Robot
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One Trajectory, Two Requirements: Accuracy and Local Stability in Euler Sampling for Flow Matching Policies
Abstract
Flow matching robot policies commonly use a fixed Euler step count selected from offline accuracy sweeps. At inference time, however, each policy input (visual observation, robot state, and language instruction) together with a source-noise draw defines a different ODE trajectory and sequence of local Jacobians. We ask whether a step count that accurately follows the nominal noise-to-action trajectory can still be too coarse for strongly contracting directions around it. An exact two-coordinate system and a controlled augmentation of six trained flow models show that trajectory accuracy and local Euler stability can impose different step requirements even when the original learned transport is unchanged. We then study a frozen $\pi_{0.5}$-LIBERO checkpoint without modifying its weights or action dynamics. Across 30 policy inputs from 10 LIBERO-10 tasks, the Jacobian exhibits strong contracting directions near the action endpoint, yet no sampled mode violates the Euler non-expansion condition. On five of those inputs, accuracy sweeps require 4--9 Euler steps at 5% terminal error and 87--112 at 1%. These five paired cases are therefore consistent with accuracy-limited rather than stability-limited sampling under this local criterion, while the controlled experiments show why endpoint accuracy alone cannot rule out a separate stability requirement.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13311
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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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Curie's Jarvis: Autonomous Robotic Chemistry Experiments from Printed Protocols
2026-11-09 21:22:55Rumi Loghmani, Bozhang Song, Victor Alvarado, Aliyah Redding, Amir Barati FarimaniAbstract
A person running a bench experiment works from a printed instruction sheet, tells identical containers apart by the labels written on them, and looks at the bench again before each step. Lab automation usually works the other way around: the procedure is written as a script, and containers stand at fixed positions or carry fiducial markers. We present Curie's Jarvis, a system that lets a robot arm carry out a chemistry experiment on a bench set up for people. It reads the steps from a photo of the printed sheet, finds containers on an ordinary bench with no fixed positions or markers, tells identical-looking containers apart by reading their labels in four camera views, locates its target again before every step, and sets how each step is carried out, including its speed, from what is being handled. On a Franka Panda arm, Curie's Jarvis completed 5 of 5 runs of one protocol from a children's chemistry kit and 4 of 5 runs of another, with no human intervention, and in 160 locates on printed and handwritten labels it identified every container correctly. This brings robots a step closer to doing bench chemistry the way people do.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13251
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Incorporating World Models in Decision Making for Next Best View Selection in Active Sensing
2026-11-09 20:56:59Nikita Sarawgi, Omey M. Manyar, Satyandra K. GuptaAbstract
Efficient robotic 3D reconstruction requires selecting views that contribute new surface measurements with few physical captures. Pretrained world models can guide this choice, but a predicted surface may not produce valid depth measurements under the operating conditions of the deployed sensor. We present a deployment adaptation methodology for next-best-view selection that learns a sensor-return adapter from limited local calibration data while keeping the pretrained predictor fixed. The adapter estimates measurement dropout over each predicted surface, allowing the selection method to favor views likely to fill gaps in the measured reconstruction. In a real-robot experiment with a structured-light sensor, the adapted method achieves higher measured surface coverage with 60% fewer captures than nominal selection. The adapted method recovers a surface region that the nominal method repeatedly targets but fails to measure. These results suggest that robots can reuse pretrained predictive models while adapting their decisions to local operating conditions in data-scarce environments.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13229
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Latent Barrier Steering: Hierarchical Safety for Generative Planning
2026-11-09 20:56:25Renhao Zhang, Mingzhe Li, Bruno Castro da SilvaAbstract
