Robot
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Camera equalisation of an 11.5x drop in environmental illuminance: four imitation-learning policies on an SO-101 arm
Abstract
Real-robot benchmarks of learned visuomotor policies often record room illuminance as the lighting condition. We show that, on a typical low-cost rig, room light and image light can be almost unrelated. Four imitation-learning policies (ACT, SmolVLA, Diffusion Policy and pi0.5) were trained on the same 50 demonstrations of a pick-and-place task on an SO-101 arm. Each was run for ten rollouts at about 520 lux and again at about 45 lux, an 11.5x reduction. Success was 20/40 in normal light and 19/40 in low light: a change of -2.5 percentage points (95% CI -23 to +19), with no evidence that the policies differed in sensitivity (interaction p = 0.86). The recorded video shows why: mean image brightness was only 3% lower in low light on the front camera and 19% lower on the wrist camera. Both cameras were set to automatic exposure and white balance, which most likely absorbed the difference; exposure was not locked in this experiment. An accidental repeat of one 10-rollout block reproduced its success count (7/10) but matched the first block at only 6 of 10 placements, consistent with chance. We conclude that lux alone does not describe what a policy sees. Camera settings, and image statistics compared with the training recordings, are the quantities to report and control. Data and code are public.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13810
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Softness is something you also need! Why compliance helps control: information metrics of a passive walker with compliant ankle
2026-11-10 09:17:17Fabio Bonsignorio, Elisabetta Barletta, Sorin Dragomir, Francesco Esposito, Enrica ZereikAbstract
A great gap in adaptivity, flexibility, and dexterity still exists between intelligent robots, whether they are made of rigid or (partially) compliant components, and their naturally evolved animal and vegetal counterparts. One reason could be of `paradigm' nature. On the one hand, most robots (both rigid and soft) are still designed by following a `Cartesian' approach, with top-down control and a clear mind-body separation. Designing artificial agents on the basis of the principles that we observe in biology is not an easy task. The principles of organization of animals and plants rely on morphological computation and emergence of fit behaviors from loosely coupled networks of embodied agents. Modeling and control require radically new approaches. In this paper we briefly recall and explain some seminal mathematical results linking information metrics and dynamics of the agents, already published years ago; we integrate them with some recent methods developed by us and by the community, we highlight some brief remarks about their relevance and we hint to our current and near future work towards finding concrete and efficient ways for the modeling and control of completely or partially compliant artificial agents and for a better understanding of physical intelligence in nature. We present a technical note summarizing phase-space volume calculations for inverted-pendulum and passive-dynamic walking models, following our proposed framework. Starting from the McGeer compass gait, we introduce knee-ankle compliance, foot-rocker effects, and a Ruina-style compass gait with distributed leg inertia. Standard heel-strike maps are derived and used in the estimation of viable phase-space volumes $W(\gamma)$, based on a state-dependent Monte-Carlo and Reproducing Kernel Hilbert Spaces methods. We show how the methods based on RKHS allow deeper and more subtle analysis.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13779
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OASIS: Optimization-Aware Subgoal Inference with Safety Constraints for Autonomous Mobile Robot Navigation
2026-11-10 09:14:15Arian Hajizadeh, Javad Pourrafi Arabani, Negar Shenavar, Sana Bashiri, Seyed Majid EsmailzadehAbstract
Navigation in dynamic environments requires mobile robots to make progress toward their goals while respecting motion limits and avoiding surrounding obstacles. Learned subgoal policies can guide model predictive control beyond its local prediction horizon, but a geometrically suitable subgoal does not necessarily lead to a successfully solved control problem. This paper proposes OASIS, a hierarchical navigation framework that incorporates recent optimization outcomes into reinforcement learning-based subgoal generation. A high-level policy trained using Soft Actor--Critic generates local subgoals, while a nonlinear model predictive controller tracks them subject to motion limits, geometric collision-avoidance constraints, and embedded high-order control barrier function constraints. The outcomes of controller optimizations over each subgoal interval are summarized and provided to the policy at its next decision, without requiring additional optimization calls. The framework is evaluated in five navigation scenarios with static and moving obstacles, including dense interactions and sensing uncertainty. Comparisons with hierarchical and direct-goal control variants show that OASIS achieves the highest aggregate navigation success. Matched comparisons across three training seeds further show a consistent success advantage over the otherwise identical hierarchy without optimization-status input. The selected-policy comparison also shows fewer primary optimization failures with similar mean controller time.
