arXiv_RO
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FPTAS for Polygonal Multi-Robot Motion Planning
2026-10-14 14:19:02Sayan Bandyapadhyay, William Lochet, Saket Saurabh, Jie XueAbstract
We study multi-robot motion planning in a polygonal environment with polygonal robots. The setting of the problem consists of a polygonal domain $\mathcal{W}$ with $n$ vertices and $k$ robots $R_1,\dots,R_k$ translating in $\mathcal{W}$ each of which is represented by a polygon of constant complexity. Given a source configuration $\mathbf{s}$ and a target configuration $\mathbf{t}$ of the robots in $\mathcal{W}$, the goal of the problem is to find a collision-free motion plan from $\mathbf{s}$ to $\mathbf{t}$ that is shortest with respect to some cost function. We present the first fully polynomial-time approximation schemes (FPTAS) for motion planning with any fixed number $k$ of robots, under the min-sum objective, the min-max objective, and the min-makespan objective. Here, the min-sum variant aims to minimize the total traveling distance of all robots, the min-max variant aims to minimize the maximum traveling distance of a single robot, and the min-makespan variant aims to minimize the total time cost under the assumption that all robots move with a maximum speed $1$. The algorithm directly generalizes to the problem in any fixed dimension $d$, with a polytope environment and polytope robots. Our result settles an open question of Agarwal, Halperin, Sharir, Steiger [SODA'24] and Agarwal, Holmgren, Steiger [SODA'26], who gave an FPTAS for $k=2$ unit-square robots and a bicriteria-approximation algorithm for a fixed number $k$ of unit-square or unit-disk robots, respectively. Compared to the previous results, our algorithm is substantially simpler.
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https://arxiv.org/abs/2610.20244
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Learning Navigation from Multimodal Demonstrations with Geometric Candidate Modes
2026-10-14 14:18:04Chengyang Peng, Zhihao Zhang, Upama Pant, Yiming Wang, Keith A. Redmill, Ayonga HereidAbstract
Offline navigation demonstrations are multimodal, because one observation often allows several valid actions, such as passing an obstacle on either side. Behavior cloning averages these actions, potentially leading to unsafe routes. Multimodal policies must discover the alternatives from the data, but similar states can lead to different routes whose current actions overlap. In navigation, the alternatives have a geometric cause and can be computed from one observation. We propose a hierarchical neural-network policy that computes candidate modes from local geometry and learns from demonstrations which mode to follow and how. A high-level policy selects a mode using the goal and its previous choice among a set of candidate modes, and a low-level policy turns it into an action. In simulation, the policy succeeds in over 95\% of seen and unseen test cases, compared with at most 43\% and 20\% for four offline learning baselines and 79\% and 67\% for a heuristic that uses the same modes without learning. The simulation-trained policy also transfers to a LimX Tron 1 bipedal robot.
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https://arxiv.org/abs/2610.20241
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Smart Fusion for Adaptive Visual-IMU Weighting in cuVSLAM Sparse Bundle Adjustment
2026-10-14 14:09:42Wonju Lee, Dara Molloy, Abhilash Nand Kumar, Onias Castelo, Petar Trslic, David Adley, Conor Ferguson, Hesam RabetiAbstract
In cuVSLAM, visual and inertial measurement unit (IMU) residuals are fused in sparse bundle adjustment (SBA) with a weighting fixed by pre-calibrated noise covariances, leaving no mechanism to adapt the visual-inertial balance at runtime. We introduce a scalar $\alpha\geq1$ that upscales the visual information matrix relative to the IMU preintegration constraint. Across the candidate set $\{1.0,2.0,5.0,10.0,20.0\}$, the resulting trajectories form two empirical performance regimes. The IMU-TRUST regime contains $\alpha\in\{1,2\}$ near the calibrated baseline, while the VISION-TRUST regime contains $\alpha\in\{10,20\}$. The intermediate value $\alpha=5$ is treated as a transition case. Differences within each regime are small relative to those observed between the regimes. This structure motivates reducing $\alpha$-selection to a binary decision. We train a lightweight Visual Quality Estimator (VQE), implemented as a multilayer perceptron with 914 trainable parameters. It maps a 10-dimensional visual quality feature vector assembled from statistics already maintained by the tracker to a per-SBA regime prediction. The proposed method reduces mean RMSE APE by $27.7\%$ and by $14.9\%$ under leave-one-out cross-validation (LOOCV) on EuRoC MAV and TUM-VI Room, respectively.
