Peer Revenue Past citations are no guarantee of future returns

Papers

Fresh from arXiv, typeset properly. Highlighted words trade on the market.

Computer Science Robotics

cs.RO Watch Robotics

Wednesday 10 June

Robotics

Deformable In-Hand Slip-Aware Tactile Sensor with Integrated Velocity Sensing, Force/Torque and Pressure Map Estimation

Gabriel Arslan Waltersson and Yiannis Karayiannidis

This paper introduces a novel tactile sensor for in-hand manipulation with slip-aware control that integrates velocity and force/torque sensing with pressure map estimation into a single device with a deformable contact pad. To the best of our knowledge, this is the first sensor to combine these sensing modalities within a single compliant structure. The sensor features a deformable contact surface and can robustly track both flat and curved surfaces across a wide range of diffuse surface materials. Its performance is evaluated through a comprehensive set of experiments that highlight both its capabilities and limitations. The sensor is designed for rapid and low-cost fabrication using a combination of standard PCB manufacturing and rapid prototyping techniques.
Read the full abstract

Robotics

DuoBench: A Reproducible Benchmark for Bimanual Manipulation in Simulation and the Real World

Tobias Jülg, Seongjin Bien, Simon Hilber et al. (+7 more)

Bimanual robot systems substantially expand manipulation capabilities, but coordinating two arms introduces additional control complexity and failure modes that are not well captured by existing benchmarks. We introduce DuoBench, an extensible benchmarking framework for bimanual manipulation policies on the FR3 Duo platform. DuoBench comprises eleven tasks spanning four coordination categories, implemented in simulation and partially reproduced in the real world through reproducible task recipes with 3D-printable assets. In addition, we propose a stage-based evaluation scheme that supports fine-grained semantic failure analysis beyond binary success and provide human-teleoperated datasets for all benchmark tasks. We benchmark several dual-arm imitation-learning and vision-language-action policies in simulation and on real hardware. Our results show that current policies remain challenged by bimanual manipulation, particularly in early interaction stages, parallel arm execution, and transfer between simulation and real-world settings. DuoBench provides a reproducible testbed for diagnosing these failure modes and studying future methods for dual-arm policy learning. Code, datasets, and videos are available at https://duobench.github.io/
Read the full abstract

All authors Tobias Jülg, Seongjin Bien, Simon Hilber, Yannik Blei, Pierre Krack, Maximilian Li, Sven Parusel, Rudolf Lioutikov, Florian Walter, Wolfram Burgard

Robotics

Critic Architecture Matters: Dual vs. Unified Critics for Humanoid Loco-Manipulation

Mehmet Turan Yard{\i}mc{\i}

Multi-objective reinforcement learning for humanoid robots must coordinate locomotion and manipulation within one policy. A natural design choice is between a single (unified) critic that estimates the combined value of all objectives and separate (dual) critics with disjoint reward signals. We compare the two on the Unitree G1 humanoid in NVIDIA Isaac Lab. In the standing mode of a standardized evaluation, the dual-critic run reaches targets 3.5x faster (6.5 vs. 22.6 simulation steps), achieves 2x the throughput (14.3 vs. 7.0 validated reaches per 1,000 steps) and a higher validated reach rate (65.2% vs. 53.8%) than the unified-critic run. That evaluation pins the fingers open for every policy, whereas the unified run had trained driving its own. When the unified run drives its own fingers, with nothing else changed, the standing-mode gap falls from 3.5x to 1.3x in speed and from 2x to 1.1x in throughput. This is a single re-evaluation of a single checkpoint, and we do not generalize from it. Adding five anti-gaming reward mechanisms to the dual critic did not raise validated reach rate (60.9% vs. 65.2%). The two runs differ not only in the critic but also in the PPO update rule (one summed advantage under one likelihood ratio, versus a per-stream advantage and a ratio per actor), and further in curriculum, arm action dimensionality, finger control and reward weights; each is a single run. The measurement therefore cannot separate the critic from the update rule. We argue that critic architecture deserves explicit treatment as a design variable in multi-objective humanoid RL, and specify the single-variable ablation needed to establish its causal contribution. Code, checkpoints and a project page: https://mturan33.github.io/critic-architecture-matters/
Read the full abstract

2606.11891 · 10 Jun · Accepted at the ICRA 2026 Workshop on Reinforcement Learning in the Era of Imitation Learning (RL4IL), Vienna. 7 pages, 2 figures. v3 fixes the workshop name and the unified run's description; with its own fingers driven, the standing-mode gap falls from 3.5x/2x to 1.3x/1.1x (speed/throughput; one re-evaluation). No retraining. https://mturan33.github.io/critic-architecture-matters/

Abstract PDF $HMND ×4$BOT ×1

Robotics · Cross-listed from cs.CV

Task-Aligned Stability Analysis of Vision-Language Models for Autonomous Driving Hazard Detection

Everett Richards

Vision-language models (VLMs) are increasingly used for scene understanding in autonomous driving, but robustness analysis often relies on task-agnostic embedding stability alone. We study whether corruption-induced embedding drift predicts changes in a task-aligned hazard score derived from CLIP image-text similarities. Using controlled corruptions on BDD100K road scenes, we compare embedding drift against margin drift, defined as the change in hazard score under perturbation. The relationship is highly corruption-dependent: some families exhibit strong coupling between representation drift and decision drift, while others induce hazardous decision instability despite relatively modest embedding change. Furthermore, corruption families differ in failure direction: most suppress hazard detections via false negatives, while occlusion instead triggers false alarms, suggesting that benchmark design should account for asymmetric failure modes, not just overall instability rates. These results suggest that robustness benchmarks should include task-aligned stability measures in addition to embedding-level perturbation statistics.
Read the full abstract