Abstract (translated)
URL
https://arxiv.org/abs/2611.11996
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From Depth to Safe Depth: Uncertainty-Aware Monocular Perception for Mobile Robot Navigation
Abstract
Monocular depth foundation models make it tempting to replace depth sensors on mobile robots with a single RGB camera. We show that, used off the shelf, this is unsafe: in a simulated indoor navigation benchmark, a standard Nav2 stack driven by Depth Anything V2 (metric) collides in 63\% of 120 episodes, because the model systematically over-estimates the distance to nearby obstacles. We propose a lightweight plug-in module that turns predicted depth into \emph{safe depth}. A 0.58\,M-parameter head on top of the \emph{frozen} depth model predicts, per pixel, a log-depth correction $\mu$ and an uncertainty $\sigma$, trained with a $\beta$-weighted Gaussian negative log-likelihood. The navigation stack consumes the one-sided bound $\hat d_{\text{safe}}=d\,e^{\mu-k\sigma}$, where $k$ is set by conformal calibration on a held-out world, and uncertainty also decides which pixels may be treated as floor. The correction halves the depth error (AbsRel $0.262\rightarrow0.134$) and reduces near-range over-estimation from 59.8\% to 12.0\%. In navigation, safe depth raises the success rate from 37\% to 70\% and lowers collisions from 63\% to 3\%, close to the 2\% of ground-truth depth. A constant safety margin calibrated to the same pixel coverage reaches only 30\% success because it freezes the robot; uncertainty-aware safety wins 52 of 56 discordant paired episodes against it. Gains hold on glass, texture-less walls and dark regions.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13769
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CIALLO: Learning Shared Intent Representations for Cross-Embodiment Manipulation
2026-11-10 08:35:27Choo Sin Wai, Dake Zhong, Jingye Zhang, Zhifang Liu, Siyao Xiao, Zirong Ding, Yuhong Zhang, Kangjie Chen, Dongbin Zhang, Guorui Song, Guocun Wang, Peng Jiao, Haoqian WangAbstract
Robot demonstrations contain reusable manipulation knowledge, but transferring this knowledge across embodiments requires representations that bridge differences in control interfaces. Action reconstruction alone does not ensure that similar behaviors across robots share a representation. To address this gap, we introduce CIALLO, an action codec that aligns actions, visual changes, and observation-grounded language in a shared space of short-horizon behavioral intent. An embodiment-conditioned decoder maps this intent back to robot-specific actions, allowing a shared representation to support different control interfaces. After pretraining on eight embodiments, CIALLO adapts to a new robot from one recording of at most 10 seconds by updating only its embodiment embeddings. The calibrated codec then remains frozen while a policy learns to predict intent from observations and instructions. We instantiate this policy with Qwen3.5-4B to form Qwen-CIALLO, whose backbone requires no large-scale robot-action pretraining before policy finetuning. With 20 demonstrations per task, Qwen-CIALLO achieves 76.7\% average success across real SO-101 and single-arm Piper robots, compared with 70.0\% for fully finetuned $\pi_0$. In simulation, we additionally finetune the action decoder, achieving 97.3\% success on LIBERO and 52.7\% on RoboTwin 2.0. The LIBERO policy also achieves 80.8\% on LIBERO-Plus without further finetuning. Together, these results support a shared action codec as a transferable control interface that separates embodiment calibration from task learning.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13732
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Agentic discrete-event simulation for manufacturing system design: an open 3D simulator, a benchmark and an industrial case
2026-11-10 07:53:28Javier Pernas-\'Alvarez, Diego Crespo-PereiraAbstract
The design of manufacturing systems relies on discrete-event simulation (DES) models that experts still build in tool-specific interfaces. AI agents can now write simulation code, but every model they write from scratch is new, unverified software. We present SimuLean, an open-source 3D DES simulator on the Unity engine designed to be programmed by AI agents: its modelling semantics are verified once, in the platform, and the agent builds and studies one model through domain-level commands, in the 3D scene or headless; physically simulated durations are calibrated for fast headless studies. We evaluated it on 18 manufacturing modelling tasks, from single machines to published production lines. A frontier agent built valid models in 93 of 108 attempts (86%), in about two minutes each; on the benchmark's interface version, two frontier agents built 105 of 108 models through domain commands alone, so that each comes with a record of how it was built. Every residual failure was silent - a model that builds and runs but answers wrongly - and only comparison with reference models revealed them all. In an industrial case, one agent working alone rebuilt, verified and studied a robotised welding workshop from a published study, in SimuLean and in SimPy. Comparing them with the published, expert-built model exposed an error in it; against the corrected model, the agent-built models agree within 1%. One agent can already build complete, realistic and traceable models for the design of manufacturing systems; independent reference models are what make them trustworthy.
