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Building Comparative Motivation Profiles with Symmetric Belief Editing
2026-11-10 17:34:17David Vella Zarb, Rustem Turtayev, Taywon Min, Jinghua Ou, Shi FengAbstract
When a model behaves in unexpected ways, we want to know why, but its intent cannot be observed directly, and its self-reports may be rationalizations. We introduce comparative motivation profiles, which treat intent as propensity: we edit the model's beliefs about facts that a hypothesized motivation would depend on and measure how its behavior changes. To keep artifacts of the editing method from driving the result, we edit two minimally different hypotheses in the same way, with matched amplify and suppress edits; we call this symmetric belief editing. We apply it to alignment faking in four open-weight model organisms, contrasting scheming, which tracks the training consequences of compliance, with performative misalignment, which tracks what researchers expect to observe, and edit beliefs with synthetic document fine-tuning (SDF), activation steering, and prompting. Under SDF, amplifying performative beliefs raises alignment faking on every model, while scheming edits move it less or shift compliance in the monitored and unmonitored conditions alike. Steering on model organisms changes compliance along the expectation-tracking direction but mainly the reasoning along the consequence-tracking direction, and prompting agrees. In these organisms, alignment faking follows what the models infer their evaluators expect, which puts it closer to sycophancy than to deceptive alignment.
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URL
https://arxiv.org/abs/2606.08243
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Gaussian Process-Augmented Model Predictive Contouring Control for 6-DoF Trajectory Tracking with a Holonomic AUV
2026-11-10 17:30:20David D\"orner, Giancarlo Troni, Ignacio Torroba, Ivan SteniusAbstract
Small, holonomic Autonomous Underwater Vehicles (AUVs) enable close-range inspection of underwater structures from arbitrary viewpoints, requiring accurate position and attitude tracking under state and input constraints. Model Predictive Contouring Control (MPCC) naturally handles these requirements, but relies on an accurate dynamics model, which is difficult to obtain underwater. In this paper, we present a nonlinear MPCC in all six Degrees of Freedom (DoF) for a holonomic AUV and alleviate the inaccuracies in the dynamics by augmenting the nominal model employed with residual dynamics learned via Gaussian Process (GP) regression. The learned residual is embedded in the optimal control problem through a zero-order robust optimization scheme, which keeps the controller real-time capable at 25 Hz. A simulation ablation shows that residual selection is essential when designing the controller, and simulation and in-water experiments with the Multimodality, Observing, Low-cost, Agile (MOLA) AUV validate the resulting controllers against a nominal MPCC and a field proven Proportional-Integral-Derivative (PID) controller.
Abstract (translated)
URL
https://arxiv.org/abs/2611.14373
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MMDM: Measuring Massive Multitask Decision Making
2026-11-10 16:40:50Junhao Shen, Qing Zong, Yue Guo, Zhanqiu Zhang, Yiwen Guo, Hong ChengAbstract
System-1 Decision Models directly make structured probabilistic decisions over query-specified options given a state in a single forward pass, without autoregressively generating reasoning trajectories or textual responses. Recent evaluations cover diverse tasks but often rely on existing benchmarks, where prior exposure to test material can obscure generalization. Moreover, real-world decision making often involves long contexts, large option spaces, and complex logical dependencies, challenges that existing benchmarks do not adequately capture together. Thus, we introduce MMDM (Massive Multitask Decision Making), a benchmark of 5,448 high-quality questions across 16 task categories, combining newly constructed tasks with source-grounded recompositions and rewrites for single-select, multi-select, and soft-label decision making in fresh contexts. It covers realistic scenarios such as agent state tracking, tool and skill selection, preference judgments, and rubric scoring, with option spaces ranging from 2 to 1,024 and inputs extending to 64,817 tokens. With MMDM, we conduct a comprehensive quantitative evaluation of 28 decision models, showing that many perform well on common knowledge yet struggle with interdependent options, constrained decisions, and multi-select tasks. For example, Perplexity Decider-V1.2 reaches 78.75% Hard Single-select Accuracy on world knowledge, but only 53.95% on option logic and 47.95% on constraint planning, while the best Hard Multi-select Accuracy across all models is 36.81%. These results highlight joint reasoning, constraint-aware selection, and complete multi-select recovery as key priorities for decision model development, with MMDM providing a testbed for measuring this progress.