Robotics

Modular Anthropomorphic Hand Design via Multi-Parameter Finger Benchmarking and Selection

Yu Zhang, Huijiang Wang and Josie Hughes

Designing anthropomorphic dexterous robotic hands remains challenging as the design space straddles morphology, actuation, and sensing properties, and performance metrics span both task-dependent and task-agnostic. Existing optimization methods are often unstructured or consider only a single performance metric, limiting systematic comparison and targeted refinement. While the design considerations of the entire hand are significant, the individual finger properties play a key role in dexterity. By developing a robotic hand platform where fingers can be modularly integrated into a full teleoperated hand, we propose that optimizing the fingers can significantly improve overall hand performance. This approach enables rapid screening of different finger-level prototypes through a number of quantitative benchmarks before their integration into the hand for task-level validation. Candidate finger designs (incorporating variations in joint, bone, skin, and sensor placement) are assessed using both mechanism-oriented and task-relevant metrics, which establish a quantitative link between component design and full hand embodiment. The framework is validated through the development of an anthropomorphic robotic hand with optimized fingers, demonstrating how these fingers enable performance improvements across tasks, including multi-object grasping and light bulb screwing.
Read the full abstract

Robotics

Human-Guided Co-Manipulation of Carbon Fiber Plies

Rami Ojanen, James Fant-Male and Roel Pieters

The handling of flexible materials is a difficult task to fully automate due to the challenges caused by the deformability of these types of objects. Meanwhile, a fully manual process can be ergonomically challenging, tedious and inefficient. Thus, human-robot collaboration (HRC) and cooperative manipulation (co-manipulation) have received increasing interest in this field as they enable human involvement when needed while also improving productivity. To enable efficient co-manipulation and interaction between the human operator and the robot, different modalities and control methods are required. In this paper, we present and examine different control methods for co-manipulation of carbon fiber plies, evaluating the pros and cons of each method in a controlled setting. We propose that a multimodal combination of speech commands, wrist-tracking through vision, and force with compliant control would provide the best solution for complete and intuitive control of the task.
Read the full abstract

Robotics

Blind Dexterous Grasping via Real2Sim2Real Tactile Policy Learning

Shengcheng Luo, Xiyan Huang, Zhe Xu et al. (+3 more)

Blind grasping with a dexterous hand is a crucial manipulation capability. Nevertheless, learning such tactile-only policies for real robots remains challenging due to the tactile sim-to-real gap and the limited expressiveness of sparse tactile signals. To bridge this gap, we propose a framework for tactile-only blind grasping that is deployable on a physical multi-fingered robotic hand. Our approach combines three key components. First, we introduce a Real2Sim tactile calibration pipeline that constructs a contact-calibrated digital-twin simulator capable of reproducing real tactile signals. Second, we improve the expressiveness of sparse tactile observations using a layout-aware tactile encoder, which incorporates sensor-geometry priors through self-supervised pretraining. Third, to improve generalization to unseen objects, we train object-specific reinforcement-learning experts in the calibrated simulator and aggregate their successful grasp trajectories into a tactile-conditioned Diffusion Policy. We evaluate our method on a physical LEAP Hand equipped with distributed tactile sensing across 10 seen and 10 unseen objects. The deployed policy achieves a 27% real-world grasp success rate across all 20 objects, without real-world grasping demonstrations or visual input. Simulation ablations show that layout-aware tactile pretraining improves grasping performance, while sensing-level evaluations confirm that Real2Sim calibration increases the consistency of tactile contact events between simulation and hardware. Together, these results suggest that contact-event calibration, geometry-aware tactile representation learning, and diffusion-based policy aggregation provide an effective path toward tactile-only blind grasping on real dexterous robotic hands. Project page:Dex-Blind-Grasp.github.io.
Read the full abstract

All authors Shengcheng Luo, Xiyan Huang, Zhe Xu, Wanlin Li, Ziyuan Jiao, Chenxi Xiao

Robotics

TacCoRL: Integrating Tactile Feedback into VLA via Simulation

Siyu Ma, Yuqi Liang, Chang Yu et al. (+5 more)

Vision-language-action (VLA) models provide strong visual, language, and action priors for robot manipulation, but visual observations alone often miss the local contact state required for contact-rich tasks. We present TacCoRL, a scalable framework that injects Tactile feedback into VLA policies and improves them through sim-real Co-training and simulation-based reinforcement learning (RL), without requiring large-scale tactile pretraining or extensive real-world contact exploration. The key idea is not only adding touch as an input, but learning how contact readings should modulate action responses in near-failure states that are rare in demonstrations and risky to collect on hardware. We use a real-aligned simulator as a closed-loop training environment for contact interaction. Mixed simulated and real trajectories first warm-start tactile-conditioned actions in the pretrained policy. Reinforcement learning with verifiable task rewards then optimizes the policy using simulated contact rollouts. It reinforces tactile-conditioned actions that lead to task completion, while a supervised objective on real trajectories keeps the refined policy anchored to deployment visual, tactile, and action distributions. The resulting policy transfers directly to the real robot without privileged simulation state or online real-world RL. Across four bimanual contact-rich tasks, the final visuo-tactile policy achieves an average success rate of 72.5%, compared to baseline of 50.0%. Result videos and more details are available at https://tac-corl.github.io/
Read the full abstract

All authors Siyu Ma, Yuqi Liang, Chang Yu, Yunuo Chen, Hao Su, Yixin Zhu, Yin Yang, Chenfanfu Jiang

Robotics

Explore From Sketch: Accelerating UAV Exploration in Large-scale Environments with Prior Maps

