arXiv_RO
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TacZero: Training-Free Peg Insertion Using a General-Purpose Vision-Language Model with Tactile Feedback
2026-10-06 02:12:21Kazutoshi TanakaAbstract
Robots that autonomously determine their actions from language instructions and sensory observations could perform new contact-rich manipulation tasks without task-specific training or hand-designed rules. To perform these tasks, robots must infer how objects contact one another and move as a result, then select actions. For contact inference and action selection, prior approaches involve designing estimation models and tactile feedback control laws, or learning models for object-motion estimation, action-outcome prediction, and action selection from tactile data. Instead, we propose TacZero, which uses a pretrained general-purpose vision-language model (VLM) to interpret visual and tactile observations and select robot actions without additional tactile or manipulation training or task-specific rules for contact interpretation or action selection. TacZero provides the VLM with camera images, robot state, and three-axis tactile responses represented as numerical values or vectors overlaid on the images. From these observations and interaction history, the VLM generates commands specifying target end-effector positions and gripper opening or closing, which a low-level controller executes. In real-world cylindrical-peg insertion experiments, TacZero succeeded in 15 of 20 trials with numerical tactile input, compared with 10 of 20 without tactile input. This study provides a concrete starting point for further research on contact-rich manipulation using general-purpose VLMs and highlights challenges in pursuing this direction.
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URL
https://arxiv.org/abs/2610.07621
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Nine Trials to Recover: A Reproducible Benchmark for Repertoire-Free Soft-Robot Damage Adaptation
Abstract
We present a repertoire-free benchmark for soft-robot damage recovery under nine online trials. The protocol pairs damage masks within each morphology, retains a measured nominal fallback, and separates method development from evaluation on new bodies. A Gaussian-process expected-improvement (GP-EI) reference controller adapts actuator phases using three initialization probes and six feedback-selected rollouts. Across two disjoint 75-body cohorts, it outperforms random search, Sobol, CEM, and CMA-ES under equal budgets. GP-EI improves the worst-mask gain on 69/75 confirmation bodies and exceeds these four baselines by 0.235-0.310 mean worst-mask reward. A TuRBO-style local GP is the closest comparator; the paired confidence interval includes zero. Post-confirmation fixed-controller replay finds 5.06 voxel widths of mean recovery together with a 0.0031 increase in worst-mask p99 geometric edge strain; a strict no-added-demand deployment gate retains 48.7% of mean gain. In a frozen development stress test that physically removes 10% of occupied voxels, GP-EI improves 71/75 bodies and exceeds official BoTorch TuRBO by 0.356 mean worst-mask gain. Together, the replayable controllers, body-level inference, and frozen-cohort evaluation provide a reference for measuring the added value of learned damage priors and future adaptation methods.
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URL
https://arxiv.org/abs/2610.07616
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Learning Grasp Targeting from Point Clouds for Log Pile Clearing on a Hydraulic Crane
2026-10-06 02:01:30George Sideris, Lucas Bessai, Heshan Fernando, Elie Ayoub, Nicolas Lemieux, Inna SharfAbstract
In mill yards, log loaders clear dense piles by a sequence of bundle grasps: hundreds of logs rest in contact, and each removal changes the pile available to the next grasp. A learned policy chooses where to place and orient the grapple from unsegmented point clouds and runs on a trailer-mounted hydraulic forestry crane. The policy classifies at which observed point to grasp and predicts depth and grapple orientation there. The same network outputs support behavior cloning (BC), reinforcement learning (RL), and deployment. BC learns from successful top-of-pile demonstrations; RL explores for improvements by fine-tuning the cloned policy (BC$\to$RL) or by training from scratch. In simulation, BC clears 98 of 100 piles of 200 logs, while BC$\to$RL improves load stability. Twelve field trials compare a geometric heuristic, RL from scratch, BC, and BC$\to$RL through complete grasp-transport-deposit cycles. BC and BC$\to$RL deposit 93.8% and 88.9% of pooled inventory, against 80.4% for the heuristic. BC$\to$RL deposits logs on 83.6% of its cycles, against 79.6% for the heuristic and 65.7% for BC, while its simulated stability gain does not carry over to the crane testbed. Trained entirely in simulation and run unchanged on the crane, the learned policies clear more than the hand-filtered heuristic while observing unfiltered clouds that still contain the storage rack's rails and poles.
