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cs.RO: explore 673 source-linked works published from 2023 to 2026, with original documents and citations.

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Includes records with this source-supplied label or an explicit phrase match in their metadata. Matches indicate a mention, not proof that a paper uses a method or tests a material. Source versions are consolidated by DOI.

Sources: arxiv. Collection updated 2026-09-16. Counts describe this index, not the complete source archives.

arXiv2026

Transformer-Based Autonomous Driving Models and Deployment-Oriented Compression: A Survey

Transformer-based models are becoming a central paradigm in autonomous driving because they can capture long-range spatial dependencies, multi-agent interactions, and multimodal context across perception, prediction, and planning. At the same time, their deployment in real vehicles remains difficult because high-capacity attention-based architectures impose substantial latency, memory, and energy overhead. This survey reviews representative Transformer-based autonomous driving models and organizes them by task role, sensing configuration, and architectural design. More importantly, it examines these models from a deployment-oriented perspective and analyzes how efficiency constraints reshape model design choices in practice. We further review compression and acceleration strategies relevant to Transformer-based driving systems, including quantization, pruning, knowledge distillation, low-rank approximation, and efficient attention, and discuss their benefits, limitations, and task-dependent applicability. Rather than treating compression as an isolated post-processing step, we highlight it as a system-level design consideration that directly affects deployability, robustness, and safety. Finally, we identify open challenges and future research directions toward standardized, safety-aware, and hardware-conscious evaluation of efficient autonomous driving systems.

cs.LG↗
arXiv2026

Receding Fixed-Horizon Optimization for Near-Time-Optimal Trajectory Planning and Control

Time-optimal trajectory planning and control is central for autonomous vehicles, yet its application and real-time deployment confronts two fundamental challenges: the non-convexity of optimal control problems and the unpredictable computation time inherent to nonlinear programming. To address these challenges, we propose a hierarchical convex optimization framework that addresses both issues by decomposing the original problem into short, fixed-horizon planning cycles. Each cycle solves a convex subproblem within a collision-free region identified by a customized search algorithm; the complete trajectory and control is assembled by concatenating state-input sequences across cycles. Under mild assumptions, we establish finite-time convergence of the decomposition procedure and show that the concatenated solution satisfies the necessary conditions for local optimality. Numerical experiments on randomly generated maps with static and dynamic obstacles demonstrate that the proposed algorithm achieves a higher success rate and substantially lower computation time than sequential convex programming, while maintaining comparable control time. These results show that decomposition-based convex optimization provides a practical pathway to reliable, real-time near-time-optimal trajectory planning.

cs.RO↗
arXiv2026

Differentiable Dynamics and Fast Simulation of Continuous Elastic Robotic Fish

Body flexibility plays a critical role in fish-like swimming, as the spatial distribution of stiffness governs body deformation, hydrodynamic loading, and propulsive performance. Exploiting this mechanism in robotic fish requires dynamic models that capture continuous body elasticity, fluid-structure interaction, and the resulting self-propelled motion. Existing approaches often prescribe body kinematics, approximate the body using discrete rigid or compliant segments, or incur high computational costs that limit their use in design optimization. In this letter, we present a differentiable full-body dynamics model and fast simulation framework for motor-actuated elastic robotic fish based on Hamilton's principle. The proposed formulation represents the robot as a continuously deformable elastic body and couples its structural dynamics with hydrodynamic forces without prescribing body kinematics. The resulting simulator is differentiable with respect to model and design parameters, enabling efficient gradient-based optimization. Numerical convergence studies and experiments with a physical robotic fish validate the proposed framework. Finally, gradient-based optimization of the body stiffness distribution demonstrates its utility for efficient design of elastic robotic fish.