Abstract (translated)
URL
https://arxiv.org/abs/2611.10602
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Ego Guidance: Enhancing Robot Policy Learning with Action-Free Human Egocentric Videos
2026-11-10 07:33:35Lin Long, Tianren Ma, Zhong Chu, Di Wu, Maosong Cao, Qixiang Ye, Junbo Zhao, Shanghang ZhangAbstract
In this paper, we investigate how human egocentric videos can benefit robot policy learning without action supervision. Building on a pretrained VLM, we propose Shared Prediction on Action and Representation (SPAR), an MoT architecture in which future visual state prediction can provide independent and complementary learning signals alongside action prediction. We find that representations learned from egocentric videos improve downstream performance on out-of-distribution robot tasks, with further gains as video data scales. We also study the effects of objective choices and data curation, providing insights into how to leverage action-free videos for scalable robot learning.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13685
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DexForesight: Grounding Dexterous Policies with Implicit Spatial Foresight
2026-11-10 07:28:24Zhongxi Chen, Yi Yang, Xinyi Chen, Mingzhe Ye, Yaping Li, Zhihong Liu, Congsheng Xu, Haonan Wang, Zhijie DengAbstract
Dexterous manipulation requires rich spatial foresight over both the surrounding scene and the robot hand. Existing world-action models often acquire such predictive knowledge through explicit future prediction. We introduce DexForesight, a framework that learns spatial foresight implicitly by injecting dynamics-aware world representations into the latent space of a dexterous policy. During training, DexForesight learns an action-conditioned spatial world model from offline scene depth, kinematic hand geometry, and demonstration trajectories. The world model predicts 3D hand-point motion and visual feature changes, capturing both articulated hand dynamics and scene changes under demonstrated actions, including the motion of fingers occluded from the cameras. We then freeze the world model and align policy queries with its predictive world queries, transferring the learned spatial dynamics into policy latents and using them to condition action generation. At deployment, only the policy is retained, operating on RGB observations, language, and robot state without depth input or explicit future prediction. On 11 DexJoCo simulation tasks, DexForesight improves the overall success rate of the base policy from 49.4% to 60.5%. On three real-robot dexterous manipulation tasks, DexForesight achieves 58.3% success, compared with 50.0% for the strongest baseline. Project page this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2611.11166
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A computational framework for understanding embodied plans
2026-11-10 07:15:38Aalap D. Shah, Basil Saeed, Joshua B. Tenenbaum, Tobias Gerstenberg, Hyowon Gweon, Ilker YildirimAbstract
Reasoning about people's plans and goals based on their actions is fundamental to human social interaction and essential for cooperative, embodied artificial intelligence. Formalizing this capacity in naturalistic, embodied settings has been a longstanding challenge. Here, we present GenEA, a novel agent representation capable of reasoning over multi-step, long-horizon embodied plans. At the heart of GenEA lies a multilevel state-space that couples the coarse biomechanics of an articulated body and its interactions with nearby objects by extending the task-and-motion planning framework from robotics with simulation-based intuitive physics. By embedding this state-space in a generative model, GenEA yields a joint probability distribution over goals and embodied plans. Across several experiments spanning the qualitatively distinct domains of object manipulation and navigation, featuring diverse tasks and multiple behavioral measures, we show that GenEA, through simple transformations of this joint distribution, recapitulates how humans reason about embodied plans. Crucially, GenEA consistently outperforms strong alternative models, including plausible heuristics, standard task-and-motion planning, and a state-of-the-art vision-language model. GenEA thereby offers a new perspective on the architecture of human reasoning and social cognition, suggesting that embodied artificial intelligence systems seeking to seamlessly interact with humans should adopt GenEA-like generative models of action planning.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13669
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Fade to... Red? Scheduling Coloured Noise Exploration Supports Efficient Robot Learning with Reduced Movement Jerk
2026-11-10 06:06:58Francisco M. L\'{o}pez, Francisco CruzAbstract
Exploration in deep reinforcement learning (RL) for continuous control is commonly driven by temporally uncorrelated Gaussian (white) action noise, which leads to jerky motions with locally constrained exploration. In contrast, temporally correlated coloured noise, particularly pink noise, has been shown to support more efficient exploration in RL. Motivated by these findings, we inquire whether scheduling changes to the noise colour over the course of training can outperform white noise for continuous control robotics. We perform a systematic study of 49 linear schedules between white and red noise, together with generalised state-dependent exploration (gSDE), on four sparse-reward manipulation tasks with a Fetch arm and a Shadow Dexterous Hand. We find that the noise colour at the end of training does not affect learning efficiency or end performance, whereas fading towards red noise halves the jerk of exploration in every task. Balancing learning efficiency, end performance and smoothness, red noise emerges as the preferred strategy: it learns as efficiently as pink noise with almost 2-fold less movement jerk. We therefore recommend exploration schedulers that fade towards red noise as the default for off-policy robot learning.
