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新论文 · v1arXiv:2610.38176

Minimum Sufficient Communication for Independent Behaviour Tree Robot Teams

Sherry Jiang, Oliver Obst

原文摘要

We present an experimental framework for determining sufficient communication in robot teams running fixed, independent Behaviour Trees with private blackboards. Within a declared policy family, we vary message content and admission frequency and measure traffic in recipient-weighted bits per robot decision. A dense sweep identifies candidate policies for comparison with full-state communication. We retest selected candidates and the reference on fresh, paired scenarios, using one-sided simultaneous confidence bounds to assess whether success remains within five percentage points of the full-state reference. The bounds also assess whether tested cheaper policies can be ruled out as insufficient. Depending on the evidence, the framework reports an exact minimum, a positive bracket, a sufficient upper bound, or an unresolved boundary. Experiments with eight content policies across four warehouse conditions identify zero communication as the exact minimum under low contention. In the scaled condition, the no-release policy at gate $q=0.992222$ is confirmed sufficient, achieving 96.33% success at an estimated rate of 738.55 bits per robot decision, or 0.544% of full-state traffic. The positive minimum remains unresolved. These claims apply only to the declared policy family, sampled gate values and operating conditions.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2607.13429

Generalizable VLA Finetuning via Representation Anchoring and Language-Action Alignment

Dwip Dalal, Shivansh Patel, Chahit Jain, Jeonghwan Kim, Utkarsh Mishra, Yuchen Song, Alex Baratian, Hyeonjeong Ha 等 11 位作者

原文摘要

Finetuning a pretrained vision-language model (VLM) on robot demonstrations via behavior cloning (BC) has become the standard recipe for vision-language-action (VLA) policies. However, BC finetuning progressively overwrites the pretrained representations that support visual and semantic generalization. Co-training on web image-text data, a common remedy, applies language and action losses to separate observations, leaving VLAs with language-action misalignment that standard manipulation benchmarks do not expose. We propose Anchor-Align, which augments BC with two objectives: Vision-Language Anchoring distills layer-wise representations from a frozen VLM copy to prevent this drift, while Language-Action Alignment converts each action target into a discrete motion-direction label and jointly trains language and action prediction on the same robot observation. We conduct real-world evaluations across eight manipulation settings on single-arm xArm7 and bimanual YAM robots, using two VLA architectures with regression and flow-matching action heads. Across these settings, Anchor-Align consistently improves over BC on novel targets, layouts, and motion-sensitive bimanual tasks requiring coordinated control. At scale in simulation, we demonstrate consistent improvements on OOD perturbations, perceptual robustness, and long-horizon control across LIBERO-PRO, LIBERO-Plus, and CALVIN, respectively, suggesting that preserving pretrained representations and effective action learning are not fundamentally at odds. Project page: anchoralignvla.github.io

cs.RO · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.08115

RadLoc: Radar-based 3-DoF Global Localization via Fast, Robust, and Lightweight Spatial Descriptor Across Diverse Environmental Scenarios

Hogyun Kim, Jiwon Choi, Jungwoo Lee, Younggun Cho

原文摘要

While global localization using spinning radar has gained attention for its robustness to adverse weather and challenging environments, many studies have focused on individual components such as place recognition or pose estimation. In this paper, we take a holistic view of radar sensor-based global localization and present RadLoc, a fast, robust, and lightweight end-to-end pipeline from place recognition to 3-DoF pose estimation. RadLoc accelerates pre-processing using 1D CA-CFAR filtering and leverages the near-range dominance in spinning radar images to design a compact descriptor and an efficient hierarchical coarse-to-fine retrieval strategy. Moreover, coupled with phase correlation-based 3-DoF pose estimation, it forms a versatile global localization module applicable to SLAM and multi-session SLAM systems. Extensive experiments on 15 sequences across 5 datasets demonstrate that RadLoc achieves robust performance while maintaining the smallest descriptor size and fastest retrieval time among state-of-the-art approaches. The supplementary materials are available at https://sparolab.github.io/research/radloc/.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38141

SKiD-SLAM: Resource-Aware Distributed Multi-Robot LiDAR SLAM for Planetary Missions with Field Deployments at Analog Sites

Hogyun Kim, Juwon Kim, Dongjin Cho, Jiwon Choi, Geonmo Yang, Seokhwan Jeong, Jiyun Lee, Miryeong Park 等 10 位作者

