Robotics
Reinforcement Learning from Implicit Neural Feedback for Human-Aligned Robot Control
Suzie Kim
Conventional reinforcement learning (RL) approaches often struggle to learn effective policies under sparse reward conditions, necessitating the manual design of complex, task-specific reward functions. To address this limitation, reinforcement learning from human feedback (RLHF) has emerged as a promising strategy that complements hand-crafted rewards with human-derived evaluation signals. However, most existing RLHF methods depend on explicit feedback mechanisms such as button presses or preference labels, which disrupt the natural interaction process and impose a substantial cognitive load on the user. We propose a novel reinforcement learning from implicit human feedback (RLIHF) framework that utilizes non-invasive electroencephalography (
EEG) signals, specifically error-related potentials (ErrPs), to provide continuous, implicit feedback without requiring explicit user intervention. The proposed method adopts a pre-trained decoder to transform raw
EEG signals into probabilistic reward components, enabling effective policy learning even in the presence of sparse external rewards. We evaluate our approach in a simulation environment built on the MuJoCo physics engine, using a Kinova Gen2 robotic arm to perform a complex pick-and-place task that requires avoiding obstacles while manipulating target objects. The results show that
agents trained with decoded
EEG feedback achieve performance comparable to those trained with dense, manually designed rewards. These findings validate the potential of using implicit neural feedback for scalable and human-aligned reinforcement learning in interactive robotics.
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Robotics
Socially aware navigation for mobile robots: a survey on deep reinforcement learning approaches
Ibrahim Khalil Kabir and Muhammad Faizan Mysorewala
Socially aware
navigation is a fast-evolving research area in robotics that enables
robots to move within human environments while adhering to the implicit human social norms. The advent of Deep Reinforcement Learning (DRL) has accelerated the development of
navigation policies that enable
robots to incorporate these social conventions while effectively reaching their objectives. This survey offers a comprehensive overview of DRL-based approaches to socially aware
navigation, highlighting key aspects such as proxemics, human comfort, naturalness, trajectory and intention prediction, which enhance
robot interaction in human environments. This work critically analyzes the integration of value-based, policy-based, and actor-critic reinforcement learning
algorithms alongside neural network architectures, such as feedforward, recurrent, convolutional, graph, and
transformer networks, for enhancing
agent learning and representation in socially aware
navigation. Furthermore, we examine crucial evaluation mechanisms, including metrics,
benchmark datasets, simulation environments, and the persistent challenges of sim-to-real transfer. Our comparative analysis of the literature reveals that while DRL significantly improves safety, and human acceptance over traditional approaches, the field still faces setback due to non-uniform evaluation mechanisms, absence of standardized social metrics, computational burdens that limit scalability, and difficulty in transferring simulation to real robotic hardware applications. We assert that future progress will depend on hybrid approaches that leverage the strengths of multiple approaches and producing
benchmarks that balance technical efficiency with human-centered evaluation.
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Robotics
Robot joint characterisation and control using a magneto-optical rotary encoder
Yunlong Guo, John Canning, Zenon Chaczko et al. (+1 more)
A robust and compact magneto-optical rotary encoder for the characterisation of robotic rotary joints is demonstrated. The system employs
magnetic field-induced optical attenuation in a double-pass configuration using rotating nonuniform magnets around an optical circulator operating in reflection. The encoder tracks continuous 360{\deg} rotation with rotation sweep rates from {\nu} = 135 {\deg}/s to {\nu} = 370 {\deg}/s, and an angular resolution of {\Delta}{\theta} = 0.3{\deg}. This offers a low-cost and reliable alternative to conventional
robot rotation encoders while maintaining competitive performance.
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All authors Yunlong Guo, John Canning, Zenon Chaczko, Gang-Ding Peng
Robotics
Translating Cultural Choreography from Humanoid Forms to Robotic Arm
Chelsea-Xi Chen, Zhe Zhang and Aven-Le Zhou
Robotic arm choreography often reproduces trajectories while missing cultural semantics. This study examines whether symbolic posture transfer with joint space compatible notation can preserve semantic fidelity on a six-degree-of-freedom arm and remain portable across morphologies. We implement ROPERA, a three-stage pipeline for encoding culturally codified postures, composing symbolic sequences, and decoding to servo commands. A scene from Kunqu opera, The Peony Pavilion, serves as the material for evaluation. The procedure includes corpus-based posture selection, symbolic scoring, direct joint angle execution, and a visual layer with light painting and costume-informed colors. Results indicate reproducible execution with intended timing and cultural legibility reported by experts and audiences. The study points to non-anthropocentric cultural preservation and portable authoring workflows. Future work will design dance-informed transition profiles, extend the notation to locomotion with haptic, musical, and spatial cues, and test portability across platforms.
