, including biologically inspired paths observed in experimental studies. We further evaluate the approach on a three-sphere swimmer model, demonstrating that it can adapt to and partially compensate for wall-induced hydrodynamic effects. The proposed optimization strategy can be applied consistently across models of different fidelity, from low-dimensional ODE-based models to high-fidelity PDE-based simulations, showing its robustness and generality. These results highlight the potential of
Bayesian optimization as a versatile tool for optimal control strategies in microscale locomotion under complex fluid-structure interactions.
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Robotics
LLM-Grounded Dynamic Task Planning with Hierarchical Temporal Logic for Human-Aware Multi-Robot Handover
Shuyuan Hu, Tao Lin, Kai Ye et al. (+1 more)
Large
Language Models (LLMs) enable non-experts to specify open-world multi-
robot tasks, but the generated plans are often kinematically infeasible and inefficient in long-horizon settings. Formal methods such as Linear Temporal Logic (LTL) offer correctness and optimality guarantees, yet they are typically offline and scale poorly. To bridge this gap, we propose a neuro-symbolic framework that grounds human instructions into hierarchical LTLf specifications (i.e., LTL on finite traces) and solves the resulting Simultaneous Task Allocation and Planning (STAP) problem. Unlike static approaches, our system handles
stochastic environmental changes-such as user motion or updated instructions-through a receding horizon planning (RHP) loop with real-time perception, dynamically refining plans over a hierarchical state space. Experiments in simulation and on real
robots demonstrate that our approach significantly outperforms baseline methods in success rate and interaction fluency while reducing replanning overhead.
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All authors Shuyuan Hu, Tao Lin, Kai Ye, Tianwei Zhang
Robotics
AtomBridge: Agentic VLA Inference Plugin for Long-Horizon Tasks in Scientific Experiments
Yiwen Pang, Bo Zhou, Changjin Li et al. (+7 more)
Robotic laboratories play a critical role in autonomous scientific discovery by enabling scalable, continuous experimental execution. Recent
vision-
language-action (VLA) models offer a promising foundation for robotic laboratories. However, scientific experiments typically involve long-horizon tasks composed of multiple atomic tasks. Existing VLA models may fail to perform composed tasks formed by reordering and composing these known atomic actions. This limitation can arise from a skill-chaining gap caused by
robot-state mismatch: the terminal
robot state of one skill can fall outside the valid initial-state distribution of the next. To address this challenge, we propose AtomBridge, an Agentic VLA Inference Plugin for Long-Horizon Tasks in Scientific Experiments. AtomBridge attaches at inference time to a VLA policy already fine-tuned on atomic tasks, while keeping its weights fixed. At each task boundary, it uses LLM-based transition
reasoning and robotic-action code generation to insert transitional actions between consecutive tasks. This plug-and-play design mitigates the skill-chaining gap caused by
robot-state mismatch without additional VLA fine-tuning or demonstrations of composed long-horizon sequences. Across scientific manipulation sequences in simulation and a real-world experimental environment, AtomBridge improves execution continuity and per-step atomic-task success. On 8-step composed tasks, AtomBridge improves full-sequence success by 10%~25%.
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All authors Yiwen Pang, Bo Zhou, Changjin Li, Xuanhao Wang, Shengxiang Xu, Deng-Bao Wang, Peng Cheng, Shimin Di, Jingkuan Song, Min-Ling Zhang
Robotics · Cross-listed from eess.SY
First-order friction models with bristle dynamics: lumped and distributed formulations
Luigi Romano, Ole Morten Aamo, Jan Åslund et al. (+1 more)
Dynamic models, particularly rate-dependent models, have proven effective in capturing the key phenomenological features of frictional processes, whilst also possessing important mathematical properties that facilitate the design of control and estimation
algorithms. However, many rate-dependent formulations are built on empirical considerations, whereas physical derivations may offer greater interpretability. In this context, starting from fundamental physical principles, this paper introduces a novel class of first-order dynamic friction models that approximate the dynamics of a bristle element by inverting the friction characteristic. Amongst the developed models, a specific formulation closely resembling the LuGre model is derived using a simple rheological equation for the bristle element. This model is rigorously analyzed in terms of stability and passivity – important properties that support the synthesis of observers and controllers. Furthermore, a distributed version, formulated as a
hyperbolic partial differential equation (PDE), is presented, which enables the modeling of frictional processes commonly encountered in rolling contact phenomena. The tribological behavior of the proposed description is evaluated through classical experiments and validated against the response predicted by the LuGre model, revealing both notable similarities and key differences.