Tiancheng Lai, Yuman Gao, Xiangyu Li et al. (+8 more)

Autonomous exploration with UAVs in large-scale, topologically complex environments often suffers from low efficiency due to suboptimal scheduling and detours. Prior maps (e.g., construction drawings), although usually imprecise and flawed, are readily available in many scenarios and have the potential to provide global structural guidance. This paper presents a novel exploration framework that leverages sparse, unaligned, and even discrepant 2D prior maps for LiDAR-based UAV exploration. First, a robust 2D-3D point cloud registration pipeline is proposed to align LiDAR observations with prior maps. The registration pipeline combines a GeoContext descriptor for single-frame candidate retrieval, a multi-frame verification mechanism for coarse transformation estimation with outlier rejection, and a Scale-ICP algorithm for refinement. The registration module can handle map discrepancies and provide multiple hypotheses when geometric ambiguities arise. To effectively utilize the registration results for exploration planning, we further develop a hierarchical viewpoint planning strategy under localization uncertainties. The hierarchical strategy first spatially attaches local viewpoints to prior guidepoints and adopts a Monte Carlo Tree Search solver to determine their traversal sequence under each registration hypothesis. To mitigate registration uncertainty, a risk-aware selector evaluates prior sequences using confidence-weighted travel risk, and a fixed-endpoint traveling salesman problem is formulated to generate an efficient local coverage path under the selected prior guidance. Benchmark evaluations reveal up to 34.2% improvement in exploration efficiency and 37.9% reduction in flight distance compared to state-of-the-art methods, while extensive simulations and field experiments further demonstrate robustness to prior map incompleteness and deformations.
Read the full abstract

All authors Tiancheng Lai, Yuman Gao, Xiangyu Li, Ruitian Pang, Xingpeng Wang, Siqi Shen, Mengke Zhang, Yin He, Fei Gao, Chao Xu, Yanjun Cao

Robotics

Improving Human Diving Endurance with a Field-Deployable Exoskeleton

Zhihao Zhou, Zhenmeng Ju, Chenxi Zhang et al. (+4 more)

Human endurance in underwater locomotion is fundamentally restricted by high energetic demands to overcome drag while managing a finite supply of self-contained breathing gas. Here, we present DiveMate, a field-deployable exoskeleton designed to improve human diving endurance via kick assistance in real-world underwater environments. DiveMate increases the travel distance using a given amount of breathing gas by 42.9% and extends dive duration by 54.9% through reducing the gas consumption rate. Marked reductions in muscle activation indicate decreased physiological exertion, with the net gas consumption rate decreasing by 47.0%. Changes in kinematic characteristics further contribute to powerful and efficient kicking. Successful open-water trials at depths up to 30 m demonstrated field feasibility. These results suggest that applying exoskeleton assistance is beneficial for improving human diving endurance and augmenting their ability to explore the aquatic world. This study extends the application frontier of exoskeletons and provides a potential reference for the design and assessment of future underwater assistive devices.
Read the full abstract

All authors Zhihao Zhou, Zhenmeng Ju, Chenxi Zhang, Ming Xu, Enhao Zheng, Lecheng Ruan, Qining Wang

Robotics · Cross-listed from cs.CV

DroneShield-AI: A Multi-Modal Sensor Fusion Framework for Real-Time Autonomous Drone Threat Detection, Behavioral Intent Classification, and Swarm Intelligence in Contested Airspace

Marius Bayizere

Unmanned Aerial Vehicle (UAV) threats have emerged as a defining security challenge of the 21st century. This paper presents DroneShield-AI, a unified open framework integrating six processing layers: RF signal classification, acoustic motor-signature detection, YOLOv8-based visual detection, evidence-weighted sensor fusion, a Behavioral Intent Classification Engine (BICE), and a Graph Neural Network Swarm Intelligence Module (GNN-SIM). This v2 revision reports measured results on the completed implementation (495 automated tests), superseding the v1 simulation-only preprint. On real public data: RF presence detection reaches F1 0.9924; acoustic detection reaches 98.12% accuracy, though a 4-feature statistical baseline reaches 93.25% on the same split, so the model's drone-specific contribution is approximately 4.9 points, a property of dataset provenance rather than a leakage bug; visual detection (YOLOv8) reaches AP50 0.8782 on a held-out test split, from a model still improving when training stopped. No fused-accuracy figure is reportable, since the three real-data layers use independently collected, unsynchronized datasets with no shared ground truth. BICE and GNN-SIM remain evaluated on physics-calibrated simulation data pending real adversarial and swarm field data: BICE shows a consistent robustness advantage over a trivial baseline under distribution shift; GNN-SIM underperforms its own trivial baseline in-distribution but independently replicates BICE's noise-robustness advantage, regarded as this work's most durable finding. All code, trained checkpoints, and the full test suite are publicly released.
Read the full abstract

2606.11687 · 10 Jun · 18 pages, 6 figures, 20 tables. v2 adds Section 7.9 reporting measured results on the completed implementation (495 automated tests) and revision notes throughout Sections 3-9 and Appendix C; superscript daggers mark results still based on simulation data. Code available at https://github.com/mariusbayizere/DroneShield-AI

Abstract PDF $DATA ×2$ADVR ×1$UAV ×1

Robotics

SAFER-Nav: Enhancing Safety for Visual Robot Navigation via Segmentation-Aware Fine-Tuning