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Robotics
SVBRD-LLM: Self-Verifying Behavioral Rule Discovery for Autonomous Vehicle Identification
Xiangyu Li, Tianyi Wang, Junfeng Jiao et al. (+2 more)
As autonomous vehicles (AVs) are increasingly deployed on public roads, understanding their real-world behaviors is critical for traffic safety analysis and regulatory oversight. However, many data-driven methods lack interpretability and cannot provide verifiable explanations of AV behavior in mixed traffic. This paper proposes SVBRD-LLM, a self-verifying behavioral rule discovery framework that automatically extracts interpretable behavioral rules from real-world traffic
videos through zero-shot large
language model (LLM)
reasoning. The framework first derives vehicle trajectories using YOLOv26-based detection and ByteTrack-based tracking, then computes kinematic features and contextual information. It then employs GPT-5 zero-shot prompting to perform comparative behavioral analysis between AVs and human-driven vehicles (HDVs) across lane-changing and normal driving behaviors, generating 26 structured rule hypotheses that comprises both numerical thresholds and statistical behavioral patterns. These rules are subsequently evaluated through the AV identification task using an independent validation
dataset, and iteratively refined through failure case analysis to filter spurious correlations and improve robustness. The resulting rule library contains 20 high-confidence behavioral rules, each including semantic description, quantitative thresholds or behavioral patterns, applicable context, and validation confidence. Experiments conducted on over 1,500 hours of real-world traffic
videos from Waymo's commercial operating area demonstrate that the proposed framework achieves 90.0% accuracy and 93.3% F1-score in AV identification, with 98.0% recall. The discovered rules capture key AV traits in smoothness, conservatism, and lane discipline, informing safety assessment, regulatory compliance, and traffic management in mixed traffic. The
dataset is available at: svbrd-llm-roadside-
video-av.
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All authors Xiangyu Li, Tianyi Wang, Junfeng Jiao, Christian Claudel, Zhaomiao Guo
Robotics · Cross-listed from cs.CV
EGSA-PT:Edge-Guided Spatial Attention with Progressive Training for Monocular Depth Estimation and Segmentation of Transparent Objects
Gbenga Omotara, Ramy Farag, Seyed Mohamad Ali Tousi et al. (+1 more)
Transparent object perception remains a major challenge in computer
vision research, as transparency confounds both depth estimation and semantic
segmentation. Recent work has explored multi-task learning frameworks to improve robustness, yet negative cross-task interactions often hinder performance. In this work, we introduce Edge-Guided Spatial
Attention (EGSA), a fusion mechanism designed to mitigate destructive interactions by incorporating boundary information into the fusion between semantic and
geometric features. On both Syn-TODD and ClearPose
benchmarks, EGSA consistently improved depth accuracy over the current state of the art method (MODEST), while preserving competitive
segmentation performance, with the largest improvements appearing in transparent regions. Besides our fusion design, our second contribution is a multi-modal progressive training strategy, where learning transitions from edges derived from RGB images to edges derived from predicted depth images. This approach allows the system to bootstrap learning from the rich textures contained in RGB images, and then switch to more relevant
geometric content in depth maps, while it eliminates the need for ground-truth depth at training time. Together, these contributions highlight edge-guided fusion as a robust approach capable of improving transparent object perception.