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All authors Luigi Romano, Ole Morten Aamo, Jan Åslund, Erik Frisk
Robotics · Cross-listed from eess.SY
Lateral tracking control of all-wheel steering vehicles with intelligent tires
Luigi Romano, Ole Morten Aamo, Jan Åslund et al. (+1 more)
The accurate characterization of tire dynamics is critical for advancing control strategies in autonomous road vehicles, as tire behavior significantly influences handling and stability through the generation of forces and moments at the tire-road interface. Smart tire technologies have emerged as a promising tool for sensing key variables such as road friction, tire pressure, and wear states, and for estimating kinematic and dynamic states like vehicle speed and tire forces. However, most existing estimation and control
algorithms rely on empirical correlations or machine learning approaches, which require extensive calibration and can be sensitive to variations in operating conditions. In contrast, model-based techniques, which leverage infinite-dimensional representations of tire dynamics using partial differential equations (PDEs), offer a more robust approach. This paper proposes a novel model-based, output-feedback lateral tracking control strategy for all-wheel steering vehicles that integrates distributed tire dynamics with smart tire technologies. The primary contributions include the suppression of micro-shimmy phenomena at low speeds and path-following via force control, achieved through the estimation of tire slip angles, vehicle kinematics, and lateral tire forces. The proposed controller and observer are based on formulations using ODE-PDE systems, representing rigid body dynamics and distributed tire behavior. This work marks the first rigorous control strategy for vehicular systems equipped with distributed tire representations in conjunction with smart tire technologies.
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All authors Luigi Romano, Ole Morten Aamo, Jan Åslund, Erik Frisk
Robotics · Cross-listed from eess.SY
Finite-time Stable Pose Estimation on TSE(3) using Point Cloud and Velocity Sensors
Nazanin S. Hashkavaei, Abhijit Dongare, Neon Srinivasu et al. (+1 more)
This work presents a finite-time stable pose
estimator (FTS-PE) for rigid bodies undergoing rotational and translational motion in three dimensions, using measurements from onboard sensors that provide position vectors to inertially-fixed points and body velocities. The FTS-PE is a full-state observer for the pose (position and orientation) and velocities and is obtained through a Lyapunov analysis that shows its stability in finite time and its robustness to bounded measurement noise. Further, this observer is designed directly on the state space, the tangent bundle of the Lie group of rigid body motions, SE(3), without using local coordinates or (dual) quaternion representations. Therefore, it can estimate arbitrary rigid body motions without encountering singularities or the unwinding phenomenon and be readily applied to autonomous vehicles. A version of this observer that does not need translational velocity measurements and uses only point clouds and angular velocity measurements from rate gyros, is also obtained. It is discretized using the framework of
geometric mechanics for numerical and experimental implementations. The numerical simulations compare the FTS-PE with a dual-quaternion extended Kalman filter and our previously developed variational pose
estimator (VPE). The experimental results are obtained using point cloud images and rate gyro measurements obtained from a Zed 2i stereo depth camera sensor. These results validate the stability and robustness of the FTS-PE.
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All authors Nazanin S. Hashkavaei, Abhijit Dongare, Neon Srinivasu, Amit K. Sanyal
Robotics
Phase-Aware Policy Learning for Skateboard Riding of Quadruped Robots via Feature-wise Linear Modulation
Minsung Yoon, Jeil Jeong and Sung-Eui Yoon
Skateboards offer a compact and efficient means of transportation as a type of personal mobility device. However, controlling them with legged
robots poses several challenges for policy learning due to perception-driven interactions and multi-modal control objectives across distinct skateboarding phases. To address these challenges, we introduce Phase-Aware Policy Learning (PAPL), a reinforcement-learning framework tailored for skateboarding with quadruped
robots. PAPL leverages the cyclic nature of skateboarding by integrating phase-conditioned Feature-wise Linear Modulation layers into actor and critic networks, enabling a unified policy that captures phase-dependent behaviors while sharing
robot-specific knowledge across phases. Our evaluations in simulation validate command-tracking accuracy and conduct ablation studies quantifying each component's contribution. We also compare locomotion efficiency against leg and wheel-leg baselines and show real-world transferability.