cs.RO↗
arXiv2026

OceanGym: A Benchmark Environment for Underwater Embodied Agents

We introduce OceanGym, the first comprehensive benchmark for ocean underwater embodied agents, designed to advance AI in one of the most demanding real-world environments. Unlike terrestrial or aerial domains, underwater settings present extreme perceptual and decision-making challenges, including low visibility, dynamic ocean currents, making effective agent deployment exceptionally difficult. OceanGym encompasses eight realistic task domains and a unified agent framework driven by Multi-modal Large Language Models (MLLMs), which integrates perception, memory, and sequential decision-making. Agents are required to comprehend optical and sonar data, autonomously explore complex environments, and accomplish long-horizon objectives under these harsh conditions. Extensive experiments reveal substantial gaps between state-of-the-art MLLM-driven agents and human experts, highlighting the persistent difficulty of perception, planning, and adaptability in ocean underwater environments. By providing a high-fidelity, rigorously designed platform, OceanGym establishes a testbed for developing robust embodied AI and transferring these capabilities to real-world autonomous ocean underwater vehicles, marking a decisive step toward intelligent agents capable of operating in one of Earth's last unexplored frontiers. The code and data are available at https://github.com/OceanGPT/OceanGym.

cs.CL↗
arXiv2026

Object Reconstruction under Occlusion with Generative Priors and Contact-induced Constraints

Object geometry is key information for robot manipulation. Yet, object reconstruction is a challenging task because camera observations are partial due to occlusions. The scene may not offer the flexibility for a robot to alter its viewpoint to obtain a full observation of the object of interest. In this paper, we leverage two extra sources of information to reduce the ambiguity of vision signals under occlusion. First, generative models learn priors of the shapes of commonly seen objects, allowing us to make reasonable guesses of the unseen part of geometry. Second, contact information, which can be obtained from videos and physical interactions, provides sparse constraints on the boundary of the geometry. We combine the two sources of information through contact-guided 3D generation. The guidance formulation is inspired by drag-based generative image editing. We explore different guidance strategies and highlight the importance of short gradient paths for guided generation. Experiments on synthetic and real-world data show that our approach improves the object reconstruction compared to pure 3D generation and contact-based optimization methods.

cs.CV↗
arXiv2026

Ethical Asymmetry in Human-Robot Interaction - An Empirical Test of Sparrow's Hypothesis

The ethics of human-robot interaction (HRI) have been discussed extensively based on three traditional frameworks: deontology, consequentialism, and virtue ethics. We conducted a mixed within/between experiment to investigate Sparrow's proposed ethical asymmetry hypothesis in human treatment of robots. The moral permissibility of action (MPA) was manipulated as a subject grouping variable, and virtue type (prudence, justice, courage, and temperance) was controlled as a within-subjects factor. We tested moral stimuli using an online questionnaire with Perceived Moral Permissibility of Action (PMPA) and Perceived Virtue Scores (PVS) as response measures. The PVS measure was based on an adaptation of the established Questionnaire on Cardinal Virtues (QCV), while the PMPA was based on Malle et al. [39] work. We found that the MPA significantly influenced the PMPA and perceived virtue scores. The best-fitting model to describe the relationship between PMPA and PVS was cubic, which is symmetrical in nature. Our study did not confirm Sparrow's asymmetry hypothesis. The adaptation of the QCV is expected to have utility for future studies, pending additional psychometric property assessments.

cs.HC↗
arXiv2026

SCALE: Self-uncertainty Conditioned Adaptive Looking and Execution for Vision-Language-Action Models

Vision-Language-Action (VLA) models have emerged as a promising paradigm for general-purpose robotic control, with test-time scaling (TTS) gaining attention to enhance robustness beyond training. However, existing TTS methods for VLAs require additional training, verifiers, and multiple forward passes, making them impractical for deployment. Moreover, they intervene only at action decoding while keeping visual representations fixed-insufficient under perceptual ambiguity, where reconsidering how to perceive is as important as deciding what to do. To address these limitations, we propose SCALE, a simple inference strategy that jointly modulates visual perception and action based on 'self-uncertainty', inspired by uncertainty-driven exploration in Active Inference theory-requiring no additional training, no verifier, and only a single forward pass. SCALE broadens exploration in both perception and action under high uncertainty, while focusing on exploitation when confident-enabling adaptive execution across varying conditions. Experiments on simulated and real-world benchmarks demonstrate that SCALE improves state-of-the-art VLAs and outperforms existing TTS methods while maintaining single-pass efficiency.