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All authors Gbenga Omotara, Ramy Farag, Seyed Mohamad Ali Tousi, G.N. DeSouza
Robotics · Cross-listed from cs.CV
Artificial intelligence approaches for energy-efficient laser cutting machines
Mohamed Abdallah Salem, Hamdy Ahmed Ashour and Ahmed Elshenawy
This research addresses the significant challenges of energy consumption and environmental impact in
laser cutting by proposing novel deep learning (DL) methodologies to achieve energy reduction. Recognizing the current lack of adaptive control and the open-loop nature of CO2
laser suction pumps, this study utilizes closed-loop configurations that dynamically adjust pump power based on both the material being cut and the smoke level generated. To implement this adaptive system, diverse material classification methods are introduced, including techniques leveraging lens-less speckle sensing with a customized Convolutional Neural Network (CNN) and an approach using a USB camera with transfer learning via the pre-trained VGG16 CNN model. Furthermore, a separate DL model for smoke level detection is employed to simultaneously refine the pump's power output. This integration
prompts the exhaust suction pump to automatically halt during inactive times and dynamically adjust power during operation, leading to experimentally proven and remarkable energy savings, with results showing a 20% to 50% reduction in the smoke suction pump's energy consumption, thereby contributing substantially to sustainable development in the manufacturing sector.
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Robotics
A visual study of ICP variants for Lidar Odometry
Sebastian Dingler and Hannes Burrichter
Odometry with
lidar sensors is a state-of-the-art method to estimate the ego pose of a moving vehicle. Many implementations of
lidar odometry use variants of the Iterative Closest Point (ICP)
algorithm. Real-world effects such as dynamic objects, non-overlapping areas, and sensor noise diminish the accuracy of ICP. We build on a recently proposed method that makes these effects visible by visualizing the multidimensional objective function of ICP in two dimensions. We use this method to study different ICP variants in the context of
lidar odometry. In addition, we propose a novel method to filter out dynamic objects and to address the ego blind spot problem.
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Robotics
Z-Merge: Multi-Agent Reinforcement Learning for On-Ramp Merging with Zone-Specific V2X Traffic Information
Yassine Ibork, Myounggyu Won and Lokesh Das
Ramp merging is a critical and challenging task for autonomous vehicles (AVs), particularly in mixed traffic environments with human-driven vehicles (HVs). Existing approaches typically rely on either lane-changing or inter-vehicle gap creation strategies based solely on local or neighboring information, often leading to suboptimal performance in terms of safety and traffic efficiency. In this paper, we present a V2X (vehicle-to-everything communication)-assisted Multiagent Reinforcement Learning (MARL) framework for on-ramp merging that effectively coordinates the complex interplay between lane-changing and inter-vehicle gap adaptation strategies by utilizing zone-specific global information available from a roadside unit (RSU). The merging control problem is formulated as a Multiagent Partially Observable Markov Decision Process (MA-POMDP), where
agents leverage both local and global observations through V2X communication. To support both discrete and continuous control decisions, we design a hybrid action space and adopt a parameterized deep Q-learning approach. Extensive simulations, integrating the SUMO traffic simulator and the MOSAIC V2X simulator, demonstrate that our framework significantly improves merging success rate, traffic efficiency, and road safety across diverse traffic scenarios.
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Robotics · Cross-listed from cs.CR
Attacking Autonomous Driving Agents with Adversarial Machine Learning: A Holistic Evaluation with the CARLA Leaderboard
Henry Wong, Clement Fung, Weiran Lin et al. (+3 more)
To autonomously control vehicles, driving
agents use outputs from a combination of machine-learning (ML) models, controller logic, and custom modules. Although numerous prior works have shown that
adversarial examples can mislead ML models used in autonomous driving contexts, it remains unclear if these attacks are effective at producing harmful driving actions for various
agents, environments, and scenarios. To assess the risk of
adversarial examples to autonomous driving, we evaluate attacks against a variety of driving
agents, rather than against ML models in isolation. To support this evaluation, we leverage CARLA, an urban driving simulator, to create and evaluate
adversarial examples. We create
adversarial patches designed to stop or steer driving
agents, stream them into the CARLA simulator at runtime, and evaluate them against
agents from the CARLA Leaderboard, a public repository of best-performing autonomous driving
agents from an annual research competition. Unlike prior work, we evaluate attacks against autonomous driving systems without creating or modifying any driving-
agent code and against all parts of the
agent included with the ML model. We perform a case-study investigation of two attack strategies against three open-source driving
agents from the CARLA Leaderboard across multiple driving scenarios, lighting conditions, and locations. Interestingly, we show that, although some attacks can successfully mislead ML models into predicting erroneous stopping or steering commands, some driving
agents use modules, such as PID control or GPS-based rules, that can overrule attacker-manipulated predictions from ML models.