原文摘要

Future planetary exploration missions require teams of robots to collaboratively map unknown environments under severe communication limits, constrained onboard computation, and no central infrastructure. In this field report, we present SKiD-SLAM, a distributed multi-robot LiDAR simultaneous localization and mapping (SLAM) system for such environments, and report its deployment across two mission concepts: cave exploration and scientific surface exploration. We survey the communication and computation budgets of actual planetary missions and select modules that fit these budgets for inter-robot place recognition, relative pose estimation, and distributed trajectory fusion. Before field deployment, we verify the assembled system on public datasets resembling planetary environments and in a simulator built on NASA JPL's Open Source Rover. We then validate SKiD-SLAM through field deployments in South Korea: a team of wheeled rovers explores the Majang lava-tube cave on Jeju Island, and quadruped robots carry out a crater-and-rock science scenario on a planetary analog terrain at the Korea Institute of Civil Engineering and Building Technology (KICT) in Yeoncheon. Across both deployments, SKiD-SLAM reliably performed distributed multi-robot LiDAR SLAM within a 250 kbps inter-robot communication budget, demonstrating its feasibility under challenging field conditions. Supplementary materials, including code and field data, are available at https://sparolab.github.io/research/skid_slam/.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.19731

What Happens to the Goal? Distinct Failures of Policy Alignment in Goal Misgeneralization

Julia Barth, Armin Sommer, Kunvar Thaman, Jannik Schilling

原文摘要

Goal misgeneralization is a common failure mode in which an agent behaves competently out of distribution but pursues a proxy that coincided with the intended goal during training instead of the goal itself. However, we still have a weak understanding of what happens to the intended goal in misgeneralizing agents and what enables them to be recovered without retraining. We study this across eight visual navigation and manipulation tasks, including robot control. While tracing where goal information remains available internally, how strongly it influences the policy, and what role it plays in action selection, we uncover that the goal is lost at one of three different depths. This depth predicts which recovery works in each task. When the goal is followed, but outcompeted by the proxy, steering the existing policy redirects it to the goal's location, raising goal reach from 16.0% to 95.3%. When it is read, but not followed, steering fails, but a lightweight new downstream wiring recovers goal reach from 31.6% to 84.3%. When it is not read, none of the methods work, as the policy does not use the goal information. The same tracing also reveals what replaces the goal: functional target substitution, where behavior follows an alternative goal, a policy shortcut, where behavior follows a learned action strategy, or cue competition between intended and proxy goal cues. Knowing where the goal is lost tells us whether an agent can be repaired or has to be retrained. Goal misgeneralization should therefore be studied and evaluated inside the agent, not only from its behavior.

cs.LG · cs.AI · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38125

Minimum-Time Escape from the Conic Field of View of a Reeds--Shepp Pursuer: the Faster Evader

Ubaldo Ruiz

原文摘要

We continue the study of minimum-time escape of an omnidirectional evader from the forward conic field of view of a Reeds--Shepp pursuer, now for a faster evader, and solve the game for every speed ratio $ν>1$ and every half-angle $0<α<π/2$: the escape time is characterized by explicit characteristics and the selection of roots of a scalar equation, and it is the value of the game in nonanticipative strategies at every state. Above the end $E$ of the evader's dispersal line on the front axis, the evader chases the reversing pursuer along a universal line fed by tributaries. The characteristic from the boundary of the usable part is an involute of a circle of radius $ν$ ending at a cusp, and two explicit thresholds decide whether it reaches the axis before the cusp and whether the jump of the escape time across it ends before the cusp or runs to it; in the latter case primaries fold back next to the barrier. The pursuer's optimal strategy is the relay of the slower case. The evader follows the tributaries and switches once to a primary, or commits in advance to a heading certified by the scalar equation of the primaries. Level-set and semi-Lagrangian computations are compared with the value.

cs.RO · math.OC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38104

Same Label, Different Interface: Beyond Action-Space Labels in Robot Learning

Arshia Sangwan, Zhongyang Zhang

原文摘要

Robot-learning studies often compare position and velocity actions as though each label identified a single experimental treatment, yet the label alone does not determine what the robot executes. Before affecting robot motion, bounded policy output passes through filtering, physical scaling, reference construction, feedback, timing, and effort limits. We study how these omitted properties affect attribution under one shared learner and training budget. Across three contact-rich Factory tasks (PegInsert, GearMesh, and NutThread), we compare five joint-space interfaces, with ten independently trained policies per condition. We first remain within the same position-feedback law and vary the executed interface: substituting only the physical action scale as the directly modified component changes sample-median evaluation success by 0.645-1.000 across tasks, while removing an exponential moving average produces task-dependent changes. We then move to a cross-law comparison and choose a velocity scale that exactly matches the position interface's local raw-torque command gain. This calibration raises the sample median on GearMesh and NutThread, with no median change on PegInsert. Yet clear residual sample-median gaps remain on PegInsert and GearMesh, while the NutThread interval includes zero. Thus, even this explicit equality leaves feedback and held-action dynamics unmatched and does not establish position-velocity trained-outcome equivalence. Action-interface comparisons should therefore report the executed pathway and state the physical quantity they match.