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URL
https://arxiv.org/abs/2610.20226
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ObjDream: Guiding Vision-Language-Action Models with Object-Level Understanding and Future State Prediction
2026-10-14 14:06:03Huailiang Ma, Yuxuan Li, Ziyi Zhao, Xinkai Wang, Mingyu Li, Xinyu Guo, Lifeng Zhu, Aiguo Song, Cewu Lu, Jianhua SunAbstract
Vision-language-action (VLA) models have emerged as a powerful paradigm for robotic manipulation, enabling robots to generate actions from visual observations and language instructions. However, most existing VLA models still operate on image-level visual representations inherited from pretrained vision-language backbones, while lacking fine-grained object understanding for manipulation. As a result, their robustness can degrade under out-of-distribution object layouts, clutter, and background variations. To address these limitations, we introduce ObjDream, an object-centric VLA framework centered on object-level understanding and future object state prediction. ObjDream uses a spatio-temporal object encoder to organize visual observations into temporal object slots, where persistent object identity and dynamic object state are represented through separate channels. These structured object representations are inserted into the token sequence of the vision-language model (VLM), where identity-guided routing modulates VLM attention to the corresponding object tokens. In addition, an action-conditioned object dynamics module predicts compact future object states relevant to manipulation during training, explicitly supervising object state evolution under robot actions without requiring future prediction at inference. Experiments on LIBERO, LIBERO-Plus, and RoboTwin 2.0 show that ObjDream improves generalization under diverse distribution shifts and scene perturbations. Real-world robot experiments further demonstrate that ObjDream transfers to physical manipulation settings with novel object arrangements and background changes.
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URL
https://arxiv.org/abs/2610.20223
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Investigating Behavior Cloning from Few Demonstrations for Autonomous Driving based on Bird's-Eye View in Simulated Cities
2026-10-14 13:47:41Eric Aislan Antonelo, Gustavo Claudio Karl Couto, Christian M\"oller, Pedro Henrique FernandesAbstract
This paper investigates the use of Behavior Cloning (BC) for autonomous driving from a bird's-eye view (BEV) perspective in simulated urban environments. BC uses supervised learning to mimic expert driving behaviors. Previous works have applied BC in the CARLA simulator but did not fully address the challenges of traffic light compliance. Our approach enhances BC by integrating a kernel density estimator to adjust training sample weights based on action density, thereby improving the learning of rare but critical actions such as stopping at red lights and accelerating at green lights, specially in scenarios of scarce number of expert demonstrations. Using BEV inputs, which provide an abstract top-down view of the driving environment, our method simplifies the policy learning process. The trained convolutional neural network (CNN) outputs steering and acceleration actions based on these BEV inputs and additional state variables. Experimental results in the CARLA simulator demonstrate that our weighted BC method significantly improves driving performance, achieving higher route completion compared to standard BC. This weighted approach proved to be crucial in learning correct driving behaviors, particularly in test environments not encountered during training, highlighting its potential for enhancing autonomous vehicle navigation.