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URL
https://arxiv.org/abs/2610.07613
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The Robot Is Not Its Description: GaugeBench for Representation Robustness in Morphology-Aware Policies
2026-10-06 01:37:10Rahath Malladi, Arshia Sangwan, Rajesh K. Gupta, Tauhidur RahmanAbstract
A robot description does more than specify a physical mechanism: it also encodes arbitrary conventions, such as joint-axis direction, joint-angle zero, and the order and names of links and joints. Morphology-aware policies consume interfaces built from these descriptions, yet cross-embodiment evaluation typically changes the robot while keeping those conventions fixed. This leaves a simple question unanswered: does behavior survive when the robot stays fixed but its description changes? GaugeBench isolates this case by rewriting a fixed mechanism under physically equivalent conventions, verifying that its physics and policy interface are preserved, and then evaluating the same policy weights. The result is stark: three MetaMorph policies score 4030.6 on 80 familiar robots, but only 51.6 when those same robots are equivalently re-described, while 98 genuinely held-out robots score 1489.6. A new description can therefore be more damaging than a new robot. Tracing the failure reveals that axis reversal alone reproduces the collapse, joint-angle zero changes are nearly harmless, and reordering lies between them; moreover, changing joint-state and torque coordinates alone is sufficient to cause the failure, while changing description-derived features alone is not. The same phenomenon appears in ModuMorph and an unrelated PyBullet framework. Yet it is not irreversible: exact two-description transport restores the original controller, and training across equivalent axis conventions raises retained return under axis reversal from 3.6% to 80.6%. Together, these results separate mechanism robustness from representation robustness and show that cross-embodiment evaluation should test both.
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URL
https://arxiv.org/abs/2610.07597
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BiGym 2.0: Benchmarking Learned and Agent-Developed Policies for Humanoid Household Manipulation
2026-10-06 01:33:35Zexi Zhang, Zecheng Zhu, Zidong Chen, Zulkhuu Tuya, Stephen JamesAbstract
Humanoid household manipulation requires the arms to act while the body balances, steps and changes posture. We present BiGym 2.0, an adaptation of BiGym for the Unitree G1 across 20 household tasks using a unified whole-body controller for demonstration and evaluation. The suite provides 60 native human virtual-reality demonstrations per task with synchronised multi-camera views and full-body execution records. We benchmark vision-language-action fine-tuning, imitation learning, demo-driven reinforcement learning, and cold-start coding agents given the interaction budget of online reinforcement learning. With the same onboard views, proprioception and whole-body controller for every method, vision-language-action fine-tuning has the highest nine-task mean, and agent-developed programs outperform every demo-driven reinforcement learning baseline on this mean and lead on bimanual reaching. Cross-workspace stacking remains open, $\pi_{0.5}$ stays low on pick-box, and multi-object transport is hard for imitation learning, demo-driven reinforcement learning and coding agents. All environments, human demonstrations, and evaluation traces are open-sourced at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2610.07594
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Preserving Unstable Modes Through Inverse Dynamics in JEPA World Models
2026-10-06 00:08:43Leonardo F. Toso, Yann LeCun, James Anderson, Oumayma BounouAbstract
Robotic systems often exhibit unstable modes, along which small perturbations and disturbances can cause unbounded growth unless corrected through feedback. Controlling such systems from high-dimensional visual observations requires representations that preserve these modes. Joint-embedding predictive architectures (JEPAs) provide a natural framework for learning such representations and their dynamics from visual data. However, we demonstrate that next step prediction combined with anti-collapse regularization does not guarantee that controllable unstable modes are preserved: the training loss can be minimized while these modes are collapsed, making stabilization from the learned representation impossible. To address this, we augment world-model training with an action reconstruction objective (i.e., an inverse dynamics loss) that encourages control-aware representations, namely, visual representations that preserve crucial features for control. We prove that exact action reconstruction makes the encoder injective on the finite-horizon reachable subspace. Thus, the encoder cannot discard any state direction reachable by an action sequence within $H$ steps. Moreover, we show that, as $H$ grows, the dominant eigenspace of the finite-horizon controllability Gramian converges to the controllable unstable subspace. We establish our theoretical results for linear systems and demonstrate empirically that our findings extend to nonlinear visual control tasks (CartPole, Walker2D, and PointMaze), highlighting the benefits of control-aware representation learning.