cs.RO · cs.LG · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.19134

Introducing M: A Modular, Modifiable Social Robot

Victor Nikhil Antony, Zhili Gong, Yoonjae Kim, Chien-Ming Huang

原文摘要

We present M, an open-source, low-cost tabletop social robot platform aimed at lowering barriers to social robotics research by making robots easier to reproduce, modify, and deploy in real-world settings. M combines a modular mechanical design, multimodal sensing, and expressive yet mechanically simple actuation architecture with a ROS2-native software package that cleanly separates perception, expression control, and data management. The platform includes a simulation environment with interface equivalence to hardware to support easy sim-to-real transfer of interaction behaviors. We demonstrate extensibility through additional sensing/actuation modules and provide example interaction templates for storytelling and two-way conversational coaching. Lastly, we report real-world use of M in interaction design and week-long in-home deployments, showing how M can serve as a practical platform for longitudinal, reproducible social robotics research.

cs.RO · cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38080

Efficacy of Monocular Depth Estimation in Lunar Environments

Michael W. Lanighan, Kenji Brameld, Seth Gee

原文摘要

Recent advances in monocular depth estimation have demonstrated highly accurate 3D reconstructions within in-distribution terrestrial domains. This study investigates the efficacy of several state-of-the-art approaches to reconstruct out-of-distribution lunar environments using the NASA Ames Polar Optical Lunar Analog Reconstruction (POLAR) stereo dataset and Apollo 17 surface imagery. By leveraging these datasets, we establish a robust analog-to-field validation pipeline that evaluates quantitative performance against ground-truth physical analogs and qualitative performance against historical field data. Our results indicate that select state-of-the-art techniques can successfully generalize to produce high-fidelity relative 3D reconstructions of the unstructured lunar surface.

cs.RO · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38047

Forward-Backward Map Matching for Airborne Magnetic Anomaly Navigation

Joseph O'Leary, Tapio Simula, Tom Kimpson, Corey Davey, Wenchao Li, Andrew D. Greentree, Allison Kealy

原文摘要

Airborne magnetic anomaly navigation provides a passive, unjammable alternative to Global Navigation Satellite Systems positioning by matching magnetometer measurements against a lithospheric magnetic anomaly map. Existing map-matching algorithms either assume a Gaussian posterior (extended Kalman filter), require Monte Carlo sampling (particle filter), or discard posterior uncertainty in favour of a single optimal path (Viterbi algorithm). Here we formulate map matching as inference on a chain-structured factor graph and solve it segment by segment using the forward-backward algorithm, which yields a discrete posterior over candidate positions and its Shannon entropy $H_t$ at time $t$. Over the \texttt{Renfrew\_395} map, $10^3$ synthetic trials yield a median position error of $32.7$ m, $91\%$ below the median inertial drift of $362$ m. On these trials and $10^3$ flight paths, the median error is $43\%$ below the extended Kalman filter and particle filter. Across these paths, a threshold on segment-level entropy detects $47\%$ of segments with errors above two map cells and $94\%$ of those above three, at a $10\%$ false-alarm rate. The foregoing results indicate that $H_t$ provides a truth-free diagnostic of map-conditioned navigation ambiguity.

eess.SP · cs.RO · physics.geo-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38040

EARS-Info: Information-Aware Event-Driven Reallocation for Multi-Robot Exploration

Kabeh Mohsenzadegan, Vahid Tavakkoli, Kyandoghere Kyamakya

原文摘要

We introduce EARS-Info, an information-aware extension of Event-Driven Ant Reallocation Search for multi-robot exploration on OpenStreetMap-derived grids. It combines marginal sensing gain, feasible relocation, return-aware routing, sensing reservations, and cached shortest paths. Across 168,000 episodes comparing 30 implementations on eight city maps, EARS-Info achieves the highest mean operational score in every evaluation panel. With eight robots and 3% communication dropout, it improves the score over EARS-Safe by 5.43%, increases weighted discovery from 88.88% to 98.46%, and reduces mean energy use by 2.62%. Score differences are positive in all eight cities ($p=0.0078125$; descriptive 95% city-bootstrap interval $[0.0159,0.0694]$), with no simulated incidents across 5,600 EARS-Info episodes. These results support the complete controller; individual component advantages remain uncertain. Generalization is limited by reused geography, one training replicate, coarse maps, and adapted comparison methods.

cs.RO · cs.AI · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38034

Learning from Relative Action Credit for Escaping Local Minima

Giovanni Minelli, Giulio Turrisi, Lorenzo Amatucci, Victor Barasuol, Claudio Semini