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All authors Henry Wong, Clement Fung, Weiran Lin, Karen Li, Stanley Chen, Lujo Bauer
Robotics · Cross-listed from cs.LG
$\pi^{*}_{0.6}$: a VLA That Learns From Experience
Physical Intelligence, Ali Amin, Raichelle Aniceto et al. (+53 more)
We study how
vision-
language-action (VLA) models can improve through real-world deployments via reinforcement learning (RL). We present a general-purpose method, RL with Experience and Corrections via Advantage-conditioned Policies (RECAP), that provides for RL training of VLAs via advantage conditioning. Our method incorporates heterogeneous data into the self-improvement process, including demonstrations, data from on-policy collection, and expert teleoperated interventions provided during autonomous execution. RECAP starts by pre-training a generalist VLA with offline RL, which we call $\pi^{*}_{0.6}$, that can then be specialized to attain high performance on downstream tasks through on-
robot data collection. We show that the $\pi^{*}_{0.6}$ model trained with the full RECAP method can fold laundry in real homes, reliably assemble boxes, and make espresso drinks using a professional espresso machine. On some of the hardest tasks, RECAP more than doubles task throughput and roughly halves the task failure rate.
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All authors Physical Intelligence, Ali Amin, Raichelle Aniceto, Ashwin Balakrishna, Kevin Black, Ken Conley, Grace Connors, James Darpinian, Karan Dhabalia, Jared DiCarlo, Danny Driess, Michael Equi, Adnan Esmail, Yunhao Fang, Chelsea Finn, Catherine Glossop, Thomas Godden, Ivan Goryachev, Lachy Groom, Hunter Hancock, Karol Hausman, Gashon Hussein, Brian Ichter, Szymon Jakubczak, Rowan Jen, Tim Jones, Ben Katz, Liyiming Ke, Chandra Kuchi, Marinda Lamb, Devin LeBlanc, Sergey Levine, Adrian Li-Bell, Yao Lu, Vishnu Mano, Mohith Mothukuri, Suraj Nair, Karl Pertsch, Allen Z. Ren, Charvi Sharma, Lucy Xiaoyang Shi, Laura Smith, Jost Tobias Springenberg, Kyle Stachowicz, Will Stoeckle, Alex Swerdlow, James Tanner, Marcel Torne, Quan Vuong, Anna Walling, Haohuan Wang, Blake Williams, Sukwon Yoo, Lili Yu, Ury Zhilinsky, Zhiyuan Zhou
Robotics
HMC: Learning Heterogeneous Meta-Control for Contact-Rich Loco-Manipulation
Lai Wei, Xuanbin Peng, Ri-Zhao Qiu et al. (+3 more)
Learning from real-world
robot demonstrations holds promise for interacting with complex real-world environments. However, the complexity and variability of interaction dynamics often cause purely positional controllers to struggle with contacts or varying payloads. To address this, we propose a Heterogeneous Meta-Control (HMC) framework for Loco-Manipulation that adaptively stitches multiple control modalities: position, impedance, and hybrid force-position. We first introduce an interface, HMC-Controller, for blending actions from different control profiles continuously in the torque space. HMC-Controller facilitates both teleoperation and policy deployment. Then, to learn a robust force-aware policy, we propose HMC-Policy to unify different controllers into a heterogeneous architecture. We adopt a mixture-of-experts style routing to learn from large-scale position-only data and fine-grained force-aware demonstrations. Experiments on a real
humanoid robot show over 50% relative improvement vs. baselines on challenging tasks such as compliant table wiping and drawer opening, demonstrating the efficacy of HMC.
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All authors Lai Wei, Xuanbin Peng, Ri-Zhao Qiu, Tianshu Huang, Xuxin Cheng, Xiaolong Wang
Robotics
Robust Verification of Controllers under State Uncertainty via Hamilton-Jacobi Reachability Analysis
Albert Lin, Alessandro Pinto and Somil Bansal
As perception-based controllers for autonomous systems become increasingly popular in the real world, it is important that we can formally verify their safety and performance despite perceptual uncertainty. Unfortunately, the verification of such systems remains challenging, largely due to the complexity of the controllers, which are often nonlinear, nonconvex, learning-based, and/or black-box. Prior works propose verification
algorithms that are based on approximate reachability methods, but they often restrict the class of controllers and systems that can be handled or result in overly conservative analyses. Hamilton-Jacobi (HJ) reachability analysis is a popular formal verification tool for general nonlinear systems that can compute optimal reachable sets under worst-case system uncertainties; however, its application to perception-based systems is currently underexplored. In this work, we propose RoVer-CoRe, a framework for the Robust Verification of Controllers via HJ Reachability. To the best of our knowledge, RoVer-CoRe is the first HJ reachability-based framework for the verification of perception-based systems under perceptual uncertainty. Our key insight is to concatenate the system controller, observation function, and the state estimation modules to obtain an equivalent closed-loop system that is readily compatible with existing reachability frameworks. Within RoVer-CoRe, we propose novel methods for formal safety verification and robust controller design. We demonstrate the efficacy of the framework in case studies involving aircraft taxiing and NN-based rover
navigation. Code is available at the link in the footnote.