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URL
https://arxiv.org/abs/2610.07540
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Task-Space Imitation Guidance for Efficient Reinforcement Learning
2026-10-05 23:49:17Salar Asayesh, Hossein Darani, Todd Cao, Evgeny Andriash, Mani RanjbarAbstract
We introduce Task-Space Imitation Guidance for Efficient Reinforcement Learning (TIGER), a reward-construction and pretraining framework for sparse-reward tabletop robotic manipulation. TIGER treats an action-chunked imitation policy not as an executable controller or action prior, but as a local task-space progress estimator: predicted action chunks are converted, using controller-aware action-to-motion mapping, into short-horizon end-effector references, and the RL agent receives dense progress rewards toward these references while the sparse environment reward remains the dominant objective. During pretraining, TIGER uses imitation-guided look-ahead signals to relax conservative value penalties for actions predicted to make task-space progress, reducing off-manifold exploration during early online RL. Across simulation and real-robot experiments, TIGER improves early sample efficiency and reduces measured safety violations while matching or improving final success rates relative to prior RL and IL-RL baselines on the evaluated tasks.
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URL
https://arxiv.org/abs/2610.07527
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ReDex: Repairing Sim-to-Real Dexterous Policies by Finger-Level Compliant Interaction
2026-10-05 23:47:05Jinzhou Li, Hadi Tabatabaee, Kelin Yu, Yuyin Sun, Cheng-Hao Kuo, Roberto Mart\'in-Mart\'in, Nima Fazeli, X. Alice Wu, Xianyi ChengAbstract
Dexterous manipulation policies trained in simulation often fail to transfer to the real world because of errors in contact timing and force regulation. Yet these policies can retain useful multi-finger coordination for task progression. We propose ReDex, a framework for adapting a simulation-trained base policy to the real world by correcting local contact failures and incorporating tactile feedback. Starting from a proprioception-only base policy, ReDex allows a human operator to physically correct contact failures at selected fingers under compliant control during real-world rollouts, while the frozen base policy continues to control the remaining fingers. These rollouts combine base policy execution, human-corrected finger motion, and fingertip force observations. We reconstruct force-informed targets from these rollouts to train a standalone force-conditioned policy via behavior cloning. This design reduces human correction effort, enables learning of contact regulation from real-world interaction, and introduces force feedback into a proprioception-only policy without tactile simulation or complex full-hand teleoperation. We evaluate ReDex on two challenging, contact-rich dexterous manipulation tasks on real hardware. Compared with sim-to-real transferred base policies, ReDex increases Object Flipping success rate from 14\% to 86\% across two objects and average Screwdriver Rotation progress from 26.0% to 95.3% across three objects.
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URL
https://arxiv.org/abs/2610.07525
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ProCut: Probabilistic Cutting Topology for Autonomous Electrosurgical Tissue Dissection
2026-10-05 23:27:23Xiao Liang, Fei Liu, Florian Richter, Genie Rubaiyat, Michael YipAbstract
Accurately modeling and tracking the deformation of soft tissue is critical for a wide range of interventional and surgical procedures. However, current methods struggle in scenarios involving topological changes, such as cutting and dissection, due to the inherent non-linearity and discontinuity introduced by explicit changes in connectivity. In this work, we present a novel, fully differentiable framework that enables robust estimation and modeling of topological changes during deformable tracking. Our method introduces a continuous, sigmoid-based formulation to smooth the otherwise discrete event of tissue cutting, making it amenable to gradient-based optimization within a differentiable Position-Based Dynamics (PBD) simulation. To account for uncertainty and improve robustness in the presence of noisy visual data, we incorporate Stein Variational Gradient Descent (SVGD) for particle-based probabilistic inference, generating multiple hypotheses for topological state estimation. Building on this foundation, we develop an autonomous dissection algorithm for thin-shell tissues that leverages topological updates to guide closed-loop cutting trajectory control. We evaluate our approach in both simulated and real-world electrosurgical environments, demonstrating significant improvements in topological estimation accuracy and dissection precision over existing methods. Our results highlight the potential of this framework to advance automation in soft-tissue surgical procedures by enabling reliable perception and control in the presence of complex structural changes.