Abstract (translated)
URL
https://arxiv.org/abs/2611.11868
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From Egocentric Human Video to Robot Manipulation in the Era of Embodied Intelligence
2026-11-10 05:36:17Yi Wang, Jingqiu Wu, Lei Yao, Zhaoshuo Li, Haoyu Zhu, Yirui Zhou, Huaiyuan Xu, Ying Zheng, Chuang Yang, Lap-Pui ChauAbstract
Egocentric human video (EHV) records close-range hand-object interaction (HOI) and the temporal structure of everyday activities, offering a potentially scalable source of experience for robot manipulation. Using this experience for robot control requires recovering useful supervision without recorded robot actions and accounting for differences in morphology, viewpoint, and action space. We define Ego2Robot learning as the process of learning or adapting robot manipulation policies from EHV, organized into four learning paradigms: explicit embodiment transfer, explicit action and task supervision, egocentric video representation pretraining, and latent action and dynamics modeling. The four paradigms differ in what they derive from human video: robot-format demonstrations, interpretable supervision, visual representations, or predictive models of actions and dynamics. For each paradigm, we trace what EHV information is extracted, how robot policies use it, and when the additional data is required. Besides, we review the benchmark datasets that cover EHV, robot demonstrations, and synthetic and simulated resources. This paper links EHV data's contribution to the task information preserved and the robot-specific grounding used during training or deployment. Finally, we identify challenges in physical feasibility, agent coordination, in-context adaptation, controlled evaluation, and predictive control.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13610
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Few-Shot Residual Adaptation of Pretrained Latent World Models
2026-11-10 05:04:41Jakub Suliga, Pankhuri Vanjani, Nisarga Nilavadi, Wolfram Burgard, Rudolf LioutikovAbstract
Pretrained latent world models can support visual planning and control, but their predictions may fail when physical dynamics change at deployment. We study whether such models can adapt from only a few interactions without modifying their pretrained weights or identifying the changed physical parameters. We introduce a few-shot residual adaptation approach that learns self-supervised corrections from latent prediction errors. The corrections are applied both inside the latent predictor and directly to its predicted latent state, while the pretrained world model remains frozen. We evaluate the approach in simulation and on a real robot under center-of-mass and friction shifts. These changes reduce the success of the frozen model to near zero. Using three interaction episodes for adaptation, our dual-site approach recovers 90\% success under the center-of-mass shift and 100\% under the modified friction on the real robot. Ablations show that correcting both locations outperforms single-site adaptation. Our diagnostics further suggest that much of the learned correction reduces a general prediction mismatch, while successful control also depends on shift-specific information captured during adaptation.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13592
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Control-Aware Command Generation for Asynchronous Control and Action Inference with Vision-Based Policies towards Fast Execution
2026-11-10 04:14:23Takayuki Osa, Kazuki OtaAbstract
Vision-based control policies, including vision-language-action models, have enabled robots to perform increasingly complex tasks. However, their execution speed is often limited by the high computational cost of action inference. In this work, we investigate what can be achieved at the control level without modifying the underlying policy learning process. We introduce an asynchronous inference and control framework that enables fast task execution while maintaining smooth and stable robot motions. By formulating the generation of continuous target positions from discontinuous model predictions as a tracking control problem, our method makes asynchronous inference practical for real robots. We further accelerate execution through adaptive action skipping based on the variance of predicted positions. Experiments with Action Chunking Transformer and OpenVLA-OFT on a bimanual robot demonstrate substantial speedups while preserving high task success rates.