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Robotics · Cross-listed from cs.CV
Yutian Chen, Yuheng Qiu, Ruogu Li et al. (+4 more)
We propose Confidence-Guided
Token Merging (Co-Me), an acceleration mechanism for visual
geometric transformers without retraining or finetuning the base model. Co-Me distilled a light-weight confidence predictor to rank
tokens by uncertainty and selectively merge low-confidence ones, effectively reducing computation while maintaining spatial coverage. Compared to similarity-based merging or
pruning, the confidence signal in Co-Me reliably indicates regions emphasized by the
transformer, enabling substantial acceleration without degrading performance. Co-Me applies seamlessly to various multi-view and streaming visual
geometric transformers, achieving speedups that scale with sequence length. When applied to VGGT and Pi3, Co-Me achieves up to 21.5x and 20.4x speedup, making visual
geometric transformers practical for real-time 3D perception and reconstruction.
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All authors Yutian Chen, Yuheng Qiu, Ruogu Li, Ali Agha, Shayegan Omidshafiei, Jay Patrikar, Sebastian Scherer
Robotics
NORA-1.5: A Vision-Language-Action Model Trained using World Model- and Action-based Preference Rewards
Chia-Yu Hung, Navonil Majumder, Haoyuan Deng et al. (+7 more)
Vision–
language–action (VLA) models have recently shown promising performance on a variety of
embodied tasks, yet they still fall short in reliability and generalization, especially when deployed across different embodiments or real-world environments. In this work, we introduce NORA-1.5, a VLA model built from the pre-trained NORA backbone by adding to it a flow-matching-based action expert. This architectural enhancement alone yields substantial performance gains, enabling NORA-1.5 to outperform NORA and several state-of-the-art VLA models across both simulated and real-world
benchmarks. To further improve robustness and task success, we develop a set of reward models for post-training VLA policies. Our rewards combine (i) an action-conditioned world model (WM) that evaluates whether generated actions lead toward the desired goal, and (ii) a deviation-from-ground-truth heuristic that distinguishes good actions from poor ones. Using these reward signals, we construct preference
datasets and adapt NORA-1.5 to target embodiments through direct preference optimization (DPO). Extensive evaluations show that reward-driven post-training consistently improves performance in both simulation and real-
robot settings, demonstrating significant VLA model-reliability gains through simple yet effective reward models. Our findings highlight NORA-1.5 and reward-guided post-training as a viable path toward more dependable
embodied agents suitable for real-world deployment.
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All authors Chia-Yu Hung, Navonil Majumder, Haoyuan Deng, Liu Renhang, Yankang Ang, Amir Zadeh, Chuan Li, Dorien Herremans, Ziwei Wang, Soujanya Poria
Robotics
Gallant: Voxel Grid-based Humanoid Locomotion and Local-navigation across 3D Constrained Terrains
Qingwei Ben, Botian Xu, Kailin Li et al. (+6 more)
Robust
humanoid locomotion requires accurate and globally consistent perception of the surrounding 3D environment. However, existing perception modules, mainly based on depth images or elevation maps, offer only partial and locally flattened views of the environment, failing to capture the full 3D structure. This paper presents Gallant, a voxel-grid-based framework for
humanoid locomotion and local
navigation in 3D constrained terrains. It leverages voxelized
LiDAR data as a lightweight and structured perceptual representation, and employs a z-grouped 2D CNN to map this representation to the control policy, enabling fully end-to-end optimization. A high-fidelity
LiDAR simulation that dynamically generates realistic observations is developed to support scalable,
LiDAR-based training and ensure sim-to-real consistency. Experimental results show that Gallant's broader perceptual coverage facilitates the use of a single policy that goes beyond the limitations of previous methods confined to ground-level obstacles, extending to lateral clutter, overhead constraints, multi-level structures, and narrow passages. Gallant also firstly achieves near 100% success rates in challenging scenarios such as stair climbing and stepping onto elevated platforms through improved end-to-end optimization.