cs.RO↗
arXiv2026

FlowCorrect: Efficient Interactive Correction of Generative Flow Policies for Robotic Manipulation

Generative manipulation policies can fail catastrophically under deployment-time distribution shift, yet many failures are near-misses: the robot reaches almost-correct poses and would succeed with a small corrective motion. We propose FlowCorrect, a modular interactive imitation learning approach that enables deployment-time adaptation of flow-matching manipulation policies from sparse, relative human corrections without retraining. During execution, a human provides brief corrective pose nudges via a lightweight VR interface. FlowCorrect uses these sparse corrections to locally adapt the policy, improving actions without retraining the backbone while preserving the model performance on previously learned scenarios. We evaluate on a real-world robot across four tabletop tasks: pick-and-place, pouring, cup uprighting, and insertion. With a low correction budget, FlowCorrect achieves an 80% success rate on previously failed cases while preserving performance on previously solved scenarios. The results clearly demonstrate that FlowCorrect learns from very few demonstrations and enables fast, sample-efficient, incremental, human-in-the-loop corrections of generative visuomotor policies at deployment time in real-world robotics.

cs.RO↗
arXiv2026

From Dialogue to Execution: Mixture-of-Agents Assisted Interactive Planning for Behavior Tree-Based Long-Horizon Robot Execution

Interactive task planning with large language models (LLMs) lets robots generate high-level action plans from natural language, but over long horizons it asks many questions, and tabular plan representations become hard to manage. We propose a framework that integrates Mixture-of-Agents (MoA)-based proxy answering into interactive planning and generates Behavior Trees (BTs) for structured long-term execution. We formulate the MoA as an abstention-based delegation cascade: each expert agent answers only the questions entailed by its own prerequisite description, forwards the rest unchanged, and the human user acts as the terminal fallback. The question set is thus partitioned disjointly, so no answer fusion or arbitration is required while every question is still resolved. The BT represents task logic hierarchically and enables retry and dynamic switching among robot policies. Experiments on a cocktail-making task show that the method removes approximately 27% of the human responses while keeping the generated BTs within the baseline generator's own variance. Real-robot experiments on a smoothie-making task further demonstrate successful long-horizon execution with adaptive policy switching and recovery from action failures. We further analyze the failure modes of the framework and show that its applicability boundary is set by the reliability of the weakest action node rather than by the planner. These results indicate that MoA-assisted interactive planning improves dialogue efficiency while preserving execution quality in real-world robotic tasks.

cs.RO↗
arXiv2026

Energy-Efficient Collaborative Transport of Tether-Suspended Payloads via Rotating Equilibrium

Collaborative aerial transportation of tethered payloads is fundamentally limited by space, power, and weight constraints. Conventional approaches rely on static equilibrium conditions, where each vehicle tilts to generate the forces that ensure they maintain a formation geometry that avoids aerodynamic interactions and collision. This horizontal thrust component represents a significant energy penalty compared to the ideal case in which each vehicle produces purely vertical thrust to lift the payload. Operating in tighter tether configurations can minimize this effect, but at the cost of either having to fly the vehicles in closer proximity, which risks collision, or significantly increasing the length of the tether, which increases complexity and reduces potential use-cases. We propose operating the tether-suspended flying system at a rotating equilibrium. By maintaining steady circular motion, centrifugal forces provide the necessary horizontal tether tension, allowing each quadrotor to generate purely vertical thrust and thus reducing the total force (and power) required compared to an equilibrium where the thrusts are not vertical. It also allows for a wider range of tether configurations to be used without sacrificing efficiency. Results demonstrate that rotating equilibria can reduce power consumption relative to static lifting by up to 20%, making collaborative aerial solutions more practically relevant.

cs.RO↗
arXiv2026

Scale-Plan: Scalable Language-Enabled Task Planning for Heterogeneous Multi-Robot Teams