Abstract (translated)
URL
https://arxiv.org/abs/2611.11089
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Mass Scaling Laws for Energy-Optimal Horizontal Multirotor Flight
2026-11-10 04:03:48Ayush Patnaik, Nicolas Michel, Leonard Baursfeld, Davide Scaramuzza, Xinfan LinAbstract
Multicopters are a type of aerial robot seeing extensive real-world applications but with range and endurance as critical bottlenecks. Optimization of its energy efficiency through energy-aware flight planning (e.g. cruise speed-scheduling) has hence been an important topic. The mass of the multicopter is an important factor to consider, as it has a significant impact on efficiency, and many common applications are subject to frequent mass variation due to payload change. This work derives closed-form scaling relationships between the energy-optimal steady horizontal flight behavior of a multicopter and its vehicle mass, and validates them experimentally on two multirotor platforms. Specifically, for mass-only variation, i.e., when payload changes the vehicle mass while the airframe geometry, aerodynamic characteristics, and rotor configuration remain unchanged, we show through a combination of numerical simulation, theoretical derivation, and experimental validation that (1) the energy-optimal velocity is proportional to the square root of vehicle mass; (2) the energy-optimal pitch angle is constant with respect to mass; and (3) the optimal energy per meter is proportional to mass. The analysis shows that these three relationships arise from a common mass-similarity property of the steady-state flight and rotor-aerodynamic equations. Furthermore, the obtained scaling relationships enable simple yet effective cruise-speed planning to optimize energy efficiency and extend the range and endurance of the multicopter.
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URL
https://arxiv.org/abs/2611.04748
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GEMOR-GS: Global Extrinsic-calibration-refined Mapping and Online Relocalization for 3D Gaussian-Splatting SLAM
2026-11-10 03:51:43Yuchen Yang, Ming Ding, Xinyu Wu, Hesheng WangAbstract
Photorealistic and metrically consistent world representations are a primary requirement for LiDAR--inertial--visual SLAM systems that serve robots over continuous operation. 3D Gaussian splatting makes such representations attainable by supplying an explicit, renderable, and queryable scene model. We present GEMOR-GS (Global Extrinsic-calibration-refined Mapping and Online Relocalization for 3D Gaussian-Splatting SLAM) to make the Gaussian map geometrically accurate, globally refined, and re-enterable, providing a better appearance for robot perception and decision-making. To improve the 3DGS map, the LiDAR--camera extrinsic is first estimated offline by direct-method alignment over dedicated calibration segments and reused by all following online and offline processes. Under the calibrated geometry, keyframe poses and Gaussian parameters are refined alternately under a gated differentiable-rendering and LiDAR--projection objective. The final global reconstruction is then rebuilt from the refined keyframe schema with all online constraints lifted. For the robot SLAM lifecycle, the method recovers the pose from the 3DGS map after tracking loss through an XFeat bypass map, RANSAC-PnP coarse localization, and Gaussian RGB-D warp refinement. Experiments on one self-collected and five public sequences quantify each element: the offline reconstruction wins all paired keyframes against the online map by 14.3--19.9 dB, improves PSNR by approximately 9 dB over Gaussian-LIC2, and achieves 86--100% held-out relocalization success tied to extrinsic quality. The results show that GEMOR-GS improves the geometric accuracy, appearance fidelity, and re-enterability of the 3DGS map, serving as a dependable robot world model for SLAM and downstream tasks.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13528
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ExRL: Sample-Efficient Online Adaptation of Long-Chunk Policies via Execution Length Control
2026-11-10 02:44:05Xu Luo, Jiaying Yang, Hao Wu, Junlin Xie, Zijin Hong, Ji Zhang, Lianli Gao, Jingkuan SongAbstract
Predicting long action chunks, often spanning dozens of timesteps, has become a key ingredient in increasingly capable robot policies. In this paper, we find that adapting these long-chunk policies with existing reinforcement learning (RL) methods can require many online samples due to the high dimensionality of the action chunks being optimized, making such adaptation impractical in real-world scenarios where interaction data are costly to collect. Our key insight is to shift adaptation from optimizing actions to optimizing how long to execute each predicted action chunk. This keeps the adaptation space low-dimensional while its expressivity grows with the chunk size. We thus introduce Execution at the Right Length (ExRL), a simple approach which augments the base policy with a lightweight adapter that predicts the execution length of each chunk and is trained using off-policy RL algorithms. Across simulation benchmarks and real-world robotic tasks, ExRL enables sample-efficient adaptation of a diverse range of long-chunk policies with minimal task-specific hyperparameter tuning.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13482
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Rhythmic-Semantic Co-Speech Gestures for Emotional Dialogue in Robots
2026-11-10 02:31:56Christian Arzate Cruz, Edwin C. Montiel-V\'azquez, Stefanos Gkikas, Miguel Mejia Tobar, Randy GomezAbstract
Co-speech gestures can increase engagement and improve speech understanding. Yet, most data-driven robot systems generate either rhythmic beat-like motion or semantically meaningful iconic gestures, but not both. We present an emotion-aware rhythmic-semantic co-speech gesture system that generates iconic and beat behaviors in real time from text and the robot's internal emotional state. We formalize the task of beat-iconic gesture generation using emotion and propose lightweight models that predict iconic timing and intensity, then fill the remaining spans with emotion-conditioned beats at an appropriate intensity level. We train on the BEAT2 dataset and deploy the system on the social robot Haru. In an online user study with 60 participants, our method shows stronger speech-gesture alignment and is preferred over baseline methods.