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URL
https://arxiv.org/abs/2611.14309
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HaiRuo: Forecasting Ocean Eddies as Fields and Objects with a Satellite-Altimetry Foundation Model
2026-11-10 14:24:07Xukun Qin, Haochen Sun, Yuyang Tian, Ge ChenAbstract
Mesoscale eddies carry most of the ocean's kinetic energy and move heat, salt and nutrients across basins, yet where an eddy will be two weeks ahead is still read off ocean models with post-hoc tracking heuristics. We introduce HaiRuo, which forecasts eddies as fields and as objects at once: from 30 days of satellite sea-level maps, one forward pass returns 15 days of sea level and geostrophic velocity, daily eddy masks and the trajectory of every eddy alive today. HaiRuo pairs two components with a clear division of labour: (1) HaiRuo-1B, an open-weight 1.05-billion-parameter foundation model pretrained without labels on 24 years of global satellite-altimetry observations, which supplies the 15-day physical fields and masks (+0.14 field skill and +1.1 mIoU points under the same decoder) and further lowers 15-day centre error by 1.2 km frozen (1.35 km over training from scratch); and (2) XuanJi, a Lagrangian object decoder that samples the evolving forecast in each eddy's co-moving polar frame and refines the 15-day path across three look-ahead rounds, removing 2.5 km on HaiRuo-1B (2.9 km with joint fine-tuning) and 2.5-3.3 km via its object attention across six published backbones. Under one pre-registered global evaluation over the whole ocean, HaiRuo improves on fourteen methods on every endpoint (25.2 vs. 30.6 km 15-day centre error, -18%), holding its margin outside the labelled regions and through both phases of the El Nino-Southern Oscillation (ENSO), while reproducing westward drift, 50-60 deg S eastward reversal and poleward cyclone-anticyclone deflection. Over ten days through one fixed tracker, HaiRuo's forecast field cuts the eddy-centre error of WenHai and XiHe by 37-43% (-2.8 km vs. day 0 while they degrade +4.3-8.7 km), and its object trajectories cut pipeline error by 77-78% globally. Open-weight checkpoints, code and project page: this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2611.14136
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Looking One Step Ahead: An Anticipation-guided Agent for Object State Readout in Videos
2026-11-10 14:05:39Yuanhe Tian, Yan SongAbstract
Understanding the progress of an activity requires tracking how the objects involved change over time. Object state readout provides this information by describing a target object's states and transitions across video frames. Although multimodal large language models (MLLMs) generate detailed visual descriptions, these descriptions may leave the attributes needed to distinguish successive states insufficiently specified. In this paper, we propose look-ahead state readout (LASER), an anticipation-guided agent for object state readout that uses possible next-step changes to determine which attributes to inspect in the present. Given temporally ordered frames and a target object, LASER converts anticipated changes into targeted visual questions and inspects the current frame to establish attribute values. It then checks state readouts and forecasts against independent observations of subsequent frames, refining a state record used to generate the final description. Reusable textual observation skills guide this process and are refined by tracing discrepancies with reference captions to the responsible instructions. Candidate revisions are selected on separate videos based on improved caption quality without increased contradictions with reference facts. Model parameters remain frozen throughout adaptation and inference, and the revised skills remain fixed during evaluation. Experiment results and further analysis on OSCaR and PLM RCap show consistent improvements of LASER over strong baselines and existing approaches, which demonstrates the effectiveness of our approach.
Abstract (translated)
URL
https://arxiv.org/abs/2611.14113
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ProofCoder: Continuous Reasoning for Automated Software Verification
2026-11-10 13:59:49Martim Afonso, In\^es Lynce, Maurizio Morisio, Jo\~ao F. FerreiraAbstract
Formal verification using proof assistants provides strong correctness guarantees for software, but constructing proofs remains labor-intensive and requires significant expertise. Recent approaches based on large language models (LLMs) have shown promise in automating proof synthesis. However, these approaches typically constrain the model to a rigid tactic-generation loop, in which the surrounding system controls premise retrieval, proof-state management, and backtracking. We introduce ProofCoder, an agentic proof-synthesis system for Rocq that approaches theorem proving from a coding-agent perspective. Rather than generating tactics within a fixed loop, ProofCoder reasons continuously throughout proof search and uses proof-assistant feedback to decide when to gather additional information, inspect proof states, revise previously generated proof fragments, or backtrack to an earlier point in the proof. This allows it to iteratively construct, repair, and refine proofs, at either tactic-level or whole-proof granularity. We evaluate ProofCoder on 280 challenging verification tasks from the AutoRocq Benchmark. On the best-performing configurations, ProofCoder increases proof success from 47% to 60% over the state-of-theart AutoRocq system, corresponding to a 27.7% relative improvement at approximately half the cost. Our ablations identify continuous reasoning as a key design choice: without it, the agent solves fewer lemmas at substantially higher cost, backtracks four to six times more often, and more frequently discards its own revisions. Whole-proof and tactic-level generation achieve similar success rates but follow distinct search and repair strategies. These results suggest that effective automated software verification requires more than improved tactic selection: continuous reasoning and feedback-guided proof revision are critical ingredients for robust proof synthesis.