原文摘要

Reinforcement learning holds the promise of robots that learn manipulation skills from interaction alone, without pretrained priors or demonstration data. In practice, on-policy methods such as PPO routinely stall on long-horizon, contact-rich tasks: dense reward shaping carries the policy through stages, yet stitching them together and discovering sparsely rewarded events rarely succeeds. We trace this failure to the missing action discrimination in state-value credit assignment: a state-value critic judges progress only by episode outcomes, so when the policy converges to a partial solution all outcomes coincide, and improvement stops because no action can prove better than another. We therefore extend PPO with an action conditioned value critic which, can compare the executed action with alternatives sampled at the same state and aid the learning signal. Combined with Generalized Advantage Estimation and a bound on exploration noise, we show how the extended algorithm escapes the plateau and completes precise peg insertion within a training budget where PPO with identical exploration fails.

cs.LG · cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2409.11199

Lexicographic Descent: A Gradient-Based Approach to Optimize Totally Ordered Rulebooks via Parametric Scalarization

Matteo Penlington, Alessandro Zanardi, Lorenzo Fiaschi, Emilio Frazzoli

原文摘要

Safety-critical systems, such as autonomous vehicles, must satisfy conflicting constraints and objectives, collectively called rules, typically ordered lexicographically. Preemptive lexicographic optimization solves a sequence of problems, one per priority level. However, higher levels are optimized without considering lower ones, making the approach susceptible to suboptimal solutions. On the other hand, scalarization optimizes all levels jointly and so avoids this myopia, but a finite weighted sum permits trade-offs across priority levels, and exact scalar representations lack the regularity standard solvers require. We propose Lexicographic Descent, which combines the benefits of both: a single weighted sum whose relative weights decay at priority-dependent rates, recovering the lexicographic order in the limit. We show that Lexicographic Descent converges to constrained stationary points of the highest-priority rule, matching preemptive guarantees. Since the lower-priority weights vanish, we derive a necessary condition for the lower-priority rules to keep guiding the iterates, with the aim of avoiding suboptimal stationary points of the higher-priority rules. We show that Lexicographic Descent can exploit problem structure to reach the lexicographic optima, and outperforms the preemptive approach on an autonomous driving case study.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38027

METATRON: A Promptable Whole-Body Execution Layer for Humanoids

Chen Tessler, Yifeng Jiang, Alec Otto, Yi Shi, Umar Iqbal, Gal Chechik, Xue Bin Peng

原文摘要

Humanoid controllers often require task-specific training despite sharing the need for balance and whole-body coordination. We present Metatron, a promptable whole-body execution layer combining a generative base policy with a programmable skill layer. The base policy receives partial goals, collectively called a prompt, that can combine timed body-part targets, velocity commands, and style cues. It produces diverse whole-body motions consistent with these goals. Training combines motion capture, synthetic recovery trajectories, and retargeted scene-interaction motions. After training, the policy is frozen. Stateful programs called skills express task intent as prompts at each control step, drawing rough geometric sketches that the policy turns into coordinated movement. Skills are ordinary code, refined by coding agents that analyze motion data and fit compact rules without task-specific policy training. On the physical Unitree G1, one frozen checkpoint supports walking, recovery, teleoperated object pickup, and sitting on furniture. Comparative evaluations show higher reaching success and push robustness than the tested reference-tracking pipelines. The same recipe produces climbing and acrobatics on a simulated character. Metatron enables coding agents to turn learned motor capabilities into new skills through code, without further policy training.

cs.RO · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38000

Self-Labeled Preference Transfer for Steerable Robot Manipulation

Kaiwen Hong, Shaoting Peng, Xiangyu Zeng, Pengcheng Wang, Boyuan Tian, Haonan Chen, Yifan Zhu, Yixiao Wang 等 9 位作者

原文摘要

Vision-language-action (VLA) policies can learn to follow execution preferences, such as where to grasp an object or how to move around an obstacle, from demonstrations annotated with preference labels. Providing these annotations for every new task, however, is costly. We study whether learned preference control can transfer to new tasks whose demonstrations contain the desired behaviors but lack preference annotations. We find that a policy trained with preference annotations can follow preferences on a new task before adaptation but achieves limited task success. Fine-tuning on target demonstrations without these annotations improves task success while weakening preference control. Motivated by this finding, we introduce Self-Labeled Preference Transfer (SPT). SPT jointly trains a shared vision-language backbone for preference-conditioned action prediction and preference recognition on annotated source demonstrations. It then freezes the recognizer to preserve its cross-task preference recognition capability and uses it to self-label target demonstrations for policy fine-tuning. Across five types of execution preferences in simulation, the recognizer achieves 96.4% accuracy, and SPT reaches 84.8% preference following and 89.2% task success, compared with 59.4% and 88.0% for naive finetuning. We further extend to multiple preferences, achieving 93.8% preference following compared with 42.5% of baselines. In real-world experiments, SPT increases preference following from 52.5% to 92.5% relative to naive finetuning across two task transfers. These results show that SPT can support preference adaptation across tasks and objects while maintaining success, without additional manual preference annotations.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37981

Active Constraints for Simultaneous Guidance and Safety in Handheld Micromanipulation