Long-horizon task planning for heterogeneous multi-robot systems is essential for deploying collaborative teams in real-world environments; yet, it remains challenging due to the large volume of perceptual information, much of which is irrelevant to task objectives and burdens planning. Traditional symbolic planners rely on manually constructed problem specifications, limiting scalability and adaptability, while recent large language model (LLM)-based approaches often suffer from hallucinations and weak grounding-i.e., poor alignment between generated plans and actual environmental objects and constraints-in object-rich settings. We present Scale-Plan, a scalable LLM-assisted framework that generates compact, task-relevant problem representations from natural language instructions. Given a PDDL domain specification, Scale-Plan constructs an action graph capturing domain structure and uses shallow LLM reasoning to guide a structured graph search that identifies a minimal subset of relevant actions and objects. By filtering irrelevant information prior to planning, Scale-Plan enables efficient decomposition, allocation, and long-horizon plan generation. We evaluate our approach on complex multi-agent tasks and introduce MAT2-THOR, a cleaned benchmark built on AI2-THOR for reliable evaluation of multi-robot planning systems. Scale-Plan outperforms pure LLM and hybrid LLM-PDDL baselines across all metrics, improving scalability and reliability. Project website: https://github.com/honda-research-institute/Scale_Plan

cs.RO↗
arXiv2026

Reasoning models do not yet follow their reasoning in autonomous driving: The KITScenes LongTail Dataset

Handling rare events is the central open challenge in autonomous driving. Reasoning models, which generate explicit chains of reasoning before acting, promise to generalize to such events. Here we show that these models frequently do not follow their own reasoning: the actions they state in their reasoning often diverge from the actions they ultimately execute. We introduce KITScenes LongTail, a curated dataset of rare driving scenarios to quantify this divergence through a measure of semantic reasoning-action coherence. We find that incoherence is widespread across current general-purpose and domain-specific models. Strikingly, when reasoned actions and executed actions disagree, the reasoning is usually right: extracting actions from the reasoning trace and executing them through a kinematic model enforces coherence and typically improves motion planning. These results suggest that the reasoning capabilities of current models exceed what their actions reveal, and that coherently acting on stated reasoning is a prerequisite for trustworthy autonomous driving. Our dataset and evaluation space are available at: https://hf.co/datasets/kit-mrt/kitscenes-longtail

cs.CV↗
arXiv2026

Benefits of Low-Cost Bio-Inspiration in the Age of Overparametrization

While Central Pattern Generators (CPGs) and Multi-Layer Perceptrons (MLP) are widely used paradigms in robot control, few systematic studies have been performed on the relative merits of large parameter spaces in highly constrained settings. As opposed to traditional Machine Learning contexts, our input and output spaces are small and performance is bounded thus having more parameters may actively hinder the learning process instead of empowering it. To empirically measure this, we submit a given robot morphology, with limited proprioceptive capabilities, to controller optimisation under two bio-inspired paradigms (CPGs and MLPs) with evolutionary- and reinforcement- trainer protocols. By varying parameter spaces across multiple reward functions, we demonstrate that shallow MLPs and densely connected CPGs result in better performance when compared to deeper MLPs or Actor-Critic architectures. To account for the relationship between said performance and the number of parameters, we introduce a Parameter Impact metric which showcases diminishing returns for MLPs but not for CPGs. Taken together these results demonstrate, on a fixed quadrupedal morphology, the benefits of integrating prior bias when considering locomotion tasks with simple hinge actuators.