Abstract (translated)
URL
https://arxiv.org/abs/2611.14106
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DexSeed: Expanding Human Hand-Object Interactions into Robot Demonstrations for Dexterous Policy Learning
2026-11-10 13:52:18Huaijin Pi, Zehong Shen, Yong Wang, Kechun Xu, Changpeng Wang, Dingkun Wei, Xueyang Wang, Wenjia Wang, Xing Zhu, Yujun Shen, Taku KomuraAbstract
We study how to generate diverse, physically executable robot demonstrations for dexterous vision-language-action (VLA) learning. Existing demonstration-generation methods typically rely on costly teleoperated examples. Meanwhile, human hand--object datasets provide a reusable source, but existing transfer methods mainly reproduce given interactions. Turning these interactions into VLA training data further requires coverage of different objects and manipulation locations, together with executable arm and finger actions, and visual observations. To achieve this, we present DexSeed, a framework for low-cost generation of dexterous robot data from human hand--object interactions. Our key idea is to expand human sequences while preserving their grasps and realizing the resulting motions under arm-tracking dynamics in physics simulation. Specifically, we organize source sequences by grasp mode with shared language descriptions. InteractionMesh-based retargeting and arm trajectory augmentation adapt these sequences to the robot hand, different object geometries, and pick-and-place locations while preserving local hand--object relationships. An RL finger-control policy produces executable rollouts, while the arm tracks precomputed trajectories. Rendered observations and recorded actions from successful rollouts are paired with grasp descriptions to fine-tune a VLA. Experiments show improved contact fidelity in retargeting and more reliable whole-arm execution than the evaluated baselines. Simulation evaluation and real-world deployment further demonstrate that a VLA fine-tuned solely on the generated demonstrations can execute language-specified grasps. Project page: this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2611.14092
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RedirectMimic: Local Motion Content Redaction for Humanoid Whole-Body Control
2026-11-10 13:15:54Zepeng Wang, Xiaochuan ShiAbstract
Humanoid motion interfaces should accommodate application-specific movement constraints while retaining useful reference behavior. Local content redaction requires a controller to replace selected movements even when it continues to receive the original reference. Removing the tracking incentive for those movements leaves their replacement unspecified. We introduce RedirectMimic, which separates the reference observed by the actor from the target used to train its execution. A rule-generated edited reference guides tracking rewards during proximal policy optimization, while the deployed observation and action interfaces remain unchanged. We evaluate pose- and speed-defined arm editing in Unitree G1 simulation using a tracker pretrained on 7,509 motion clips. On the overhead-arm task, RedirectMimic reduces core-window content occurrence to 2.7%, compared with 58.9% for reward zeroing and 32.8% for anti-tracking. It achieves 94.2% core-window joint success, combining content suppression, fall-free execution, and local retention. On the speed-defined task, fast outward arm movements occurred at similarly low rates under all three local editing methods. Across both tasks, RedirectMimic achieved the lowest replacement error among these methods and tracked unedited motions with accuracy comparable to that of the pretrained controller. Project page: this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2611.14049
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Optimization-Based Path Planning for Industrial Manipulators: Experimental Validation in Additive Manufacturing
2026-11-10 12:25:31Shiva Ganesh Murali, Valentin Bhend, Deepak Ingole, Gabriele Fadini, Alisa RupenyanAbstract
Robotic additive manufacturing requires joint paths that reconcile robot feasibility with process-specific geometry. We develop an offline optimization-based path planner that combines joint-path regularity and self-collision penalties with process costs and constraints, so that a new process enters through its own reference, residuals, and tolerances. Full-path and boundary-coupled chunked optimization handle references of different lengths. Iterative Weight Tuning (IWT) determines the cost weights from the gaps between Key Performance Indicators (KPIs) and their targets. In carbon-fiber winding and robotic 3D printing, the planned paths meet the geometric tolerances and all KPI targets, and both paths are executed on a UR10e, without extrusion for printing, with joint-level tracking recorded during execution.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13983
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Delay-Certified Readiness Barriers for Overactuated Multirotor Allocation Without Lipschitz Constants