Geonyeong Ko, Giung Lee and Changjoo Nam

Vision-based navigation models, particularly foundation models, generate viable trajectories from RGB observations alone. However, even state-of-the-art transformer- and diffusion-based policies struggle to generalize in unfamiliar deployment environments containing unseen obstacles or shifted conditions. The resulting trajectories often remain goal-directed but unsafe. Existing efforts improve safety through external trajectory correction or internal geometric priors, yet the resulting policies are not trained to explicitly represent obstacle boundaries or traversable free-space structure. To address this, we propose a navigation model that incorporates these structures directly into the policy via fine-tuning and is designed for transformer-based RGB navigation policies. Across three robot platforms, two indoor environments, and static and dynamic obstacle scenarios, our method reduces collisions per run from 1.76 to 0.20 and raises the goal arrival rate from 42% to 93% relative to ViNT, with consistent gains over NoMaD and their CARE-augmented variants. Project page: https://paper-demo.github.io/SAFER_Nav/
Read the full abstract

Robotics

LUCID: Learning Embodiment-Agnostic Intent Models from Unstructured Human Videos for Scalable Dexterous Robot Skill Acquisition

Harsh Gupta, Guanya Shi and Wenzhen Yuan

The most widely-adopted robot learning pipelines today learn skills from robot demonstrations or structured human data, which are expensive to collect and tied to specific embodiments. In contrast, unstructured human videos provide a scalable alternative. They contain diverse manipulation demonstrations across objects, scenes, and strategies, but are not directly connected to robot action. We propose LUCID, a two-stage framework that learns task intent from unstructured human videos drawn from internet-scale datasets and learns robot control in massively-parallel simulation. The intent model predicts short-horizon intent (what should happen next in the scene) from the current observation in closed loop. An embodiment-specific sensorimotor policy converts this intent into robot actions. The intent interface is shared across controllers, so the same intent model can be applied to different embodiments, from our primary dexterous hand to a parallel-jaw gripper. We evaluate LUCID on five real-world manipulation tasks: stirring, wiping, and binning supervised by only internet video, with zero-shot transfer to novel scenes and object instances; and push-T and cable routing supervised by 1 hr each of self-collected smartphone video. Project page: https://lucid-robot.github.io/.
Read the full abstract

Robotics

Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing

Hao Wang, Fu-Zhao Ou, Shiqi Wang et al. (+2 more)

The rapid development of intelligent control methodologies has endowed robots with powerful autonomous intelligence. Cable routing, a ubiquitous foundational task in industry, provides a rigorous benchmark for robotic dexterity and sequential decision-making. In these practical scenarios, image observation distortion frequently occurs. Samples characterized by low-quality image observations often hinder accurate model training, posing challenges to the reliability and accuracy of intelligent control systems. Nevertheless, no dedicated intelligent control solution has been proposed for scenarios of image signal distortion. Meanwhile, image quality information has not been sufficiently exploited to further enhance the performance of intelligent control methodologies. To this end, we propose a novel robotic imitation learning framework that comprises an image quality assessment module, a confidence-based learning mechanism, and a decision-making module, which is designed to maintain high performance even under distorted image observations. In the proposed framework, the image quality assessment module synergizes with the confidence-based learning mechanism to enhance the efficacy of the decision-making module. Specifically, the image quality assessment module is incorporated to extract image quality information from image observations, while the confidence-based learning mechanism adaptively prioritizes challenging samples to improve learning effectiveness. The decision-making module determines appropriate discrete skills or continuous actions. Experimental results demonstrate that our formulated framework enhances the overall performance of the decision-making module.
Read the full abstract

All authors Hao Wang, Fu-Zhao Ou, Shiqi Wang, Zhaolin Wan, Xiaopeng Fan

Robotics

ConsistencyPlanner: Real-time Planning with Fast-Sampling Consistency Models

Qichao Zhang, Xing Fang, Jiaqi Fang et al. (+4 more)

Closed-loop planning in complex, real-world driving scenarios presents a critical challenge for autonomous driving systems. While traditional rule-based methods are interpretable, their predefined heuristics lack the adaptability for dynamic traffic environments. Learning-based approaches have shown considerable promise. Conversely, learning-based approaches, despite their promise, struggle to balance the modeling diverse and multimodal driving behaviors and real-time planning, often leading to indecisive or unsafe actions. To address this limitation, we propose Consistency Planner, a real-time planning framework with fast-sampling consistency models. Our approach is built upon two key technical contributions. Efficient Multimodal Sampling: We employ fast-sampling consistency models to generate a diverse set of plausible future trajectories. This enables efficient, real-time exploration of multimodal actions, overcoming the computational bottlenecks of previous iterative generative methods. Heterogeneous Feature Fusion: We introduce an attention-enhanced decoder that dynamically integrates heterogeneous input features (including scene feature and action token) into a cohesive representation for robust planning. Extensive evaluation in the Waymax simulator demonstrates superior performance in safety metrics compared to existing methods, with particularly strong results in challenging dynamic scenarios.
Read the full abstract

All authors Qichao Zhang, Xing Fang, Jiaqi Fang, Zhenwen Cai, Jie Ling, Qiankun Yu, Dongbin Zhao

Robotics · Cross-listed from cs.CV

Cross-Modal Benchmarking for Robotic Perception in Natural Environments

David Hall, Joshua Knights, Mark Cox et al. (+1 more)

Natural environments present a complex challenge to robotics perception systems. Current models, particularly vision foundation models, are largely trained on structured, urban environments leading to weaknesses in their perception for field robotics tasks. We showcase the limitations of current models using our recently released WildCross benchmark, a new cross-modal benchmark for place recognition and metric depth estimation in large-scale natural environments. WildCross comprises over 476K sequential RGB frames with semi-dense depth and surface normal annotations, each aligned with accurate 6DoF pose and synchronized dense lidar submaps. In this work, we provide an expanded analysis of the benchmark results from the recent WildCross benchmark, with particular emphasis on expanded metric depth estimation experiments. Access to the code repository and dataset for this work can be found at https://csiro-robotics.github.io/WildCross.
Read the full abstract