cs.RO↗
arXiv2026

SUFLECA: Scaling Up Feature Learning for CAD-to-image Alignment

CAD-to-image alignment aims to estimate an object's 9D pose (rotation, translation, and anisotropic scale) from a single RGB image, with applications in robotics and augmented reality. Recent zero-shot methods use vision foundation models to match image regions to CAD models; yet their correspondences are typically appearance-driven or unreliable under occlusion or synthetic-to-real domain shift. To address these limitations, we introduce SUFLECA (Scaling Up Feature LEarning for CAD-to-image Alignment), a weakly supervised framework for zero-shot CAD alignment with two key contributions. First, SUFLECA scales up geometry-grounded feature learning from pretrained visual representations through Normalized Object Coordinates (NOCs) supervision on images spanning up to 12 real and synthetic datasets, learning compact geometry-aware features that generalize across domains. Second, we propose a geometrically consistent matching algorithm that establishes reliable CAD-to-image correspondences. Together, these contributions enable accurate, sub-second alignment per object instance without iterative pose refinement. On ScanNet25k, SUFLECA achieves 32.8%/42.6% category/instance accuracy, outperforming the strongest zero-shot baseline by 9.7/12.5 percentage points with a smaller computational footprint, and for the first time on this benchmark, even surpassing existing pose-supervised methods. Code is available at: https://github.com/snt-arg/SUFLECA

cs.CV↗
arXiv2026

FleetScape: A Mixed Reality Sandtable for Spatial Supervision and Control of Scalable Drone Fleets

As autonomous drone deployments scale from individual units to coordinated swarms, the human operator's role shifts from direct piloting to high-level supervision. Current interfaces often treat multi-drone control as a scaled-up version of single-drone operation. We instead investigate how reframing fleet supervision as spatial interaction can better support the spatial, temporal, and safety demands of complex missions. We present FleetScape, a Mixed Reality (MR) sandtable system that externalizes layered real-time mission, safety, and environmental data while enabling fluid transitions between manual intervention and autonomous supervision. We developed a high-fidelity building inspection simulation that generates and streams synchronized multi-drone and environmental data for MR visualizations. We used this prototype to conduct a user study with six experienced drone pilots managing fleets of up to 15 drones. Our findings show that FleetScape supports situational awareness through layered spatial representations and clarifies control mode transitions. However, a limit to situational awareness was observed as fleet size increases, leading to different supervisory strategies. Finally, we derive design implications for supporting scalable drone fleet supervision.

cs.HC↗
arXiv2026

RoboEdit: Turning Human Manipulation Videos into Scalable Robot Experience

Collecting robot hand-object interaction data is costly and embodiment-specific, yet abundant human-object videos remain unusable for robot training. We present RoboEdit, a human-to-robot video editing suite that transforms human manipulation videos into action-consistent, physically plausible robot videos with aligned 3D hand states. To enable scalable supervision, we introduce RoboEdit-ADC, an automatic pipeline that reconstructs and retargets 3D interactions from RGB videos across embodiments. This pipeline generates RoboEdit-14M, a large-scale dataset of 174K aligned video pairs (14M frames) spanning seven robot embodiments, diverse scenes, and interaction types. The core editing engine, RoboEdit-Trans, employs cross-embodiment adaptation modules to preserve temporal coherence while adapting appearance and motion. It further integrates a 3D Robot-State Decoder to recover per-frame hand states for structured motion supervision. Experiments show that RoboEdit achieves state-of-the-art editing quality and supports downstream robot control policies in real-world manipulation tasks. Ultimately, the RoboEdit suite unlocks the vast potential of unlabeled human videos, providing scalable, high-fidelity visual and 3D motion supervision for generalizable robot learning. Project webpage: https://roboedit.github.io/

cs.RO↗
arXiv2026

Meta-Ctrl: Guaranteed Plan Generation by Decoupling Syntactic and Semantic Constraints

LLMs generate fluent plans for robots but routinely violate the syntactic and se8mantic constraints they must satisfy to execute, and existing remedies trade formal guarantees against plan quality: soft methods (affordance scoring, grounded decoding) give no guarantee, while symbolic planners (LLM+P) discard the LM's commonsense. We propose \textbf{Meta-Ctrl}, a constrained-decoding framework that guarantees the encoded constraints while preserving the base LM's plan quality. Meta-Ctrl introduces \emph{meta-tokens}---a compact vocabulary of grounded actions---enforcing syntax at the token level and semantics (preconditions, goals, ordering) at the action level, an exact factorization that cuts the memory of constrained decoding from over 107TB to under 2GB. With it, a small open-weight LM becomes competitive where it otherwise sits at the bottom of the leaderboard: on WAH-NL under the LoTa-Bench protocol it reaches the highest reported subgoal success rate, exceeding GPT-4's, with consistent gains across the Embodied Agent Interface. We further demonstrate it on a real tabletop robot, where every generated plan satisfies its preconditions and goals by construction. Project website: https://metactrlg.github.io