Roger Ho, Robert A. MacLachlan, Cameron N. Riviere

原文摘要

In micromanipulation, erroneous hand motion limits unaided manipulation accuracy, which can affect task performance and safety in critical applications like microsurgery. Handheld robots (micromanipulators) enable motion accuracy beyond human limits by providing robotic assistance in a low-cost, compact, intuitive form factor. Particularly, active constraints are an effective form of robotic assistance, which aids the operator by restricting the robot's permissible motion based on task-specific requirements or objectives, thereby providing task guidance and enforcing safety. However, existing formulations of active constraints for handheld systems are limited, most notably because of the inability to provide guidance and enforce safety simultaneously. Implementing active constraints with optimization-based methods can address these limitations, but existing implementations are designed for free-standing robots, and neglect that handheld robots actuate relative to the operator's hand with a limited workspace. In this work, we propose an optimization-based active constraint framework for handheld micromanipulation that enables simultaneous guidance and safety enforcement. The formulation was implemented in a handheld robot and demonstrated on a medically relevant artificial task. Results show that the proposed formulation reduced safety violation rate by more than two orders of magnitude relative to baselines, while recovering the threefold accuracy loss from enforcing safety alone.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37974

FORGE: Feasible Online Reduced-Order Reference Generation and Whole-Body Execution for Agile Humanoid Locomotion

Adrian B. Ghansah, Yujin An, Jeeseop Kim, Soon-Jo Chung

原文摘要

This paper presents a two-stage learning framework for versatile and agile humanoid locomotion that couples online reduced-order model (ROM) reference generation with whole-body tracking control. In the first stage, we train a command-conditioned whole-body reference generator using single rigid body dynamics combined with full humanoid kinematics. In the second stage, the frozen ROM policy is rolled out online to generate references for training a whole-body tracking policy on the full-order dynamics model. Learning a kino-dynamically consistent reference generator in the simplified ROM space enables us to generate references over a broad command space and over multiple locomotion strategies using a single unified framework. We demonstrate the approach on a Unitree G1 humanoid robot, achieving longitudinal velocities from -2.0 m/s to 2.8 m/s, lateral velocities up to +-1.0 m/s, angular velocities up to +-3.0 rad/s, one-legged hopping, and continuous transitions across the commands, all using one unified reference policy and one whole-body tracking policy.

cs.RO · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.35040

Context-dependent Runtime Construction of Safe Regions of Attraction from Design-Time Identification for Multi-Candidate Simplex Architectures

Georg Jäger, Nils-Jonathan Friedrich, Norman Seyffer, José Cecílio, António Casimiro

原文摘要

Deploying safety-critical cyber-physical systems requires control strategies that reconcile high performance and adaptability with rigorous safety guarantees. Classical control strategies offer stability proofs but are conservative, while advanced optimization- and learning-based controllers achieve high performance but lack formal verification. Runtime Assurance (RTA) architectures bridge this gap by introducing their own (dis-)advantages. Safety filters use online optimization and introduce chattering, while Simplex permissiveness is bounded by the tightness of the certified Region of Attraction (RoA) of its High-Assurance (HA) controller. Targeting this conservatism, we present a two-stage RoA certification procedure leveraging contraction-based stability to establish a target set for Taylor-model-based reachability analysis. We then treat the closed-loop system under HA control as a switched system, enabling: (a) runtime construction of context-dependent safe RoA and (b) safe switching between HA candidates to optimize recovery and maximize the operating envelope of the uncertified High-Performance (HP) controller. We validate this framework on a robotic path-tracking task using a three-stage evaluation: Ordinary Differential Equation (ODE) simulation for theoretical verification, high-fidelity Gazebo simulation under unmodeled dynamics, and experiments on physical hardware. Results show that the Simplex architecture maintains safety under fault injection and successfully shields a behavioral-cloning-trained HP controller. Crucially, the decision module introduces negligible overhead (0.028 ms average, 4.13 ms worst-case), consuming under 10 % of the control period.

cs.RO · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2509.20333

BBoE: Leveraging Bundle of Edges for Kinodynamic Bidirectional Motion Planning

Srikrishna Bangalore Raghu, Alessandro Roncone

原文摘要

In this work, we introduce BBoE, a bidirectional, kinodynamic, sampling-based motion planner that consistently and quickly finds solutions in environments with varying obstacle clutter. The algorithm combines exploration and exploitation while relying on precomputed robot motions, resulting in efficient convergence towards the goal. Our key contributions include: i) a strategy to quickly find solutions in obstacle-rich spaces by leveraging a bundle of precomputed robot motions (i.e., edges); and ii) BBoE, a robust bidirectional kinodynamic planner that utilizes this strategy to produce fast and feasible solutions. Across four systems spanning 3D to 6D state spaces, BBoE is the only planner to achieve $\geq96\%$ success on every system while attaining the lowest planning time on three of four.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.36305