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Overactuated multirotors can produce the same wrench with many rotor-speed combinations, some of which leave the vehicle unable to change its wrench quickly, e.g., to reject a gust. Readiness barriers keep this ability, the readiness, above a floor, but only in continuous time; with sampled commands and varying speed-controller delays, the available robustness bound covers less than one millisecond. We remove this limit by exploiting the structure of the problem: each rotor responds monotonically and in closed form to its own torque, so the speeds reachable under uncertain switching times lie in a cheaply computed box, and the lowest readiness over that box sits at a corner known in advance. This yields a Lipschitz-free test that a command keeps the readiness above its floor until the next sample, plus a fallback torque that keeps the vehicle on a certified plan. In hexarotor simulations with 2--40~ms delays and $\pm2$~ms jitter, the filter never violated the floor. At $10\pm2$~ms, it matched a delay-compensating predictor within the design envelope and cost 2--30\% more tracking error beyond it, where the predictor alone violated the floor in 2 to 16 of 16 missions. A tuned robust predictor was also safe with synchronous switching, but not with per-rotor jitter.
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URL
https://arxiv.org/abs/2611.03490
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ReACT: Reflection-Consistent Modulation and Cross-Load Consistency for Humanoid Velocity Tracking Under Asymmetric Loads
2026-11-10 11:18:34Xianghui Wang, Aoran Fu, Liyou Luo, Shanze Wang, Xiaoyu Shen, Dan Zhang, Wei ZhangAbstract
In recent years, humanoid loco-manipulation has progressed to whole-body grasping, lifting, and transport. A basic requirement underlying these tasks is carrying the object to its destination, which depends on the locomotion controller accurately executing velocity commands from the navigation policy. However, carrying an object in one hand introduces a persistent asymmetric upper-body load that can degrade velocity tracking. Persistent tracking biases can accumulate into position and heading deviations, increasing the risk of collisions and navigation failure. To address this problem, we present ReACT, a framework for accurate velocity tracking under asymmetric upper-body loads. Reflection-consistent disturbance modulation uses the estimated wrench to modulate actor features for load compensation while preserving action correspondence under reflected observations. Cross-load tracking consistency training compares loaded and unloaded conditions under the same velocity commands to promote consistent velocity tracking across loading conditions. Simulation experiments on a Unitree G1 humanoid robot show improved velocity tracking and reduced directional bias under varying asymmetric loads. Real-world experiments demonstrate collision-free goal reaching in all 40 tested navigation trials under unilateral hand loads with a fixed mapless navigation policy.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13929
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Word-Level Alignment of OCR and Ground Truth under Zoning and Reading-Order Errors
Abstract
OCR post-processing needs OCR text aligned word by word with its ground truth. Data sets rarely provide that mapping, and a monotone aligner can lose its place when zoning errors permute the reading order. We revisit this problem, the Zoning Problem of our 2019 paper, with a protocol that tests correctness under such permutations. We apply OCR-side reading-order permutations with construction-time word-level gold and score correct-match F1, cascade length and recovery. We compare the 2019 location-aware aligner, a re-anchoring extension of it, a RETAS-style baseline (RETAS-lite), a multi-region aligner that combines unique-word and unique n-gram anchors with chunk-to-window assignment, genalog's RETAS and the original Java RETAS. Experiments use synthetic character noise with block moves, paragraph swaps and column interleaving, historical newspaper reading-order permutations, geometry-derived gold for Tesseract OCR, and a small word-box set. Synthetic tests show where local tracking fails after large displacements and where unique-anchor methods collapse when unique anchors are scarce. On our synthetic boilerplate page the 2019 method had the highest F1 among those tested. Historical newspaper and geometry-derived evaluations carry the limitations of approximate gold and the article-order convention. The first multi-region version has a quadratic scaling cost. Local tracking remains fragile under large reading-order changes; the tested anchor-based methods handle many of those cases but fail when unique anchors are scarce. Approximate gold, the article-order convention, missing residual annotation, and the lack of modern multi-column word-level ground truth remain limitations.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13752
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EgoHMP: Achieving Precise Human Motion Prediction in 3D Scenes via Egocentric Cues
2026-11-10 06:54:06Xin Zhao, Pengzhan Zhou, Ziyi Li, Riheng Jia, Zhida Qin, Yu LiuAbstract