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URL
https://arxiv.org/abs/2610.07515
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MRPilot: Supervising and Intervening LLM-Based Multi-Robot Teams through Mixed Reality
2026-10-05 22:40:55Xiaoran Yang, Xun Qian, Yang Zhan, Nathan Tran, Ziyi Liu, Qiao JinAbstract
Large language models (LLMs) let users direct heterogeneous multi-robot systems (MRS) through natural language, but make task interpretation, robot assignment, and coordination difficult to inspect and change. Based on a formative study with 12 non-expert users, we developed MRPilot, a mixed reality system organized around four stages of supervision and intervention. MRPilot represents robot-team plans and execution states as structured commitments shared across synchronized situated and overview views. Across four stages, it helps users resolve ambiguous references (Forming), review plans before execution (Reviewing), monitor distributed execution (Following), and make robot-level or team-level changes when problems arise (Repairing). In a within-subjects study with 20 participants in a virtual reality-simulated home, MRPilot reduced workload, increased situational awareness, transparency, trust, and perceived control compared with a conventional LLM-based conversational interface using the same LLM planner and robot capabilities. We provide design implications for multi-scale intervention, adaptive supervision, and calibrated reliance in LLM-based MRS.
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URL
https://arxiv.org/abs/2610.07477
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Risk-Sensitive Crowd Navigation with Adaptive Ellipsoidal Conformal Prediction
2026-10-05 22:40:35Ruihan A. Li, Ziyao Guo, Yingying LiAbstract
Safe crowd navigation under distribution shift requires uncertainty representations that capture structured human-motion prediction errors and safety objectives that account for rare but consequential failures. Existing uncertainty-aware methods typically represent prediction errors using isotropic regions, which can be either overly conservative or poorly aligned with directional motion uncertainty. We introduce a risk-aware navigation framework that uses anisotropic conformal ellipsoids to translate structured prediction uncertainty into an episode-level conditional value-at-risk (CVaR) signal that regulates the Lagrangian safety penalty. In particular, adaptive ellipsoidal conformal prediction (AECP) captures directional prediction errors and adaptively calibrates uncertainty regions under distribution shift, while the resulting CVaR-regulated navigation policy is optimized using Lagrangian proximal policy optimization. We evaluate our proposed method under both in-distribution settings and out-of-distribution (OOD) settings involving shifts in pedestrian motion patterns. Compared with state-of-the-art baselines, our method maintains competitive in-distribution performance while improving success rates by 5.68-7.44 percentage points and reducing collision rates by 5.44-6.64 percentage points across OOD settings. We further deploy the trained policy without fine-tuning on a physical robot with onboard perception and CPU-only inference, showing that the full pipeline is feasible in physical crowd navigation.
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URL
https://arxiv.org/abs/2610.07474
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SURGE: Sonar-fUsed Reconstruction and localization via image-gated Graph Estimation
2026-10-05 22:38:27Mohammed Ibrahim M, Vallabh Deogaonkar, Trung Dong, Jane Shin, Abhilash Somayajula, Xiaomin LinAbstract
Remotely operated vehicles (ROVs) are widely used to explore and inspect underwater environments such as caves, shipwrecks, and submerged infrastructure. These missions require accurate 3D understanding of the surrounding environment, which depends on both reliable vehicle localization and metric scene reconstruction. However, external positioning is often unavailable underwater, requiring small ROVs to rely primarily on onboard perception. Optic vision provides rich visual and geometric information but suffers from scale ambi- guity and trajectory drift, whereas 2D imaging sonar provides metric range but incomplete 3D geometry. Existing underwater reconstruction approaches typically address these limitations separately or assume known sensor poses, leaving localization and reconstruction disconnected. We present SURGE, a camera sonar framework that jointly estimates the ROV trajectory and target location by integrating visual and acoustic observations within a factor graph, then uses the recovered metric poses for sonar Gaussian splatting. Experiments on real underwater RGB sonar observations show that SURGE substantially improves localization consistency over conventional vision based pose estimation and produces a more compact, natively metric reconstruction than RGB Gaussian splatting baselines.
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URL
https://arxiv.org/abs/2610.07472
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Dynamics Modeling of a Multi-UAV Slung Load System Using a Discrete-Link Cable Approach
Abstract
A common assumption to simplify the problem of controlling a multi-UAV slung load system (MUSLS) is that the flexible cables can be modeled as massless rigid rods. In this work, we propose an alternative Euler-Newton derived dynamical model which uses a series of rigid links to model the flexible cables. The model is specifically designed to allow efficient simulation using Featherstone's articulated body algorithm. We perform real-world validation of this model on gentle, aggressive, and tension-engagement maneuvers and run a parameter sweep to determine the number of links, joint damping, and joint friction to achieve the greatest model fidelity. The model closely matches real-world flight data with mean load translation errors below 132 mm (5.5% of the cable length) and orientation errors below 11.4 degrees. We make the real-world flight data publicly available for the development of future cable models.