Bilinear World Models: Learning Representations with Structured Dynamics for Efficient Control

Antonio Pariente, Ignacio Boero, Nikolai Matni, Alejandro Ribeiro

原文摘要

World models jointly learn latent representations and dynamics that predict how high-dimensional observations evolve under actions. In this work, we propose a JEPA-style world model in which, rather than learning arbitrary latent dynamics, we restrict them to follow a bilinear parameterization. This structure enables efficient planning and control while shifting the modeling burden onto the encoder, encouraging richer representations that expose the controllable geometry of the system. In particular, this structured parameterization allows us to structurally enforce action recoverability, thereby preventing representation collapse by construction. Although prescribing a bilinear parametrization may appear restrictive, we show that a broad class of nonlinear dynamical systems admits a transformation under which the dynamics become bilinear. Empirically, we show across standard 2D and 3D control tasks that representations with bilinear-parameterized dynamics can be learned directly from high-dimensional observations, reducing planning time by nearly three orders of magnitude while retaining or even improving control accuracy. We also propose more demanding regimes of longer-horizon planning and real-time control, and demonstrate that our method succeeds in both, moving JEPA-style world models beyond short-horizon offline planning.

cs.RO · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37929

3D Occupancy-Language Fields from mmWave Radar

Haowen Lai, Zhiwei Zheng, Mingmin Zhao

原文摘要

Radar systems can recover scene geometry and recognize predefined categories, but these outputs do not provide a persistent spatial representation for open-ended language queries. Such queries require linking observations from different viewpoints to the same physical locations. We present TextRadar, a framework that constructs a scene-level 3D occupancy-language field from panoramic radar observations. The field combines occupancy with scale-dependent language features at surface locations, allowing the reconstructed scene to be queried with new descriptions. A single-frame radar distillation model predicts aligned ranges and language features from each radar observation. Using radar-estimated poses, we register these predictions to reconstruct the field from radar alone in a new scene. Experiments on approximately 20k synchronized observations across seven buildings demonstrate scene-level natural-language querying and prompt-free semantic visualization. Analysis of camera-to-radar concept transferability shows stronger agreement for structures, materials, and human poses than for colors and printed text.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37917

What Actually Matters? A Paired Evaluation of Inference Settings for $π_{0.5}$ on BEHAVIOR-1K

Ruben Hammele, Samira Huber, Mohammad Nazeri, Amirreza Payandeh, Alexandyr T. Card, Aaron Nguyen, Anuj Pokhrel, Daeun Song 等 10 位作者

原文摘要

Vision-Language-Action (VLA) models offer a promising paradigm for general-purpose household robotics. However, inference and data-collection strategies that have been deployed in smaller-scale settings do not necessarily generalize to large-scale, diverse task suites. To address this gap, we conduct a systematic, paired evaluation of previously proposed inference and data-collection strategies across all 100 tasks. Our evaluation uses a fine-tuned $π_{0.5}$ model based on the 2025 BEHAVIOR Challenge winner, which achieves a Q-score of 0.39 on the public test set. These results show that proposed strategies do not transfer uniformly to new environments and tasks. Project page, videos, and detailed analysis: https://rubenhammele.github.io/behavior2026

cs.RO · cs.AI · cs.CV · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2608.23927

GlanceWAM: Sparse Test-Time Imagination for World-Action Models

Linhan Wang, Zijian An, Mingyuan Zhang, Chen Dai, Yi Xu, Can Cui, Jiayan Wang, Zichong Yang 等 11 位作者

原文摘要

Video generative models provide rich physical priors for robot learning, yet existing world-action models (WAMs) face a fundamental trade-off: synchronous video generation at control rate is latency-prohibitive, while abandoning test-time visual imagination sacrifices task success. We show that visual imagination achieves both real-time inference and superior success rates when generated asynchronously off the critical path and consumed directly in latent space. We introduce GlanceWAM, which decouples imagination from control on a single shared video DiT backbone: an asynchronous proposer glances ahead on a slow clock to imagine a single lookahead frame seconds into the future in the background, while an action head decodes action chunks at control rate (48 ms) purely in latent space without blocking. Enabled by a non-interfering attention mask that isolates video representations and staleness-robust horizon training that accommodates asynchronous lookahead aging, GlanceWAM breaks the speed-success dilemma. Trained purely on demonstrations, it attains 72.2% on the 24-task RoboCasa kitchen benchmark (vs. 67.1% for synchronous Cosmos Policy) and 99.0% on LIBERO while cutting per-chunk control latency 24x relative to synchronous world-action models (48 ms on one A100). In single-arm and bimanual real-robot manipulation, it achieves higher average success than $π_{0.5}$ without any robot-data pretraining. Code is available at https://github.com/linhanwang/GlanceWAM and real-robot videos at https://linhanwang.github.io/glancewam/.