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URL
https://arxiv.org/abs/2610.20203
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Atomic-WAM: Execution-Grounded Atomic-Motion Reasoning for World Action Models
2026-10-14 13:13:35Shijun Shi, Zhe Liu, Zhi Hou, Yinan Chen, Tao Zhang, Kai Hu, Yong Liu, Jiangning ZhangAbstract
World Action Models (WAMs) typically adapt video foundation models to robotic control through joint video and action prediction. However, task-level instructions may leave the next physical interaction underspecified, requiring semantic grounding and state-dependent motion selection. To this end, we introduce Atomic-WAM, an understand-then-execute framework that connects task reasoning to action generation through atomic-motion descriptions. Specifically, a vision-language reasoner describes the next local motion from the task instruction and visual context, guiding a WAM to predict future video and an action chunk. Furthermore, an automatic annotation pipeline curates temporally aligned, contact-enriched supervision from multi-view DROID demonstrations. To make atomic-motion predictions executable by the WAM, we post-train the reasoner with Execution-Grounded GRPO (EG-GRPO). With the WAM fixed, EG-GRPO adapts the reasoner through rewards for motion appropriateness and execution success. Finally, Atomic-WAM achieves 39.6% task success under default instructions and 31.6% under vague instructions in a real-to-sim evaluation on RoboLab. Project page: this https URL
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URL
https://arxiv.org/abs/2610.20157
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Enabling Tidying Tasks in Robotic Vacuum Cleaners via a Geometry-Induced Self-Locking Deployable Linkage
2026-10-14 13:08:58Eunseok Song, inwoong Kim, Sohee Lim, Taehyeong Kim, Yongkook Kim, Hyeontaek Lim, Hyungmin Son, Seungyong HyungAbstract
Robotic vacuum cleaners must extend their functionality within tight limits on size, cost, and durability. Deployable linkages with few actuators meet these limits, but their backdrivability makes it difficult to hold a load-bearing posture without continuous motor torque. We introduce a two-stage deployable linkage that is self-locking, in the sense used for toggle and singularity-based linkages, through its own geometry: at full deployment an internal three-link subchain approaches a collinear, near-singular configuration where the input--output transmission ratio approaches zero, so that plate loads produce a small residual back-driving torque, absorbed by drive-train friction and a lightweight magnetic detent. A single actuator performs deployment and locking, a passive chassis-mounted trigger releases the lock for retraction, and a second actuator adjusts only the deployment height. We integrate the mechanism into a commercial robotic vacuum cleaner and validate it through a 100-cycle repeatability test, a quasi-static load test in which the locked plate held 50\,N with the motor de-energized, and 140 pushing-type tidying trials on seven household objects against a commercial arm-equipped vacuum robot under a documented protocol. We derive the analytical holding condition and discuss its limits as an intermediate design point between passive compliance and articulated manipulation.
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https://arxiv.org/abs/2610.20150
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Tactile-Guided Robot Learning: From Representation Learning to Foundation Models
2026-10-14 12:54:05Kiyanoush Nazari, Luchen Li, Haoshu Fang, Nima Fazeli, Zhibin Li, Fumiya Iida, Thomas George ThuruthelAbstract
Tactile sensing is becoming central to robust, adaptive, and dexterous robotic manipulation. Beyond its traditional role in contact detection and reactive control, touch is increasingly used to learn transferable representations, condition manipulation policies, and support embodied physical reasoning. Yet existing surveys largely emphasize tactile hardware and perception, leaving the rapid shift toward learning-based tactile manipulation insufficiently synthesized. This paper reviews tactile-guided skill learning from a learning-centric perspective, organized by the functional role of touch across the learning and control pipeline. We examine tactile representation and policy learning, tactile foundation models, predictive and adaptive control, and generalization across objects, tasks, sensors, embodiments, and simulation-to-real transfer. Across these directions, we identify common design principles and remaining challenges in learning transferable physical representations, integrating semantic reasoning with contact-level control, and scaling tactile learning across heterogeneous data and hardware. Finally, we outline emerging directions toward tactile foundation and world models that can predict, reason about, and adapt physical interactions. Together, this review characterizes the transition of touch from a task-specific feedback signal toward a core modality for general-purpose embodied intelligence. An accompanying database of the reviewed literature and associated resources is available at this https URL.