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All authors Qingwei Ben, Botian Xu, Kailin Li, Feiyu Jia, Wentao Zhang, Jingping Wang, Jingbo Wang, Dahua Lin, Jiangmiao Pang
Robotics · Cross-listed from cond-mat.soft
Active Matter as a framework for living systems-inspired Robophysics
Giulia Janzen, Gaia Maselli, Juan F. Jimenez et al. (+3 more)
Robophysics investigates the physical principles that govern living-like
robots operating in complex, realworld environments. Despite remarkable technological advances,
robots continue to face fundamental efficiency limitations. At the level of individual units, locomotion remains a challenge, while at the collective level,
robot swarms struggle to achieve shared purpose, coordination, communication, and cost efficiency. This perspective article examines the key challenges faced by bio-inspired robotic collectives and highlights recent research efforts that incorporate principles from active-matter physics and biology into the modeling and design of
robot swarms.
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All authors Giulia Janzen, Gaia Maselli, Juan F. Jimenez, Lia Garcia-Perez, D A Matoz Fernandez, Chantal Valeriani
Robotics
DSBench: A Comprehensive Benchmark for Evaluating External and In-Cabin Risks
Xianhui Meng, Yuchen Zhang, Zhijian Huang et al. (+12 more)
Vision-
Language Models (VLMs) show great promise for autonomous driving, but their suitability for safety-critical scenarios is largely unexplored, raising safety concerns. This issue arises from the lack of comprehensive
benchmarks that assess both external environmental risks and in-cabin driving behavior safety simultaneously. To bridge this critical gap, we introduce DSBench, the first comprehensive Driving Safety
Benchmark designed to assess a VLM's awareness of various safety risks in a unified manner. DSBench encompasses two major categories: external environmental risks and in-cabin driving behavior safety, divided into 10 key categories and a total of 28 sub-categories. This comprehensive evaluation covers a wide range of scenarios, ensuring a thorough assessment of VLMs' performance in safety-critical contexts. Extensive evaluations across various mainstream open-source and closed-source VLMs reveal significant performance degradation under complex safety-critical situations, highlighting urgent safety concerns. To address this, we constructed a large
dataset of 98K instances focused on in-cabin and external safety scenarios, showing that fine-tuning on this
dataset significantly enhances the safety performance of existing VLMs and paves the way for advancing autonomous driving technology. The
benchmark toolkit, code, and model checkpoints will be publicly accessible.
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All authors Xianhui Meng, Yuchen Zhang, Zhijian Huang, Zheng Lu, Ziling Ji, Yandan Lin, Yaoyao Yin, Hongyuan Zhang, Wei Zhou, Guangfeng Jiang, Li Zhang, Long Chen, Hangjun Ye, Jun Liu, Xiaoshuai Hao
Robotics
Masked IRL: LLM-Guided Reward Disambiguation from Demonstrations and Language
Minyoung Hwang, Alexandra Forsey-Smerek, Nathaniel Dennler et al. (+1 more)
Robots can adapt to user preferences by learning reward functions from demonstrations, but with limited data, reward models often overfit to spurious correlations and fail to generalize. This happens because demonstrations show
robots how to do a task but not what matters for that task, causing the model to focus on irrelevant state details. Natural
language can more directly specify what the
robot should focus on, and, in principle, disambiguate between many reward functions consistent with the demonstrations. However, existing
language-conditioned reward learning methods typically treat instructions as simple conditioning signals, without fully exploiting their potential to resolve ambiguity. Moreover, real instructions are often ambiguous themselves, so naive conditioning is unreliable. Our key insight is that these two input types carry complementary information: demonstrations show how to act, while
language specifies what is important. We propose Masked Inverse Reinforcement Learning (Masked IRL), a framework that uses large
language models (LLMs) to combine the strengths of both input types. Masked IRL infers state-relevance masks from
language instructions and enforces invariance to irrelevant state components. When instructions are ambiguous, it uses LLM
reasoning to clarify them in the context of the demonstrations. In simulation and on a real
robot, Masked IRL outperforms prior
language-conditioned IRL methods by up to 15% while using up to 4.7 times less data, demonstrating improved sample-efficiency, generalization, and robustness to ambiguous
language. Project page: https://MIT-CLEAR-Lab.github.io/Masked-IRL and Code: https://github.com/MIT-CLEAR-Lab/Masked-IRL
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All authors Minyoung Hwang, Alexandra Forsey-Smerek, Nathaniel Dennler, Andreea Bobu