cs.CV · cs.AI · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37891

When More Planning Hurts: Learned Planners for World Models

Josef Chen

原文摘要

On PushT, our fixed action updater (1,505 parameters) solves 8 of 16 scenes reserved from our configuration selection. The comparison planners perform as follows: Selected Adam (7 tasks solved, total planning and encoding time 218.440s), adapted iCEM (2 tasks solved, total planning and encoding time 49.309s), released LePlanner (16 tasks solved, total planning and encoding time 5.652s). The total planning and encoding time for our action updater is 59.079s. Note that our action updater does not satisfy the task-cost criterion we fixed before evaluation. We also conduct another matched experiment where we only replay the first ten commands in each context and then allow the next solve 4, 8 or 12 updates (subsequent solves all get 8 updates). Among all 16 contexts (8 scene clusters), the 3 budgets get 9, 8 and 7 successes, respectively. Interestingly, the extra updates did not rescue any failing task in this experiment. All the changes in success/failure happen in one scene cluster, where we observe that the extra updates decrease the costs of the selected latent world-model trajectories for our updater, while the tasks that used to be successes become failures. The code, weights, evaluators, execution traces and cost accounting accompany the paper.

cs.RO · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37865

Echo: Scaling Visual Reinforcement Learning with Off-policy Experience

Gagan Khandate

原文摘要

Scaling visual reinforcement learning (RL) with GPU-accelerated simulation is computationally expensive due to rendering and visual-policy inference costs. Consequently, two-stage teacher--student distillation is commonly used to train visual policies, but introduces an imitation gap. In this preliminary study, we introduce \echomethod{}, \textbf{a single-stage off-policy RL framework that scales experience collection without proportionally increasing visual computation}. Through \textbf{inverse distillation}, a state-based auxiliary policy imitates the evolving visual policy and collects inexpensive, unrendered experience. Echo leverages this experience for off-policy value learning, directly improving the visual policy through a shared privileged critic. We instantiate \echomethod{} with FastTD3 and evaluate it on three visual control tasks and two dexterous manipulation tasks in Genesis. Relative to \sdpgmethod{} under the evaluated configurations, Echo reduces time to reference returns by an average of \textbf{60\% across the three locomotion tasks} and \textbf{56\% across both hand tasks}, while improving peak mean returns by \textbf{62\% and 12\%}, respectively. Both hand tasks also achieve higher mean returns at matched simulator-transition budgets. These initial results suggest that scaling off-policy experience is a promising approach to efficient direct visual RL.

cs.RO · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37818

GOFish: Full Field-of-View Gaussian Splatting and Mesh Extraction

Allen Tu, Meher Gitika Karumuri, Shifan Zhu, Hailing Wang, Yen-Liang Lin, David Held, Matthias Zwicker, Tom Goldstein

原文摘要

Robots, autonomous vehicles, and consumer 360° cameras use fisheye lenses for wide fields of view (FoVs). Simulation-ready digital twins require photorealistic rendering across the full FoV and an aligned collision mesh. However, prior fisheye Gaussian Splatting methods only rasterize up to 180° and do not produce meshes, while Gaussian surface reconstruction methods assume pinhole training images. We present GOFish, the first 3D Gaussian Splatting surface reconstruction method to train directly on fisheye images, rasterize FoVs beyond 180°, and extract an aligned mesh from the same scene. Calibrated densification recovers the surface across the entire FoV, while Calibrated Mip Filtering and Wide-Angle Appearance Modeling keep pixel blur and lens falloff out of the scene and mesh. Across the 200° FIORD and 205° FullCircle benchmarks, GOFish improves PSNR over the best prior method by 2.28 and 1.97 dB, respectively. It also outperforms fisheye rendering baselines within their own FoVs and achieves the highest mesh F-score on FIORD and the main ScanNet++ set. Together, the Gaussian scene and mesh form a LiDAR-free, simulation-ready digital twin that renders in real time, ray traces the full FoV, and provides collision geometry.

cs.CV · cs.GR · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37806

TACOS: Terrain-Aware COntact Supervision for Visual Footstep Planning

Jean-Michel Fortin, William Larrivée-Hardy, Gabriel Jeanson, Olivier Gamache, Daniele De Martini, François Pomerleau, Philippe Giguère

原文摘要

Humans navigate their environment based on their vision system, anticipating how the ground will respond before committing to a step. Self-supervised learning could provide legged robots the same ability, but the supervisory signal is typically chosen on intuition, and its effect on what the model learns is rarely examined. We present Terrain-Aware COntact Supervision (TACOS), which proposes a meaningful and consistent footstep instability score, based on the residual of a factor graph in legged state estimation. It projects the continuous score into preceding images to supervise a dense visual predictor. We propose two variants, a hard score that exploits hindsight and a causal soft score available immediately. Both metrics are compared against state-of-the-art alternatives in two independent evaluations that share no data or labels. On 1716 manually annotated contacts, both variants outperform every evaluated alternative at detecting instability and ranking its severity, improving pooled severity ranking by 28 % over the strongest baseline. On three outdoor test runs, our metric also trains the most accurate visual predictor, improving rank correlation by 58 % over the strongest alternative, and by a further 25 % when supplemented with onboard context. In a real-world experiment over a 50 m forest trajectory, the trained predictor handles non-geometric terrain properties such as mud and vegetation, while stepping around geometric obstacles like fallen branches. Code is available for reproducibility at https://github.com/norlab-ulaval/TACOS.