The complex interactive dependencies between human behavior and 3D scenes make accurately predicting human intent a major challenge for human motion prediction (HMP). While recent studies use human gaze to infer intent, gaze signals suffer from ambiguity, drift, and a reliance on eye-tracking hardware. Research in cognitive science indicates that egocentric views inherently encode potential interactions, while head motion serves as a crucial precursor in motion planning. Motivated by this, we propose EgoHMP, a novel framework that uses egocentric views and head motions as robust carriers of interaction intent. By translating this intent into an envisioning of future interactions, EgoHMP achieves precise HMP in 3D scenes. EgoHMP first employs a vision-motion alignment encoder to align the visual and motion feature spaces. Based on these aligned features, an egocentric intent predictor with a modality-aware modulation mechanism adaptively suppresses invalid interaction cues to predict an accurate interaction probability map. Furthermore, to enhance the stability and realism of the generated motions, we propose a contact-aware motion decoder. Specifically, inspired by the human cognitive process of planning trajectories prior to action execution, we first predict a global trajectory to serve as a self-prompt, effectively mitigating the optimization conflict in diffusion models. Subsequently, we introduce a multi-stage denoiser, which relieves motion artifacts by incorporating human contact modeling through a multi-stage optimization strategy. Finally, a GCN-based motion decoder is employed to synthesize physically plausible and semantically consistent motions. Extensive experiments demonstrate that EgoHMP achieves state-of-the-art performance.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13654
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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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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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Leveraging Psychophysical Attentional Distribution for Gaze-Augmented Reward Modeling
2026-11-10 02:36:14Chaeho Lee, Junyup Kim, Wansoo Kim, Young Min Jung, Sang Ho LeeAbstract
Integrating human feedback into language models has gained increasing attention as a means to align model outputs with human preferences, with Reinforcement Learning from Human Feedback (RLHF) serving as a prominent framework for preference-based alignment. Recent studies have incorporated eye-tracking (ET) data as an additional supervisory signal for reward model training, grounding reward learning in human reading behavior. Building on this approach, we extend prior fixation-centric models by leveraging established findings from cognitive psychology on human letter recognition. Specifically, we model visual attention as a spatially graded distribution centered on the current gaze location and incorporate this representation into reward model training. Our results show that the reward model trained with the proposed gaze distribution achieves higher preference prediction accuracy than a baseline model. Moreover, some results from our experiments are qualitatively consistent with established findings on human attention during reading, suggesting that they potentially capture cognitively meaningful aspects of human attention allocation.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13477
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Does Haptic Connection to a Healthy Partner Improve Motor Learning in Chronic Stroke? A Pilot Study of Robot-Based Dyadic Training
2026-11-09 22:20:25Erica L. Waters, Natalia Stroutinsky-Sobotka, Reem M. Azar, Frances S. Shofer, Pamela Z. Cacchione, Rochelle J. Mendonca, Michelle J. JohnsonAbstract
Purpose: Robot-based haptic dyads, where two users exchange forces through robots, have been proposed for robot-assisted neurorehabilitation. This intervention could increase motor learning by promoting motor variability, providing an implicit communication channel, and facilitating social interaction. Despite the potential benefits, few studies have investigated robot-based haptic dyads for neurological populations, and none have included a comprehensive clinical assessment. Design/Methodology/Approach: We present a pilot study investigation of individuals with chronic stroke learning a complex tracking task in solo and dyad conditions. Prior to training, participants completed a battery of cognitive, motor, and sensory assessments. We assessed changes in key kinematic outcomes including tracking error, distance traveled, mean absolute power, and sample entropy. We also performed a correlation analysis of clinical scores with baseline tracking errors. Findings: Baseline tracking error was strongly correlated (r > 0.6) with lower cognitive scores and higher motor scores. Only the solo group demonstrated lower tracking error at assessment ({\Delta} = -5.33, 95%CI [-10.25, -0.40], p = 0.027), but this result was tempered by the removal of an outlier participant. Neither group demonstrated clear retention. The dyad group showed significantly elevated performance, effort, and movement complexity during training. Research Limitations/Implications: The selected task was too difficult to reveal learning for either group, which may be related to its high cognitive demand. Sample size and stroke heterogeneity were also limitation. Originality/Value: Dyadic training increased movement complexity and effort, which are associated with better motor learning. Clinical characteristics such as lesion location, cognition, sensation, and chronicity may influence potential benefits of dyadic training.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13307