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URL
https://arxiv.org/abs/2610.20134
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HM3D-M3: A Benchmark for Multi-Agent, Multi-Modal, Multi-Object Goal Navigation
2026-10-14 12:44:42Hongyu Ding, Kanyu Xu, Mingjia Chen, Zixuan Chen, Jieqi Shi, Jing Huo, Yifan Zhang, Yang GaoAbstract
Rapid progress in AI agents and robotics makes it a matter of the near future that humans are served by a team of robots. Such a team will receive several goals at once, each specified in whatever form is convenient for the human, such as a category name, a photo, or a spoken description. Existing benchmarks study multi-agent, multi-modal, and multi-object goal navigation in isolation, and a benchmark that places all three in one episode is missing. To fill this gap, we introduce HM3D-M3. A team of K agents starts at distributed locations in a photorealistic indoor scene and searches for an unordered set of M object goals, each given as an open-vocabulary category, an instance image, or an instance description. HM3D-M3 provides 1,000 validation episodes over 3,926 goals from 379 open-vocabulary categories, 100K observation-assignment training samples, and two team-level metrics, goal-level SR and makespan-normalized SPL. We further contribute two baselines. HiGA is a trained hierarchical goal assigner on top of a frozen open-vocabulary navigation policy, and LaViRA-M3 is a training-free team of VLM agents with shared mapping and progress analysis. Adapted multi-robot and zero-shot methods reach 16--27% SR, while our baselines reach 38--41% with complementary strengths and distinct failure modes. Furthermore, we deploy LaViRA-M3 on a heterogeneous three-robot team. Project page: this https URL
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URL
https://arxiv.org/abs/2610.20121
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PoLAR: Factorizing Extent and Mode in Latent Actions for Robot Policy Learning
2026-10-14 12:18:57Youngjoon Jeong, Jihwan Yu, Minsoo Jo, Junha Chun, Taesup KimAbstract
Robot actions vary in both extent and mode: how much change they produce and what kind of change they produce. A latent action space that reflects this structure may make it easier for downstream policies to map latent actions to robot actions. However, existing latent action methods typically rely on visual reconstruction from observation pairs without explicitly encouraging such structure. We introduce Polar Latent Actions with Radial structure (PoLAR), which imposes a radial-direction structure on latent actions, encouraging radius to encode transition extent and direction to retain transition mode. PoLAR uses temporal offsets between observations as a weak proxy for transition extent, encouraging transitions with larger offsets to occupy larger radii. We instantiate this structure in hyperbolic space, whose expanding volume with radius offers a natural fit for more diverse transition modes at larger extents. Across in-task and large-scale pretraining settings, PoLAR improves downstream policy performance in simulation and real-world robot experiments, outperforming latent action baselines and strong pretrained VLAs. These results suggest that structuring latent actions by extent and mode helps transfer visual pretraining to robot policy learning.
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URL
https://arxiv.org/abs/2606.21139
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FoveaVLA: Foveation Actions Replace the Wrist Camera in VLAs
2026-10-14 12:04:21Bingao Chen, Lihan Zha, C. Karen Liu, Guy TevetAbstract
We present FoveaVLA, a Vision-Language-Action (VLA) model that replaces the wrist camera with learned foveation actions. Wrist cameras provide close-up detail but restrict the view to the end-effector's surroundings. External cameras see the whole scene, but the policy receives them at low resolution, so the fine detail needed for manipulation is lost. FoveaVLA recovers this detail by predicting, alongside each action chunk, the interaction region to observe at the next policy query. The policy then receives crops of these regions, taken from the original-resolution images, together with the full views. We build FoveaVLA on $\pi_{0.5}$ in two configurations, Single and Dual, using one or two external stereo cameras with full and foveal RGB and depth inputs, and post-train both on DROID with RoboInter interaction annotations. Across four real-world task suites, FoveaVLA-Single achieves 70.8% success with one external RGB-D view, and FoveaVLA-Dual achieves 77.1%, versus 52.1% for the wrist-equipped $\pi_{0.5}$. Both match the baseline on plain pick-and-place and improve success with distractors, cloth manipulation, and far-away objects.