Robotics
Aerial Assistance System for Automated Firefighting during Turntable Ladder Operations
Jan Quenzel, Valerij Sekin, Daniel Schleich et al. (+5 more)
Fires in industrial facilities pose special challenges to firefighters, e.g., due to the sheer size and scale of the buildings. The resulting visual obstructions impair firefighting accuracy, further compounded by inaccurate assessments of the fire's location. Such imprecision simultaneously increases the overall damage and prolongs the fire-brigades operation unnecessarily. We propose an automated assistance system for firefighting using a motorized fire monitor on a turntable ladder with aerial support from an unmanned aerial vehicle (
UAV). The
UAV flies autonomously within an obstacle-free flight funnel derived from geodata, detecting and localizing heat sources. An operator supervises the operation on a handheld controller and selects a fire target in reach. After the selection, the
UAV automatically plans and traverses between two triangulation poses for continued fire localization. Simultaneously, our system steers the fire monitor to ensure the water jet reaches the detected heat source. In preliminary tests, our assistance system successfully localized multiple heat sources and directed a water jet towards the fires.
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All authors Jan Quenzel, Valerij Sekin, Daniel Schleich, Alexander Miller, Merlin Stampa, Norbert Pahlke, Christof Röhrig, Sven Behnke
Robotics · Cross-listed from cs.CV
Enhancing End-to-End Autonomous Driving with Risk Semantic Distillaion from VLM
Jack Qin, Zhitao Wang, Yinan Zheng et al. (+4 more)
The autonomous driving (AD) system has exhibited remarkable performance in complex driving scenarios. However, generalization is still a key limitation for the current system, which refers to the ability to handle unseen scenarios or unfamiliar sensor configurations.Related works have explored the use of
Vision-
Language Models (VLMs) to address few-shot or zero-shot tasks. While promising, these methods introduce a new challenge: the emergence of a hybrid AD system, where two distinct systems are used to plan a trajectory, leading to potential inconsistencies. Alternative research directions have explored
Vision-
Language-Action (VLA) frameworks that generate control actions from VLM directly. However, these end-to-end solutions demonstrate prohibitive computational demands. To overcome these challenges, we introduce Risk Semantic
Distillation (RSD), a novel framework that leverages VLMs to enhance the training of End-to-End (E2E) AD backbones. By providing risk
attention for key objects, RSD addresses the issue of generalization. Specifically, we introduce RiskHead, a plug-in module that distills causal risk estimates from
Vision-
Language Models into Bird's-Eye-View (BEV) features, yielding interpretable risk-
attention maps.This approach allows BEV features to learn richer and more nuanced risk
attention representations, which directly enhance the model's ability to handle spatial boundaries and risky objects.By focusing on risk
attention, RSD aligns better with human-like driving behavior, which is essential to navigate in complex and dynamic environments. Our experiments on the Bench2Drive
benchmark demonstrate the effectiveness of RSD in managing complex and unpredictable driving conditions. Due to the enhanced BEV representations enabled by RSD, we observed a significant improvement in both perception and planning capabilities.
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All authors Jack Qin, Zhitao Wang, Yinan Zheng, Keyu Chen, Yang Zhou, Yuanxin Zhong, Siyuan Cheng
Robotics
Advancing Minimally Invasive Precision Surgery in Large Open Cavities with Robotic Flexible Endoscopy
Michelle Mattille, Alexandre Mesot, Miriam Weisskopf et al. (+4 more)
Flexible
robots hold great promise for enhancing minimally invasive surgery (MIS) by providing superior dexterity, precise control, and safe tissue interaction. Yet, translating these advantages into endoscopic interventions within large open cavities remains challenging. The lack of anatomical constraints and the inherent flexibility of such devices complicate their control, and the limited field of view of endoscopes restricts situational awareness. We present a robotic platform designed to overcome these challenges and demonstrate its potential in fetoscopic
laser coagulation, a complex MIS procedure typically performed only by highly experienced surgeons. Our system combines a magnetically actuated flexible endoscope with teleoperated and semi-autonomous
navigation capabilities for performing targeted
laser ablations. To enhance
surgical awareness, the platform reconstructs real-time mosaics of the endoscopic scene, providing an extended and continuous visual context. The ability of this system to address the key limitations of MIS in open spaces is validated in vivo in an ovine model.