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Force-Closure Condition for Cooperative Multi-robot Pushing of Rectangular Objects
Abstract
In cooperative object manipulation, the placement of pushing contacts determines whether force closure can be achieved and agents can generate arbitrary forces and torques on the object correspondingly. This study derives a necessary and sufficient closed-form condition for the planar force closure of a rectangular object, a common object shape in everyday settings, under four frictionless pushing contacts. For a rectangle of length $b$ and width $a$, force closure holds if and only if $(l_1+l_3-b)(l_2+l_4-a)<0$, where $l_1,l_2,l_3,l_4$ are the four edge contact locations. This condition is independent of the object's center of mass and is used to verify contact configurations before contact force allocation. When the condition is not satisfied, the signs of the two factors indicate which opposing contact pair should be adjusted. The condition and adjustment rule are then integrated into a full-pose trajectory-tracking framework as a geometric feasibility layer to demonstrate their use in control. Static wrench feasibility tests validate the proposed condition, while narrow-passage floating-body simulations show that maintaining a force-closure configuration reduces the RMS tracking errors in $x$, $y$, and unwrapped yaw from $12.05$ m, $18.11$ m, and $535.96^\circ$ to $2.24$ m, $2.66$ m, and $2.69^\circ$, respectively.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13268
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Ground Reaction Force-Aware Model Predictive Control for Underactuated Upright Physical Assistance
2026-11-09 20:29:14Darren Biskup, Jeonghan Yu, Zibo Yu, Sunil K. AgrawalAbstract
Many rehabilitation robots execute prescribed motions using reactive tracking controllers that do not explicitly incorporate the user's instantaneous human-ground interaction. This paper presents Ground Reaction Force-Aware Model Predictive Control (GRF-MPC), a framework for upright physical assistance. By integrating live GRF and center of pressure (CoP) measurements directly into a human centroidal dynamics model, GRF-MPC formulates a real-time quadratic program (QP) that optimizes assistive forces and moments over a receding horizon. We deployed GRF-MPC on the Robotic Upright Stand Trainer (RobUST) in an underactuated 4-cable configuration and evaluated it against a fully actuated 8-cable Cartesian impedance baseline in nearly 300 trials across six healthy participants. Across tasks spanning impulsive perturbations, lunge trajectories, and voluntary limits of stability, GRF-MPC reduced peak planar center-of-mass (CoM) error by 19.6% during perturbations, lowered trajectory tracking error by 24.1%, required 28.7% less net mechanical work, and permitted a 45.9% greater normalized voluntary excursion area. The underlying QP solves in under 1 ms, comfortably enabling 100 Hz replanning and demonstrating that incorporating live human-ground interaction into predictive control yields stronger, more transparent physical assistance with half the actuators.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13204
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One Temperature to Rule Them All?
2026-11-09 19:00:01Aviv Orly (School of Physics and Astronomy, Tel Aviv University, Israel), Ori Shem-Ur (School of Physics and Astronomy, Tel Aviv University, Israel), Yaron Oz (School of Physics and Astronomy, Tel Aviv University, Israel)Abstract
Inference-time sampling is a central mechanism for improving large language models, with temperature controlling the tradeoff between reliability and diversity. While recent work suggests that varying temperature across samples can improve multi-sample inference, simple fixed-temperature schemes remain the standard in practice. We investigate this discrepancy in the canonical pass@K setting, asking when temperature diversity is useful, and when a single tuned temperature suffices. To this end, we analyze per-question success probabilities across multiple models and datasets, tracking how they vary with sampling temperature. We identify several consistent properties of these profiles, including that harder questions are less likely to share efficient sampling temperatures, although this trend is only logarithmic in hardness. We then derive an efficient method for evaluating the theoretically optimal multi-temperature allocation scheme, and show that although such a scheme improves performance, its gains over single-temperature baselines are typically modest when temperature is tuned as a function of the sampling budget K. We explain this phenomenon through mathematical mechanisms that we call dulling effects, and show how they ensure the robustness of temperature choices across different question distributions. Our results clarify when multi-temperature sampling is useful, and how temperatures should ideally be chosen.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13077
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