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URL
https://arxiv.org/abs/2610.07456
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Entropy-Gated Belief Coordination for Decentralized Multi-Agent Search Under Intermittent Communication
Abstract
We study decentralized multi-agent target search where homogeneous agents communicate intermittently at Poisson-distributed times. Standard unconditional belief fusion wastes communication opportunities by synchronizing agents during high-entropy exploration, when diverse independent beliefs provide better coverage than a premature consensus. We introduce \emph{entropy-gated belief coordination}, in which agents skip fusion while their collective entropy ratio exceeds a threshold~\(\theta\) and merge only during exploitation, consistent with the bifurcation structure of nonlinear opinion dynamics and the submodular structure of the per-step information gain objective. We further derive \(\Istep(\alpha,\beta)\), the expected mutual information per observation step between two agents' binary sensors, as an interpretable, communication-free measure of sensor informativeness that motivates the gating design and guides system-level analysis. Experiments across 103{,}680 trials (nine grid sizes up to \(100{\times}100\), Poisson communication timing, four target movement patterns) show that the Entropy-Gated Trust-Decay Planner (\textsc{EG-TDP}), which adds a detection-probability planner switch in exploitation mode, achieves mean belief quality \(\bar{Q}=0.300\) (mean belief mass at the true target cell, averaged across all trials and steps), a \(58.9\%\) gain over arithmetic mean and a \(37.4\%\) gain over visit-weighted fusion. On representative configurations, EG-TDP also outperforms a joint-Bayesian reference that uses all agents'~observations at every step, despite operating under random intermittent contact only.
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URL
https://arxiv.org/abs/2610.07432
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RACER: Residual-Adaptive Closed-Loop Estimation for Sampling-Based Planning in Wheeled-Quadruped Racing
2026-10-05 21:17:46Yuxiang Liu, Marla Eisman, Lizhi Yang, Aaron Ames, Francesco BorrelliAbstract
We present RACER, a hierarchical control framework for wheel-based quadruped racing that combines an MPPI planner with a learned residual dynamics model and a low-level RL velocity tracker. The planner augments a nominal unicycle kinematic model with a neural residual term to capture the closed-loop tracking behavior of the RL policy. To train this residual model under limited real-world data, we propose Low-Rank Residual Adaptation (LoRRA), a two-stage approach that pre-trains on large-scale simulation data for broad coverage and then fine-tunes on a small real-world dataset with a low-rank constraint. In simulation, we empirically validate our engineering choices by showing (A) Residual dynamics improve the overall performance of our pipeline by capturing the tracking error of RL velocity tracker at high-speed cornering. (B) Residual dynamics trained with both source-domain and target-domain data gives racing performance significantly better than the residual dynamics trained with only target-domain data. (C) Low-rank constraint at target-domain adaptation gives higher success rates and higher performance than full-tune and from-scratch when domain gap in ground coefficient or joint gain increases.
Abstract (translated)
URL
https://arxiv.org/abs/2610.07409
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An Autonomous, 3D Printed, Waterjet-Powered, Open-Source Robotic Trimaran for Environmental Inspection and Monitoring
2026-10-05 21:09:18Reuben O'Brien, Martin Lambrechtse-Reid, Minas LiarokapisAbstract
Versatile, autonomous robotic boats can offer excellent environmental inspection and monitoring solutions for remote, dangerous, hard to reach, or access protected water bodies. This paper introduces such a platform in the form of an autonomous, cost-effective, waterjet-powered robotic trimaran. Motivated by the need for an efficient aquatic monitoring, particularly in Aotearoa - New Zealand's diverse environments, the trimaran provides an efficient, low-cost, and easy to replicate alternative to resource-intensive research vessels. The proposed platform, costs $600-1,500 USD to develop (depending on the sensing system configuration), weighs under 5 kg, and excels in bathymetry and water quality testing. The trimaran can reach speeds of up to 2 m/s offering obstacle avoidance of natural features, such as rocks. Utilizing off-the-shelf components and 3D printing technology, the proposed platform offers excellent reproducibility and robustness while operating in shallow waters with its jet propulsion system. The paper presents in detail the design characteristics, the sensing system employed, testing results focusing on bathymetry, and highlights the ability of vessel and the potential for future research and data collection.