All authors David Hall, Joshua Knights, Mark Cox, Peyman Moghadam

Robotics

Adversarial Vulnerabilities of Learned Telesurgery Policies

Shutong Jin, Ziyang Chen, Preethi Satish et al. (+3 more)

While not yet in clinical deployment, learning-based policies are increasingly considered to augment the dexterity of human surgeons in robot-assisted surgery. Can the end-to-end mapping from visual observations to robot actions be vulnerable to adversarial attacks? We present the first study of adversarial vulnerabilities in learning-based policies for surgical robotics, conducted in a laboratory white-box setting where the attacker is assumed to have access to policy information and injects perturbations into the video stream transmitted over the network. Two attack modes are considered: (a) disruptive attacks, where subtle visual perturbations interrupt policy execution without being noticed by a surgeon, and (b) steering attacks, where perturbations steer policy actions toward attacker-specified directions. We study three adversarial attack methods, each with increasing access to policy information, and evaluate their impact on two surgical subtasks: debridement and suturing, performed on phantoms. Our evaluation covers three end-to-end policy architectures: ACT, Diffusion Policy, and pi0. In addition, we identify a vulnerability to photometric perturbations, which mimic natural visual changes such as lighting variation. Results from 620 physical experiments suggest that state-of-the-art policies can be significantly disrupted, resulting in an average 61% reduction in surgical subtask success rates. These findings suggest that adversarial vulnerabilities are important to consider for learned telesurgery policies. Project page: https://surgical-robotics.github.io/adversarial-vulnerability/
Read the full abstract

All authors Shutong Jin, Ziyang Chen, Preethi Satish, Paavan Gupta, Florian T. Pokorny, Ken Goldberg

Robotics

Learning Object Manipulation from Scratch via Contrastive Interaction

Tongle Shen, Caleb Chuck, Fan Feng et al. (+1 more)

Contrastive Reinforcement Learning (CRL) has seen recent success in a wide variety of goal-conditioned robotics tasks by learning structured representations of the dynamics. However, despite its success in locomotion and simpler control domains, CRL often struggles in interaction-rich manipulation. We argue that a key source of this difficulty is object-centric interaction, such as contact or grasping, that induces distinct changes in the underlying dynamic modes. In this work, we formulate manipulation dynamics as a piecewise-smooth Markov process and show that interaction-induced mode changes create piecewise nonlinear reachability structures that are difficult for standard CRL energy functions to represent and plan over. Based on this analysis, we introduce Interaction-weighted Resampling (IWR). IWR performs interaction-aware resampling around phases before, during, and after interactions, encouraging the learned representation to preserve the mode boundaries that determine future reachability to capture multi-modal and piecewise nonlinear reachability. Across interaction-centric environments, including 2D dynamic control, robotic manipulation, and robot air hockey, IWR improves both sample efficiency and overall performance over prior CRL methods, with 19.8% average improvement in simulation. Finally, using a sim-to-real pipeline with policies trained by IWR, we demonstrate the first real-world goal-conditioned robot air hockey agent capable of hitting goals, improving success from 25% to 60%. Project Page: IWR-arxiv.github.io.
Read the full abstract

All authors Tongle Shen, Caleb Chuck, Fan Feng, Biwei Huang

Tuesday 9 June

Robotics · Cross-listed from cs.AI

Emotion in an active inference model of human driving

Julian F. Schumann, Johan Engström, Ran Wei et al. (+3 more)

Active inference has emerged as a principled framework for modeling adaptive behavior by balancing goal-directed action with uncertainty reduction. It has been successfully applied across biological and artificial systems, including recent work on human driving. However, existing active inference models of driving have yet to address an important determinant of behavior in traffic: affective state, which significantly influences decision-making. Prior work in non-traffic domains has explored active inference agents in which emotions are represented along the axes of valence and arousal in the circumplex model. However, this work has been limited to simplified settings with discrete state spaces. In this work, we propose an expanded formulation of valence and arousal that can be extracted from a more complex active inference model of driving with continuous states. In particular, we condition affective estimates not only on the current state but also on predicted future outcomes. We evaluate the proposed approach in two interactive driving scenarios and show that the resulting emotion signals correspond to affective patterns reported in similar scenarios.
Read the full abstract

All authors Julian F. Schumann, Johan Engström, Ran Wei, Jens Kober, Martijn Wisse, Arkady Zgonnikov

Robotics

Learning Control as Enabling Layer for Embodied Intelligence Research explored with Soft Robotic Swimming in diverse Flow Speeds

Fabian Schwab, Federico Allione, Bingcheng Wang et al. (+5 more)

Soft robots are valuable robophysical platforms for studying body-caudal undulatory locomotion, but their compliant bodies are difficult to control precisely under changing hydrodynamic loading. Conventional proportional-integral-derivative (PID) feedback stabilizes periodic undulation in static water, but can accumulate flow-dependent tracking delay and increasing inter-trial variability when environmental flow becomes non-trivial. Here, we evaluate whether augmenting PID control with a Linear Repetitive Learning Estimation Scheme (PID-LRLES) recovers tracking accuracy and repeatability under dynamic flow. The LRLES generalizes classical integral action from constant to periodic, non-constant references, while using a stable transfer-function realization whose poles have negative real parts to avoid the long-term instability issues of classical repetitive control. Closed-loop experiments were carried out in a recirculating flow tank at five bulk flow speeds spanning 0 to 32.6 cm s^-1, using an embedded soft capacitive bending sensor at a 1 kHz control-loop rate. With controller gains tuned once in static water and then held fixed across all conditions, PID-LRLES tracked the periodic bending-envelope reference more closely than the PID baseline and significantly reduced the inter-trial spread of the per-trial RMSE (paired Wilcoxon signed-rank test, p = 1.8 x 10^-4, n = 25). Embedded soft proprioception and cycle-to-cycle learning act as complementary contributors to robustness: the sensor exposes the periodic hydrodynamic bias in body deformation, while the learning term absorbs it over recent oscillation cycles. By reducing flow-dependent control-induced variability, the approach provides an enabling layer for future robophysical studies seeking to isolate the effects of morphology, sensing, and environmental flow on aquatic locomotion.
Read the full abstract