cs.RO↗
arXiv2026

Macro-Operator Generation and Predicate Selection for TAMP Operator Learning

Creating symbolic operators by hand is one of the main bottlenecks in deploying Task and Motion Planning systems (TAMP). Recent works show that these operators can instead be learned directly from demonstration data. Existing methods, however, typically learn each action in isolation and cannot capture the recurring multi-step structure of manipulation tasks, so the search becomes intractable on long sequential tasks. A further inefficiency arises in the symbolic state: every provided predicate is evaluated at every search node, even when it never appears in any learned operator. We present a system that addresses both problems together. Its central component is the automatic generation of macro-operators, composite actions that compress a recurring sequence of individual actions into a single planning step. Our system discovers causally linked action pairs directly from the training data, where one action produces exactly the condition that the next one requires, and turns each pair into a new operator. Alongside this, our system prunes every predicate that no learned operator references, which shrinks the symbolic state evaluated at each search node. Together, these changes shorten the effective planning horizon, and the benefit they bring grows with the length of the task. Across four TAMP domains, our method reaches up to a 4.6x planning speedup compared to the baseline method, namely Learning Operators for TAMP. More importantly, it solves a long sequential task that the baseline cannot solve. Macro-operator discovery thus not only accelerates planning but, in certain domains, determines solvability in practice.

cs.RO↗
Compare source metadata on this page
WorkPublishedSource identifierSource
Transformer-Based Autonomous Driving Models and Deployment-Oriented Compression: A Survey2026-08-282304.10891arxiv
Receding Fixed-Horizon Optimization for Near-Time-Optimal Trajectory Planning and Control2026-08-282503.11072arxiv
Differentiable Dynamics and Fast Simulation of Continuous Elastic Robotic Fish2026-08-282509.16145arxiv
OceanGym: A Benchmark Environment for Underwater Embodied Agents2026-08-282509.26536arxiv
Object Reconstruction under Occlusion with Generative Priors and Contact-induced Constraints2026-08-282512.05079arxiv
Ethical Asymmetry in Human-Robot Interaction - An Empirical Test of Sparrow's Hypothesis2026-08-282602.02745arxiv
SCALE: Self-uncertainty Conditioned Adaptive Looking and Execution for Vision-Language-Action Models2026-08-282602.04208arxiv
FlowCorrect: Efficient Interactive Correction of Generative Flow Policies for Robotic Manipulation2026-08-282602.22056arxiv
From Dialogue to Execution: Mixture-of-Agents Assisted Interactive Planning for Behavior Tree-Based Long-Horizon Robot Execution2026-08-282603.01113arxiv
Energy-Efficient Collaborative Transport of Tether-Suspended Payloads via Rotating Equilibrium2026-08-282603.06955arxiv
Scale-Plan: Scalable Language-Enabled Task Planning for Heterogeneous Multi-Robot Teams2026-08-282603.08814arxiv
Reasoning models do not yet follow their reasoning in autonomous driving: The KITScenes LongTail Dataset2026-08-282603.23607arxiv
Benefits of Low-Cost Bio-Inspiration in the Age of Overparametrization2026-08-282604.20365arxiv
SUFLECA: Scaling Up Feature Learning for CAD-to-image Alignment2026-08-282607.15058arxiv
FleetScape: A Mixed Reality Sandtable for Spatial Supervision and Control of Scalable Drone Fleets2026-08-282607.26423arxiv
RoboEdit: Turning Human Manipulation Videos into Scalable Robot Experience2026-10-172608.18948arxiv
Meta-Ctrl: Guaranteed Plan Generation by Decoupling Syntactic and Semantic Constraints2026-08-282608.22149arxiv
Macro-Operator Generation and Predicate Selection for TAMP Operator Learning2026-08-282608.23629arxiv

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