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https://arxiv.org/abs/2610.20064
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MorphAct: Robot Policies Morphing with the Worlds They Act In
2026-10-14 11:31:24Ziyan Liu, Jiting Liu, Weihong Zhong, Wenda Yu, Yeqiu Chen, Jason Li, Xiaojiang Peng, Shuyuan Hu, Kai Wang, Bo ZhaoAbstract
Progress toward general intelligence in the physical world requires scaling robot learning across diverse environments and interactions. However, existing robot policies use shared parameters that remain fixed across interaction contexts. When different operations favor competing parameter adjustments, fitting their mixture can leave individual operations insufficiently adapted. Our atomic-task diagnostics reveal competing update directions in both vision-language and action parameters, with source-improving updates increasing loss on other tasks. This motivates MorphAct, a contextual parameterization framework that dynamically adjusts robot policy parameters as interaction contexts change. Two coupled generators produce context-dependent low-rank updates for the vision-language and action modules. Their coupling follows the policy's computational dependency: vision-language updates shape the representations that condition action-side parameter generation. Both generators are trained jointly through the native action objective, without operation labels or expert-adapter targets. MorphAct improves performance across three VLA backbones on LIBERO and Meta-World, with gains of up to 16.30%. In real-world evaluation, it achieves gains of up to 14% over $\pi_{0.5}$. Ablations support the benefits of contextual parameter generation and representation coupling.
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https://arxiv.org/abs/2610.20019
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FoLD: Force-Informed Learning for Dexterous Articulated Object Manipulation
2026-10-14 11:14:22Haowei Shen, Tingai Li, Yumeng Liu, Wenyuan Guang, Xuanze Yang, Qing Fang, Kai Xu, Ligang Liu, Ruizhen HuAbstract
Transferring human demonstrations to dexterous robots remains challenging because differences in hand morphology and contact dynamics often cause retargeted motions to fail at producing the intended object behavior. We present \textbf{FoLD}, a framework for learning dexterous manipulation of articulated objects through explicit force guidance. FoLD compute compensatory force fields from human demonstrations together with the robot's current interaction state, yielding a force prior that promotes the demonstrated object motion. This force prior informs a residual policy that adapts retargeted hand motions to the contact requirements of the task. We evaluate FoLD on a public benchmark for articulated object manipulation, where it consistently outperforms state-of-the-art baselines across tasks and embodiments. We further validate FoLD on real dexterous robot platforms, demonstrating successful transfer of human manipulation skills to robot execution. Here is the link of our project page: this https URL.
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URL
https://arxiv.org/abs/2609.33551
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Complementary Future Token Pruning for World Action Models
2026-10-14 10:39:16Baekseung Kim, Junhoo Lee, Sohyeon Hong, Suhyun Jeon, Minkyu Kim, Seungyeon Kim, Donghoon Han, Nojun KwakAbstract
World action models (WAMs) jointly predict future visual states and robot actions, but processing dense future-frame tokens incurs substantial inference latency. We propose CF-Prune, a training-free framework for pruning future-frame tokens in pretrained WAMs. Our key insight is that future visual predictions support action generation and therefore need not preserve every visual detail required for high-fidelity video synthesis. CF-Prune combines instruction and action attention to identify task-relevant tokens during the initial denoising forward pass, even when future-frame and action representations remain highly noisy. An entropy-based phase identification mechanism further retains dense computation during precision phases to mitigate pruning-induced performance degradation. Experiments on RoboLab, RoboCasa, and real-world manipulation tasks demonstrate consistent inference speedups. At a 35% pruning ratio, CF-Prune achieves average speedups of 1.19-1.27x while improving task success rates. At a 50% pruning ratio, it achieves 1.29-1.33x average speedups on both simulation benchmarks, with overall success rates within 1.0 percentage point of the baseline.