cs.RO · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37801

An Empirical Study of Wrist-Target Construction and Joint-Angle Limits in Humanoid Arm Retargeting

MinJun Chang

原文摘要

Motion retargeting maps a demonstrated human movement onto a robot to compute its joint motion. This study examines how the construction of the desired wrist position affects whether computed arm joint angles reach their permitted limits on a Unitree H1-2. A reconstructed target is formed from demonstrated arm directions and robot segment lengths, so its shoulder-to-wrist distance depends on the demonstrated angle between the segments even when no posture-matching preference is applied. A reach-normalized target retains that direction while rescaling the distance, using the same processed demonstrations and the same joint-angle method. Across a forward-reach sweep the reconstructed target places a joint angle at a bound in 10 of 20 repetitions and the reach-normalized target in none. Across 25 recordings from one participant the corresponding counts are 8 and 6 on the reaching arm and 14 and 5 on the supporting arm, and a second participant repeats the bricklaying and lifting comparison. In hardware replay a joint is commanded to a bound in 12 of 16 direction-matching arm-records and in none of the 16 normalized records, and within the eligible records both tracking error and executed torque at those frames exceed their values elsewhere in the same run. Cross-body reach and arm-to-arm clearance identify limits of radial adjustment. The findings support treating target construction and posture matching as separate retargeting decisions, and evaluating both alongside the wrist positions and clearance that manipulation requires.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37799

DexSuite: An All-in-One Framework for Dexterous Manipulation Across Arms, Hands, and Tasks

Anas Houssaini, Hugo He, Junming Shi, Amin Abyaneh, Ricardo Chahine, Shuo Wen, Mohamad H. Danesh, Charles-Étienne Fréchette 等 22 位作者

原文摘要

Human-level dexterity remains a central open problem in robotics, yet few simulation frameworks cover the full workflow of dexterous manipulation, from setting up a robot to collecting dexterous demonstrations and evaluating policies. We present DexSuite, an all-in-one simulation framework for dexterous manipulation built on the Genesis simulator. DexSuite provides 30 tasks and assembles its 10 arms and 12 end-effectors, from parallel-jaw grippers to dexterous hands, into more than 100 single-arm robots and thousands of bimanual pairs. It also ships a standardized environment API, integrated teleoperation, and simulated tactile sensing. Moreover, we define a benchmark of 16 task-embodiment pairs spanning diverse contact complexities and horizons, and release a dataset of 4,700 successful episodes in simulation from human teleoperation, scripted experts, and learned policies. We release DexSuite as open source and hope others will build on it. Videos and datasets are available at https://dexsuiteorg.github.io

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37666HF 社区推荐

ThinkWorld：世界想象强化动力学链推理

ThinkWorld: World Imagination Reinforces Chain-of-Dynamics Reasoning

Chaehyun Kim, Chi-Pin Huang, Jisoo Kim, Min-Hung Chen, Yu-Chiang Frank Wang, Seungryong Kim, Fu-En Yang

AI 速读 基于摘要

提出 ThinkWorld 框架，用 Chain-of-Dynamics（CoD）离散运动 token 作为跨具身的统一动作表示，并通过世界想象与强化学习利用无动作标注视频进行监督。

cs.RO · cs.AI · cs.CV

arXiv ↗PDF ↗
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Vision-Language-Action (VLA) models have made substantial progress in robotic manipulation, but their generalization remains constrained by the diversity and coverage of action-labeled robot demonstrations. Human videos provide much broader behavioral and environmental experience, while learning from them faces a fundamental trade-off between scalable data and explicit action grounding. We introduce ThinkWorld, a VLA learning framework that grounds action reasoning in world dynamics and strengthens it through reinforcement learning with world imagination. ThinkWorld represents each action thought as a Chain-of-Dynamics (CoD), a sequence of discrete motion tokens that captures the intermediate dynamics connecting the current world to a future state and serves as a common action representation across embodiments. We first initialize CoD with action-labeled demonstrations, then predict the future induced by each CoD and reinforce those whose predictions better match the observed outcome. This enables videos without action annotations to provide direct supervision from observed state transitions, allowing human and robot experience to refine the same reasoning space. Experiments in simulation and on real robots demonstrate effective human-to-robot transfer and strong generalization to unseen tasks and environments.