All authors Fabian Schwab, Federico Allione, Bingcheng Wang, Mohamed El Arayshi, Claudio Mucignat, Ivan Lunati, Cristiano Verrelli, Ardian Jusufi

Robotics

Synthetic-to-Real Pipeline for Safe Landing Zone Detection

Shrikant Banerjee and Reza Faieghi

As Uncrewed Aerial Vehicles (UAVs) transition toward higher levels of autonomy, the ability to perform unassisted recovery in non-cooperative, unstructured environments becomes critical. Achieving safe autonomous landing requires high-fidelity semantic resolution to distinguish navigable terrain from hazardous obstacles, yet development is often hindered by the scarcity of annotated aerial datasets. This work proposes a comprehensive perception and data generation pipeline designed to bridge the sim-to-real gap for autonomous landing tasks. We introduce a procedural synthetic data engine that generates photorealistic urban environments with automated semantic annotations through domain randomization. A Transformer-based OneFormer architecture is fine-tuned exclusively on this synthetic data, leveraging multi-head self-attention mechanisms for global context resolution. To ensure operational safety, a deterministic landing module utilizes a Euclidean Distance Transform (EDT) and dynamic inference logic to identify the largest inscribed safe landing zones while maintaining strict clearance buffers around obstacles. Quantitative benchmarking against the UAVid dataset demonstrates robust semantic segmentation performance, while qualitative validation on real-world UAV footage confirms the system's ability to identify collision-free landing sites in unseen environments. Our results highlight the potential of high-fidelity procedural simulation to eliminate the need for manual annotation while providing robust, edge-deployable situational awareness for autonomous UAV recovery.
Read the full abstract

Robotics

Steering Multirobot Behavior via Closed-Loop Affine Activation Editing

Satyajeet Das, Darren Chiu, Shashank Hegde et al. (+1 more)

Real-world robots need to adapt their behavior beyond the envelope of their pre-trained policy. Policy finetuning or retraining are options, but they risk catastrophic forgetting, degrading the pretrained policy's base performance. To combat this, we introduce CLAE: Closed-Loop Affine Activation Editing, an inference-time framework for steering the behavior of a frozen policy by editing intermediate activations while keeping the base policy weights and downstream action head untouched. CLAE approaches behavior steering as a closed-loop problem whose outputs edit policy activations that adapt online to the robot state, environment, target behavior, and multi-robot context. It trains a sparse autoencoder over frozen-policy activations, selects behavior-relevant latent features via post-hoc probing, and learns a lightweight RL-based steering policy that applies state-dependent affine edits to selected latents during inference. We validate CLAE on a frozen multi-quadrotor navigation policy trained to perform a single task: navigating robots to a set of goal locations while avoiding obstacles. Through extensive simulations and physical tests, we show that while navigating to their goal positions, CLAE can 1. steer individual robot behavior by controlling each robot's velocity profile; 2. coordinate multirobot behavior by preserving a desired formation; and 3. produce entirely new behavior wherein robots are required to reduce their exposure to surveillance cameras in the environment. Our project website is available at https://clae-robotics.github.io/.
Read the full abstract

All authors Satyajeet Das, Darren Chiu, Shashank Hegde, Gaurav S. Sukhatme

Robotics

Bridging the sim2real gap in the table tennis robot with a transformer-based ball states predictor

Yin Bi, Christian Conti, Bilan Yang et al. (+3 more)

Robotic table tennis is a representative benchmark for high-speed, closed-loop robotic control in dynamic environments, where accurate and fast prediction of ball states is critical for reliable planning and control. Physics-based approaches rely heavily on accurate parameter identification and precise initial state, while learning-based methods often struggle to capture long-range temporal dependencies and are typically trained on limited or simulated data. We propose a transformer-based framework for table tennis ball state prediction that leverages attention mechanisms to model long-range temporal correlations directly from historical observations, without relying on explicit flight or bounce models. To support robust learning and generalization, we collected a large-scale real-world dataset from players of varying skill levels and diverse ball cannon configurations. The combination of a high-capacity transformer architecture and extensive real-world data enables accurate long-horizon forecasting. Building on this capability, we introduce a plug-and-play sim-to-real transfer strategy, Swap Predictor at Deployment (SPAD), which replaces the physics-based simulator used during training with the proposed real-world-trained predictor at deployment, improving the sim-to-real transferability of the policy without requiring retraining. We demonstrate that this simple substitution effectively narrows the sim-to-real gap while preserving the efficiency and scalability of simulation-based training.
Read the full abstract

All authors Yin Bi, Christian Conti, Bilan Yang, Alexander Sigrist, Peter Dürr, Naoya Takahashi

Robotics

A Modular Dual-Camera Pipeline for Micro-Inspection Using Aerial Robots

S.H. Mirtajadini, N. Rublein, R.M. Ramakrishnan et al. (+3 more)