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URL
https://arxiv.org/abs/2610.19957
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MUCO: A Mani-Unit-Structured Manipulation Framework for Compositional Long-Horizon Robot Tasks
2026-10-14 10:04:57Xiangyi Wei, Hanzhe Yao, Jingwen Shang, Zhenhong Wu, Yang Li, Changbo WangAbstract
Learning and executing long-horizon robot manipulation as complete task trajectories raises data-collection costs, lim?its capability reuse, and causes local failures to cascade into later task stages. We introduce manipulation units (MUs) as reusable operations, each defined by a local goal, an initi?ation condition, and observable success and failure criteria. MUs are learned and verified individually. During execution, consecutive MUs remain coupled through shared environ?ment dynamics. We formulate MU-Structured Manipulation (MUSM) and realize it through MU-Centric Orchestration (MUCO). Given language and vision, MUCO constructs an MU sequence, matches each MU to compatible policy models in a frozen capability registry, and dispatches the model with the highest calibrated success rate. Visual verification checks MU outcomes against success and failure criteria and governs transitions, enabling retry or fallback to another model from the current state without re-executing verified MUs. Canonical keys enable checkpoint reuse for equivalent MUs across tasks without new demonstrations or updates. We evaluate MUCO on RLBench, RoboTwin 2.0, and five real-world tasks. MUCO achieves higher complete-task success and verified progress, stronger failure containment, and lower error propagation than baselines. Results demonstrate cross-task MU reuse and het?erogeneous policy composition without joint training, shared backbones, or common action representations.
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URL
https://arxiv.org/abs/2610.19913
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Active Perception of Upper-Limb Functional Mobility through Human-Robot Handovers
2026-10-14 09:55:31Francesco Semeraro, Marta Lagomarsino, Alberto Ranavolo, Arash AjoudaniAbstract
The reintegration of people in the workplace after an upper-limb injury is often a long process. Collaborative robots and humanoids operating in shared workspaces must adapt their behavior to the physical capabilities of their human partners. Achieving this requires robots to autonomously estimate task-relevant human mobility during interaction, without relying on external clinical assessments. In this work, we formulate the estimation of upper-limb functional mobility as an active perception problem. We model task-available upper-limb functional ranges of motion through impairment coefficients that parameterize reductions of healthy joint limits, and integrate this representation into a human model used by the robot during interaction. Based on this formulation, we propose an online active perception pipeline in which the robot actively probes the user through repeated handover requests while progressively updating a Bayesian belief over the impairment coefficients. This enables the robot to continuously refine its internal model of the user's joint-level capabilities through interaction rather than passive observation. The proposed framework was evaluated through simulation studies and a proof-of-concept physical validation with a robotic platform and enacted human impairments, demonstrating that the pipeline can accurately recover the impairment coefficients during real human-robot handovers.
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URL
https://arxiv.org/abs/2610.19896
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FOMI: A Force-Feedback Manipulation Interface for Teaching Contact-Rich Manipulation
2026-10-14 09:10:24Yishu Shen, Yiming Luo, Ruilin Wang, Bowen Yang, Shuai Yang, Fangyi Xu, Yujun Shen, Fu Zhang, Boyu Zhou, Yinghao XuAbstract
Robot-free collection with handheld interfaces enables scalable acquisition of human demonstrations for policy learning. However, existing handheld interfaces often provide limited intuitive force feedback, making contact-rich demonstration collection challenging for human operators. To address this, we present FOMI, a force-feedback manipulation interface with a scissor-shaped mechanism, which transmits object interaction forces directly to the operator's hands through actuation links without force sensors. This intuitive force feedback supports real-time action adjustment during robot-free collection of contact-rich demonstrations. Around FOMI, we build a demonstration collection system that integrates sensor calibration, data acquisition, and synchronization for robot-free collection. By further mapping pose changes and gripper opening of FOMI to robot end-effector poses and gripper commands, the system also supports teleoperation. Across four contact-rich tasks, FOMI improves collection efficiency by approximately 29% and 18% over the best baselines in robot-free collection and teleoperation, respectively. Demonstrations collected with FOMI in both modes enable policies to perform challenging contact-rich manipulation tasks with high success rates.