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URL
https://arxiv.org/abs/2610.07398
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What the Elevation Map Cannot See: Semantic-Aware Locomotion and Execution-Aware Navigation for Humanoid Robot
2026-10-05 21:08:23Shunyu Yao, Songyang Liu, Dinghao Chen, Yuanyuan Lei, Shuai LiAbstract
Navigation for humanoid robots is critical, yet large-scale evaluation on physical hardware is often impractical due to cost and safety concerns, making simulation benchmarks essential. Existing VLN benchmarks achieve physically executable navigation, but still assume (1) all hazards are observable from elevation maps; (2) realized motions closely match desired motions. In real environments, however, fallen bottles may be ambiguous in elevation maps, while phones and water spills may be difficult to differentiate; hazard avoidance by the locomotion policy can cause the robot's actual trajectory to deviate from the path intended by the VLN policy. Such command-execution mismatch can accumulate and lead the robot toward unintended locations. To expose these failure modes, we introduce a benchmark that models both elevation-subtle hazards and execution deviations, together with a closed-loop VLN + locomotion control framework that continuously realigns high-level navigation with the robot's actual state. We evaluate navigation in simulation and further validate the locomotion policy on a physical Unitree G1 humanoid robot. Results show that semantic input reduces contact with hazards poorly represented in elevation maps, while anti-deviation improves navigation success. These findings highlight the need to evaluate humanoid navigation jointly in terms of route completion and hazard avoidance.
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URL
https://arxiv.org/abs/2610.07396
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AeroBuoy: A Drone Deployable, 3D Printed, Autonomous Robotic Buoy for Environmental Inspection in Remote and Hazardous River Systems
2026-10-05 21:04:54Reuben O'Brien, Angus Lynch, Minas LiarokapisAbstract
Monitoring of waterways such as remote and hazardous rivers and streams is important so as to assess the impact of external factors including construction runoff or climate change. Versatile, autonomous robotic boats can offer excellent environmental inspection and monitoring solutions for remote, dangerous, or access protected water bodies but they have several shortcomings in terms of maneuverability. This paper proposes an environmental inspection system consisting of an autonomous data collection buoy which is designed to be deployed to inaccessible river systems using a drone. The system can perform a drop off and pickup of the buoy depending on the requirements of a particular location and monitoring task. Utilising the natural flow of the river the buoy autonomously steers down, using GPS and magnetometers so as to maintain the desired trajectory. The buoy is capable of measuring water temperature but it can also be equipped with a range of sensors such as water oxygen meter, sonar for river bed inspection, or turbidity for water clarity. This paper describes the system design, presents an analysis of the self-righting capabilities of the buoy, and shows a full system demonstration at the Årewa River in Auckland, New Zealand.
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URL
https://arxiv.org/abs/2610.07390
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Toward Trustworthy Physical AI for Human Interaction
2026-10-05 20:53:59Niccol\`o Pagliarani, Maximilian St\"olzle, Cosima du Pasquier, Jeff Lui, Andrew Sabelhaus, Cecilia Laschi, Gentiane Venture, Daniela Rus, Matteo CianchettiAbstract
Robots are entering human spaces faster than we can establish when they deserve trust. We propose a framework for trustworthy physical AI that integrates Safety, Behavioral Intelligibility, and Perceptual Alignment across embodiment, control, cognition, and design. Trustworthiness emerges from aligning physical capabilities, observable behavior, and expectations people form during interaction.
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URL
https://arxiv.org/abs/2610.07382
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Trajectory Planning without Trajectory Data: A Manifold-Guided Approach
2026-10-05 20:51:54Silong Yong, Anji Liu, Cunxi Dai, Carl Busart, Guanya Shi, Yilun Du, Katia Sycara, Yaqi XieAbstract
A common way for trajectory planning is to leverage generative models trained on large collections of expert trajectories. At inference time, the model generates executable trajectories by conditioning on task goal constraints. However, trajectory-based methods rely on costly supervision, scale poorly with sequence length, and often generalize poorly to unseen constraints such as novel start-goal pairs. We propose an alternative to learn the underlying state-space manifold and use the geometry of the manifold for trajectory planning. This approach requires only state observations and enables generalization to unseen constraints by con- structing trajectories on the learned manifold of the state space. Experiments on Maze2D and robotic motion-planning benchmarks show that Ariadne constructs feasible paths from state-only supervision and generalizes to unseen start-goal combinations. On high-dimensional dual-arm planning, it remains competitive with trajectory-supervised and classical planners, while requiring no trajectory data for training.
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URL
https://arxiv.org/abs/2610.08863
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