Most existing drone-based inspection systems require the drone to fly dangerously close to the target or follow complex flight paths to capture small details. In addition, drone flight is affected by disturbances and localization inaccuracies, which can cause the drone to lose sight of its supposed target when it has a narrow view. Furthermore, trajectory planning often requires prior information about the target's geometry, position, and orientation, which is not always available for non-structural targets such as trees, vehicles, or people. To address these challenges, this paper presents aerial_micro_inspection, a generic pipeline for aerial micro-inspection across different use cases. The pipeline assumes a PX4-powered drone equipped with two cameras: (i) a zoomed, gimbal-mounted inspection camera that captures fine details without requiring the drone to fly very close to the target, and (ii) a wide-field-of-view stereo navigation camera that acquires the target surface on site, estimates its range, and partitions it into smaller inspection regions. In addition, a vision-based feedback loop compensates for drone motion while the inspection camera visits small partitions of a larger surface. We evaluate the pipeline in simulation and real-world experiments, mainly in two use-case scenarios: tree inspection for detecting oak processionary caterpillars and their eggs, and greenhouse inspection of sticky traps for detecting whiteflies. The results show improved coverage robustness under drone disturbances in simulation, as well as effective detection of caterpillars and eggs and high-detail imaging of insects in real-world experiments. The pipeline is open-source, developed in ROS 2, and can be adapted to new applications by replacing the surface-segmentation and micro-target detection checkpoints. The code is available at: https://github.com/SaxionMechatronics/aerial_micro_inspection
Read the full abstract

All authors S.H. Mirtajadini, N. Rublein, R.M. Ramakrishnan, G. ter Maat, M. Aldibaja, A.Y. Mersha

Robotics

Dynamic Execution Horizon Prediction for Chunk-based Robot Policies

Yuchi Zhao, Miroslav Bogdanovic, Arjun Sohal et al. (+5 more)

Action chunking has become a standard design in modern robot policies, from diffusion/flow policies to vision-language-action models, where the policy predicts a sequence of actions and executes a fixed number of them instead of acting one step at a time. However, this paradigm relies on a key assumption: a fixed execution horizon. During chunk execution, the policy operates open-loop, which is particularly problematic for fine-grained manipulation tasks that require frequent replanning. In practice, the execution horizon is typically chosen through empirical tuning and is highly task-dependent. To this end, we propose Dynamic Execution Horizon Prediction (DEHP), an effective method that trains a lightweight execution-horizon prediction branch using online reinforcement learning while keeping the pretrained chunk policy completely frozen. This makes the method compatible with black-box chunk policies and isolates the effect of adapting the execution horizon from changes to the underlying action generator. Across our evaluations, DEHP improves the success rate of different high-precision and long-horizon manipulation tasks by a large margin. Our qualitative analysis further shows that DEHP predicts shorter execution horizons during fine-grained stages of the task and longer horizons during free-space motion. In this way, DEHP balances the efficiency of open-loop chunk execution with the reactivity of closed-loop single-step control. Project page: https://dehp-chunking.github.io/
Read the full abstract

All authors Yuchi Zhao, Miroslav Bogdanovic, Arjun Sohal, Liyu Tao, Kourosh Darvish, Alán Aspuru-Guzik, Florian Shkurti, Animesh Garg

Robotics

PLUME: Probabilistic Latent Unified World Modeling and Parameter Estimation for Multi-Finger Manipulation

Abhinav Kumar, Soshi Iba, Rana Soltani Zarrin et al. (+1 more)

Dexterous manipulation with multi-finger hands can be sensitive to physical parameters such as object shape, pose, and friction coefficients. While simulation enables large-scale data collection with known parameter values, simulation-trained policies must still handle uncertainty at deployment, where the true parameters and therefore the true dynamics are unknown. Standard domain randomization strategies may be insufficient for precise tasks like screwdriver turning, as manipulation strategies may need to change depending on specific parameter values. To address this, we propose Probabilistic Latent Unified world Modeling and parameter Estimation (PLUME), a world model that jointly learns to evolve a belief over parameter values as well as the system dynamics conditioned on those parameters. We learn a latent space to jointly represent multiple qualitatively different physical parameters along with rewards, themselves functions of partially-observable variables, to inform planning. Our novel learning framework leads to efficient alignment of the world model to true dynamics through online parameter inference as opposed to re-training or fine-tuning. We evaluate our method on simulated screwdriver turning, valve turning, bucket lifting, and disk flicking tasks, as well as a hardware screwdriver turning task, where we achieve successful zero-shot transfer of our simulation-trained policy and outperform state-of-the-art offline reinforcement learning and world-model-augmented behavior cloning baselines. Please see our website at https://plume-world-model.github.io for videos.
Read the full abstract

All authors Abhinav Kumar, Soshi Iba, Rana Soltani Zarrin, Dmitry Berenson

Robotics

HiPi: Reproducible High-Fidelity Piezoresistive Sensors for Robotic Manipulation

Changyi Lin, Raihan Haque, Hui-Ping Wang et al. (+1 more)

Piezoresistive tactile sensors are attractive for robotic manipulation because they are thin, lightweight, low-cost, and scalable to dense large-area sensing. However, existing systems still face a practical trade-off: recent reproducible designs emphasize accessibility and ease of reproduction, whereas high-fidelity readout architectures remain more difficult to fabricate, assemble, and deploy. We present HiPi, a reproducible high-fidelity piezoresistive sensing system for robotic manipulation. Building on a low-crosstalk readout principle, HiPi redesigns the complete hardware stack around reproducibility, deployability, and multi-sensor scalability. The system includes a compact readout PCB compatible with commercial PCB fabrication and assembly services, eliminating manual soldering; a smaller and lower-cost STM32-based MCU module; an optimized communication pipeline that achieves 220 Hz readout in a bimanual setup with four dense tactile arrays (2048 taxels in total); and FPCB-based conductive layers that simplify sensor fabrication and stacking. Experiments with structured 3D-printed contact patterns show that HiPi preserves contact geometry substantially better than a reproducible baseline, improving the average IoU from 0.428 to 0.797 and the average Dice score from 0.539 to 0.886. These results suggest that HiPi bridges an important gap between reproducible fabrication and high-fidelity readout, making dense piezoresistive tactile sensing more practical for bimanual manipulation and multi-fingered robotic systems.
Read the full abstract