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URL
https://arxiv.org/abs/2610.19839
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Learning from Demonstrations meets Symbolic Control - A Supervisory Control Architecture for Formal Certification of Learned Motion
2026-10-14 08:57:20Ashok Kumar, Mahathi Anand, Angela Schoellig, Abdalla SwikirAbstract
Dynamical system (DS)-based learning from demonstration generates flexible robot motions, but existing guarantees rely on certificate generation to obtain formal guarantees of safety and goal convergence. We propose a symbolic supervisory control architecture that automatically generates a fail-safe backup plan, certifying safety, reachability, and actuator feasibility directly from the robot's dynamics, independent of the learned DS. A nominal controller tracks the learned DS, while the supervisory controller monitors safety and target convergence, providing non-conservative intervention when the nominal controller violates necessary constraints. Experimental results include simulations and real robot deployment with various 2D and 3D datasets, including handwriting datasets, validating the effectiveness of our approach in providing formal guarantees with learned motion even in complex, dynamic environments.
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URL
https://arxiv.org/abs/2610.19815
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Design and Characterization of a Unified Continuum Robot Actuation Platform
2026-10-14 08:41:32Yunti Xu, Aedan Mangan, Miheer Potdar, Alex Lu, Tania K. MorimotoAbstract
Continuum robots enable dexterous access to confined spaces in applications such as minimally invasive surgery and industrial inspection. However, their actuation systems are often custom-designed for specific robot architectures and tasks, limiting reuse across publications, institutions, and applications. This paper presents a unified actuation platform that decouples actuation hardware and software from continuum robot design. The platform provides modular actuation across continuum robot classes, scales to a large number of degrees of freedom (DOFs), and integrates with conventional serial manipulators for hybrid architectures. We validate the platform on an open-sourced surgical robotic system and experimentally characterize the actuation modules, achieving sub-millimeter translational accuracy and less than $5^\circ$ rotational error over ranges of $30$~mm and $180^\circ$, respectively. We further demonstrate the platform in two surgical use cases: teleoperation of a continuum camera in a constrained environment and bimanual manipulation using $2.53$~mm concentric-tube-robot-based forceps alongside a conventional wristed instrument. All hardware designs and ROS-based software are open-sourced to facilitate reuse and further research in continuum and surgical robotics.
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URL
https://arxiv.org/abs/2610.19798
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Refine Actions If Needed: Adaptive Action Chunk Refinement for Robot Manipulation
2026-10-14 08:17:57Hanlin Liu, Zichen Zhou, Jiacheng Zhang, Liuyuan Na, Jin Jin, Guangyan Chen, Yufeng Yue, Gen Li, Fangqiang DingAbstract
Computationally lightweight manipulation policies predict executable actions with low inference latency, but prediction errors can limit task success. Iterative generative policies can achieve higher task success rates, but repeated network evaluations increase inference latency. Refining action proposals from computationally lightweight policies can combine these strengths, but the amount of refinement needed varies across states. We introduce RAIN (Refine Actions If Needed), a framework that predicts action errors at different refinement levels to decide whether and how much to refine each executable proposal, before performing any refinement. We train an ensemble to predict these errors by comparing action proposals and their refined versions with expert actions from offline demonstrations, without requiring explicit labels of refinement needs. Calibration balances uncertainty, computation, and temporal consistency. We evaluate RAIN on eight ManiSkill tasks and four real-robot tasks. In simulation, our policy achieves 66.6% success, approaching the best evaluated diffusion-policy success rate with 5.71 times lower policy latency. On the real robot, success increases from 55.0% with Action Chunking with Transformers (ACT) to 72.5%, with comparable average successful completion time. Matched controls show that dynamic routing maintains the strongest observed fixed-refinement success level with 26.6% fewer refiner evaluations. Project page: this https URL.
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URL
https://arxiv.org/abs/2610.19765
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