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All authors Michelle Mattille, Alexandre Mesot, Miriam Weisskopf, Nicole Ochsenbein-Koelble, Ueli Moehrlen, Bradley J. Nelson, Quentin Boehler
Robotics · Cross-listed from cs.LO
Towards A Catalogue of Requirement Patterns for Space Robotic Missions
Mahdi Etumi, Hazel M. Taylor and Marie Farrell
In the development of safety and mission-critical systems, including autonomous space robotic missions, complex behaviour is captured during the requirements elicitation phase. Requirements are typically expressed using natural
language which is ambiguous and not amenable to formal verification methods that can provide robust guarantees of system behaviour. To support the definition of formal requirements, specification patterns provide reusable, logic-based templates. A suite of robotic specification patterns, along with their formalisation in NASA's Formal Requirements Elicitation Tool (FRET) already exists. These pre-existing requirement patterns are domain agnostic and, in this paper we explore their applicability for space missions. To achieve this we carried out a literature review of existing space missions and formalised their requirements using FRET, contributing a corpus of space mission requirements. We categorised these requirements using pre-existing specification patterns which demonstrated their applicability in space missions. However, not all of the requirements that we formalised corresponded to an existing pattern so we have contributed 5 new requirement specification patterns as well as several variants of the existing and new patterns. We also conducted an expert evaluation of the new patterns, highlighting their benefits and limitations.
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Robotics
Achieving Safe Control Online through Integration of Harmonic Control Lyapunov-Barrier Functions with Unsafe Object-Centric Action Policies
Marlow Fawn and Matthias Scheutz
We propose a method for combining Harmonic Control Lyapunov-Barrier Functions (HCLBFs) derived from Signal Temporal Logic (STL) specifications with any given
robot policy to turn an unsafe policy into a safe one with formal guarantees. The two components are combined via HCLBF-derived safety certificates, thus producing commands that preserve both safety and task-driven behavior. We demonstrate with a simple proof-of-concept implementation for an object-centric force-based policy trained through reinforcement learning for a movement task of a stationary
robot arm that is able to avoid colliding with obstacles on a table top after combining the policy with the safety constraints. The proposed method can be generalized to more complex specifications and dynamic task settings.
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Robotics · Cross-listed from cs.LO
Safe-ROS: An Architecture for Autonomous Robots in Safety-Critical Domains
Diana C. Benjumea, Marie Farrell and Louise A. Dennis
Deploying autonomous
robots in safety-critical domains requires architectures that ensure operational effectiveness and safety compliance. In this paper, we contribute the Safe-ROS architecture for developing reliable and verifiable autonomous
robots in such domains. It features two distinct subsystems: (1) an intelligent control system that is responsible for normal/routine operations, and (2) a Safety System consisting of Safety Instrumented Functions (SIFs) that provide formally verifiable independent oversight. We demonstrate Safe-ROS on an AgileX Scout Mini
robot performing autonomous inspection in a
nuclear environment. One safety requirement is selected and instantiated as a SIF. To support verification, we implement the SIF as a cognitive
agent, programmed to stop the
robot whenever it detects that it is too close to an obstacle. We verify that the
agent meets the safety requirement and integrate it into the autonomous inspection. This integration is also verified, and the full deployment is validated in a Gazebo simulation, and lab testing. We evaluate this architecture in the context of the UK
nuclear sector, where safety and regulation are crucial aspects of deployment. Success criteria include the development of a formal property from the safety requirement, implementation, and verification of the SIF, and the integration of the SIF into the operational robotic autonomous system. Our results demonstrate that the Safe-ROS architecture can provide safety verifiable oversight while deploying autonomous
robots in safety-critical domains, offering a robust framework that can be extended to additional requirements and various applications.
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