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Robotics
Certified Gradient-Based Contact-Rich Manipulation via Smoothing-Error Reachable Tubes
Wei-Chen Li and Glen Chou
Gradient-based methods can efficiently optimize controllers by leveraging differentiable simulation and physical priors. However, contact-rich manipulation remains challenging because hybrid contact dynamics often produce discontinuous or vanishing gradients. Although smoothing the dynamics can restore informative gradients, the resulting model mismatch can cause controller failures when deployed on real systems. We address this trade-off by planning with smoothed dynamics while explicitly quantifying and compensating for the induced error, providing formal guarantees on safety and task completion under the original nonsmooth dynamics. Our approach applies smoothing to both contact dynamics and contact geometry within a differentiable simulator based on convex optimization, allowing us to characterize the deviation from the nonsmooth dynamics as a set-valued discrepancy. We incorporate this discrepancy into the optimization of time-varying affine feedback policies through analytical reachable sets, enabling robust constraint satisfaction for the closed-loop hybrid system while relying solely on the informative gradients of the smoothed model. By bridging differentiable simulation with set-valued robust control, our method produces affine feedback policies that respect the unilateral nature of contact. We evaluate our method on several contact-rich tasks, including planar pushing, object rotation, and in-hand dexterous manipulation, achieving certified constraint satisfaction with lower safety violations and smaller goal errors than baseline approaches.
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Robotics
CAPER: Constrained and Procedural Reasoning for Robotic Scientific Experiments
Jinghan Yang, Jingyi Hou, Xinbo Yu et al. (+2 more)
Robotic assistance in scientific laboratories requires procedurally correct long-horizon manipulation, reliable execution under limited supervision, and robustness in low-demonstration regimes. Such conditions greatly challenge end-to-end
vision-
language-action (VLA) models, whose assumptions of recoverable errors and data-driven policy learning often break down in protocol-sensitive experiments. We propose CAPER, a framework for Constrained And ProcEdural
Reasoning for robotic scientific experiments, which explicitly restricts where learning and
reasoning occur in the planning and control pipeline. Rather than strengthening end-to-end policies, CAPER enforces a responsibility-separated structure: task-level
reasoning generates procedurally valid action sequences under explicit constraints, mid-level
multimodal grounding realizes subtasks without delegating spatial decision-making to large
language models, and low-level control adapts to physical uncertainty via reinforcement learning with minimal demonstrations. By encoding procedural commitments through interpretable intermediate representations, CAPER prevents execution-time violations of experimental logic, improving controllability, robustness, and data efficiency. Experiments on a scientific workflow
benchmark and a public long-horizon manipulation
dataset demonstrate consistent improvements in success rate and procedural correctness, particularly in low-data and long-horizon settings.
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All authors Jinghan Yang, Jingyi Hou, Xinbo Yu, Wei He, Yifan Wu
Robotics
Disambiguating Anthropomorphism and Anthropomimesis in Human-Robot Interaction
Minja Axelsson and Henry Shevlin
In this preliminary work, we offer an initial disambiguation of the theoretical concepts anthropomorphism and anthropomimesis in Human-
Robot Interaction (HRI) and social robotics. We define anthropomorphism as users perceiving human-like qualities in
robots, and anthropomimesis as
robot developers designing human-like features into
robots. This contribution aims to provide a clarification and exploration of these concepts for future HRI scholarship, particularly regarding the party responsible for human-like qualities -
robot perceiver for anthropomorphism, and
robot designer for anthropomimesis. We provide this contribution so that researchers can build on these disambiguated theoretical concepts for future
robot design and evaluation.
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