All authors Changyi Lin, Raihan Haque, Hui-Ping Wang, Ding Zhao

Robotics · Cross-listed from cs.LG

Energy-Conserved Neural Pipelines: Attenuating Error Propagation in Modular Neural Networks via Physical Conservation Constraints

David Young and Swan Yi Htet

Modular neural network pipelines suffer from error compounding: noise at any module boundary propagates and potentially amplifies through subsequent modules. We introduce energy conservation as a hard physical constraint on inter-module information flow. Activation energy (the squared L2 norm of feature vectors) is enforced to be exactly preserved at every module boundary. Unlike soft energy penalties, conservation is an inviolable law: the network may redistribute energy across neurons but cannot create or destroy it. Four experiments on CIFAR-10 demonstrate: (1) conservation retains 77.4% of clean accuracy at noise sigma=0.2, versus 35.1% for baselines and 30.9% for energy-penalized models (p<0.001, 5 seeds); (2) pipelines become depth-invariant, retaining 93.3% at depths 2 through 5 with noise at every boundary; (3) the advantage generalizes to systematic bias (+45.1%), Gaussian (+40.4%), and adversarial noise (+4.8%), with a principled non-effect on dropout (-0.3%); (4) on ResNet-18, the conservation advantage scales inversely with intrinsic normalization: +0.3 pp with BatchNorm, +26.2 pp without at sigma=0.2, reaching +58.0 pp at sigma=0.5. Experiment 5 validates the operator on a real modular robotic pipeline (MuJoCo physics, Franka Panda). Across three independent runs on separate machines (90 trials per cell), conservation provides +18.9 pp average advantage on monocular-depth-style noise. A formal bound proves conserved noise energy is strictly less than input noise energy.
Read the full abstract

Robotics

Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models

Yifu Yuan, Yaoting Huang, Xianze Yao et al. (+20 more)

We introduce Embodied-R1.5, a unified Embodied Foundation Model (EFM) that integrates comprehensive embodied reasoning capabilities, spanning embodied cognition, task planning, correction, and pointing, within a single architecture toward general physical intelligence. Leveraging three automated data construction pipelines to significantly expand the data coverage of critical capabilities, we build a large-scale data system of over 15B tokens, and design a multi-task balanced RL recipe to alleviate heterogeneous task conflicts. We further introduce a Planner-Grounder-Corrector (PGC) closed-loop framework that enables a single model to autonomously execute and self-correct over long-horizon tasks. With only 8B parameters, Embodied-R1.5 achieves SOTA on 16 out of 24 embodied VLM benchmarks, surpassing leading models like Gemini-Robotics-ER-1.5 and GPT-5.4. Benefiting from the internalized embodied capabilities, Embodied-R1.5 can be fine-tuned into a VLA with only a small amount of data, outperforming leading VLA models like $\pi_{0.5}$ across 4 popular manipulation benchmark suites. We further conduct extensive zero-shot real-robot experiments, validating performance in instruction following, affordance grounding, articulated object manipulation, and long-horizon complex tasks, demonstrating strong generalization to the physical world. We open-source model weights, datasets, training code, and EmbodiedEvalKit, an evaluation framework tailored for embodied tasks, to facilitate future research in EFMs.
Read the full abstract

All authors Yifu Yuan, Yaoting Huang, Xianze Yao, Yutong Li, Shuoheng Zhang, Linqi Han, Pengyi Li, Jiangeng Sun, Wenting Jia, Zhao Zhang, Yuhao Liu, Ruihao Liao, Yucheng Hu, Qiyu Wu, Yuxiao Li, Zibin Dong, Fei Ni, Yan Zheng, Shuyang Gu, Yi Ma, Hongyao Tang, Han Hu, Jianye Hao

Robotics

Model-based Optimization of Anguilliform Swimming Gaits for Soft Robotic Applications

Brian Van Stratum, James Gallentine, Caleb Rucker et al. (+3 more)

In this paper, we introduce the Soft Lamprey-Inspired Dual Environment Robot (SLIDER) and a proper modeling and optimization procedure employed to design the robot. We represent the primary fluid environment actions - inertial effects, vortex forces, and viscous dissipation - using Lighthill's theory for large-amplitude elongated bodies. For structural design parameters such as internal pressure, tail size, and body stiffness, a fast, geometrically and materially nonlinear model is developed and validated. The fluid-structure interaction equations are solved implicitly with an efficient second-order box method. A pneumatic manifold robotic system is employed to actuate SLIDER in a quiescent water tank environment, allowing cross-comparison of computational and experimental results. We find that low-frequency swimming is dominated by resistant environmental forces, whereas higher-frequency swimming is primarily affected by inertial fluid forces. Using our efficient model alongside a genetic algorithm, we co-optimize a swimming control pattern and caudal fin design (subject to SLIDER's climbing morphology) to achieve a tethered swimming speed of 21.7 +/- 0.4 cm/s (0.59 Bl/s). Furthermore, we investigate the optimization procedure for a multimodal robot performing both swimming and climbing tasks.
Read the full abstract

All authors Brian Van Stratum, James Gallentine, Caleb Rucker, Eric Barth, Jonathan E. Clark, Kourosh Shoele