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URL
https://arxiv.org/abs/2611.13472
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Discrete Model-Based Diffusion for Combinatorial Optimization in Multi-Robot Task Allocation
2026-11-09 23:59:47Haejoon Lee, Thomas Marshall Vielmetti, Xinyi Wang, Dimitra PanagouAbstract
Decision making in multi-agent and multi-robot systems often involves discrete decision variables, leading to NP-hard combinatorial optimization problems that must be solved within seconds for near real-time implementation. Denoising diffusion has emerged as a powerful tool for such problems, but existing approaches face a trade-off: Model-Based Diffusion (MBD) incorporates diffusion in sampling-based optimization (SBO) without training but only over continuous spaces, while existing diffusion-based combinatorial solvers operate over discrete spaces but require offline training. To this end, we propose Discrete Model-Based Diffusion (Discrete MBD), a training-free SBO method that directly optimizes discrete variables by approximating categorical reverse transition distributions through Monte Carlo sampling. We establish a finite-step lower bound on the probability of finding a global minimizer and show that this bound converges to one as the number of denoising steps increases. Extensive experiments on generic integer programs and heterogeneous multi-robot coalition formation and scheduling demonstrate that Discrete MBD outperforms SBO baselines in nearly all settings while achieving solution quality competitive with that of mixed-integer programming and reinforcement learning methods at a fraction of their computational cost.
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URL
https://arxiv.org/abs/2611.13382
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AHEADance: Anticipatory Humanoid Dance from Streaming Audio
2026-11-09 23:44:46Tianhu Peng, Yukun Wang, Lingfan Bao, Yiyang Jia, Chengxu ZhouAbstract
A humanoid that dances to streamed music must commit each motion chunk before it has heard the music that chunk accompanies, and must continue from motion it has already committed. We present AHEADance, a robot-native streaming dance generator executed on a humanoid robot by a whole-body tracker. ListenAhead conditions each chunk on heard music and a frozen music model's forecasts of the coming music, aligned to their target times and weighted by reliability. The forecasts carry the timing of upcoming beats almost as well as the actual future; with them, the generator is better synchronized with the music and concentrates more motion energy at its song's beat frequency. Commit Forcing trains continuation from the model's own committed chunks, updating both the latent history and the decoded boundary of the next chunk; it reduces low activity from 83.5% to 10.3%, the level of real dance. Three music evaluators, each validated on real G1 dance, test synchronization, song and style. On a physical Unitree G1, it dances while the music plays and comes to rest when it stops, without a fall. Project page: this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2611.13370
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PneuGen: Task-Driven Agentic Co-Design of Soft Pneumatic Robots
2026-11-09 23:13:59Shaohong Zhong, Marco Pontin, Perla MaiolinoAbstract
Task-driven design of soft robots requires the consideration of factors such as geometry, morphology, material properties, and actuation, resulting in a large and complex design space that is challenging to explore. Computational optimisation methods leverage signals from simulation to iteratively improve designs to achieve task objectives. However, these methods typically demand high computational load for both optimisation and simulation. We present PneuGen, an agentic co-design framework for soft robots that leverages Large Language Model (LLM) agents to search over the soft robot design space efficiently. A designer agent proposes design programs from a parametric component library, and a separate critic agent analyses simulation diagnostics and recommends targeted revisions. Fitted module response models provide reusable physical priors relating geometry and pressure to deformation. Design programs are automatically validated and converted into finite-element simulations, enabling iterative evaluation. Across grasping tasks on primitive shapes and challenging objects, as well as shape-matching tasks that require multi-directional planar and 3D deformation, the proposed framework significantly outperforms prior computational optimisation baselines. Ablation experiments also highlight the utility of the critic agent and the module response model in facilitating efficient design. We additionally demonstrate the agent-generated soft robot designs in real-world shape-matching and grasping tasks.
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URL
https://arxiv.org/abs/2611.13344
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