We introduce Latent Barrier Steering (LBS), a hierarchical semantic-to-path framework for safe flow-matching-based generative planning. Existing control-barrier-function (CBF) safety filters for diffusion and flow-matching planners act locally on generated samples, either during sampling or after prediction. Such repair is effective for small violations, but can struggle when a new constraint blocks the whole selected behavior mode rather than merely perturbing it locally. LBS decouples behavior selection from path certification through two coordinated interventions: it first steers the generator's behavior latent toward rollouts with larger safety margin, then decodes the steered latent and applies a path-space CBF quadratic program (QP) only for residual violations. We show that latent steering increases the margin available to the path-space corrector, yielding finite-time recovery guarantees and reduced correction burden. Experiments across navigation and robot end-effector planning tasks show that LBS preserves safety while improving goal reaching and reducing correction burden compared with path-level safety filters.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13227
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Evaluating Long-Horizon Robot Planning under Uncertainty
2026-11-09 20:46:30Harsh Muriki, Gayathri Aravindan, Yuchen Zheng, Sonia ChernovaAbstract
Household robots must successfully navigate im-perfect circumstances, both in their internal models and the external world. We introduce a benchmark of seven long-horizon household tasks in Habitat that vary initial robot memory and environment configurations. We evaluate adaptations of ReAct, VLM-TAMP, and Tru-POMDP within a shared execution framework across 90 tasks-environment-memory combinations, totaling 1,350 episodes. Our evaluation measures task success, partial completion, and action efficiency, along with rule-based analysis of execution traces. ReAct achieves the highest overall task success (45.3%) and completion (71.7%), while having accurate initial memory does not consistently yield the highest performance for every planner. We identify recurring placement errors, precondition errors, and redundant actions through trace analysis. These findings highlight differences in how the evaluated planners use initial knowledge and respond to uncertainty.
Abstract (translated)
URL
https://arxiv.org/abs/2610.17326
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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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Cognitive Multi-Agent Search and Rescue: Language-Grounded Hierarchical Reinforcement Learning for Collaborative Robotics
2026-11-09 19:45:28Sepideh Ravanbakhsh, Hamidreza MoradiAbstract
Effective search and rescue (SAR) operations require autonomous systems capable of coordinating multiple heterogeneous agents, adapting to dynamic hazards, and aligning with human mission intent. Existing language-guided reinforcement learning (RL) methods primarily focus on single-agent instruction following and lack mechanisms for team-level coordination in complex, partially observable environments. This paper presents a Cognitive Multi-Agent Framework based on Language-Grounded Hierarchical Reinforcement Learning (LG-HRL) that integrates natural-language understanding, hierarchical policy decomposition, and a shared cognitive workspace to achieve cohesive multi-agent behavior in SAR scenarios. Human instructions are interpreted by a large language model (LLM) and transformed into structured semantic cues that guide high-level strategy selection and role assignment across UAV and UGV agents. A formal Language-Grounded Multi-Agent Hierarchical Decision Process (LG-MAHDP) is introduced to model the interplay between semantic grounding, hierarchical control, and distributed coordination. Comprehensive experiments in a simulated disaster environment show that LG-HRL improves learning speed by 2.3 times, increases coordination coherence by 37%, and enhances subgoal accuracy and hazard avoidance by 46% and 31%, respectively. The system achieves 20-30% higher victim rescue rates, demonstrates emergent role specialization, and adapts immediately to human verbal updates. Ablation studies confirm that both the shared cognitive workspace and language grounding are critical to overall system performance. Overall, this work establishes a new paradigm for language-guided multi-agent SAR robotics, demonstrating that semantic grounding combined with hierarchical reasoning enables safer, more efficient, and more interpretable autonomous rescue operations.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13161
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ASPIRE: An Agentic System-Identification Platform for Iterative Reasoning and Exploration
2026-11-09 19:03:05Erik Derner, Ji\v{r}\'i Kubal\'ik, Robert Babu\v{s}kaAbstract
System identification requires expert decisions on the identification method, incorporation of prior physical knowledge, and model validation. We propose ASPIRE, a framework for nonlinear system identification in which a large language model agent selects and executes the identification method. ASPIRE constructs closed-form, nonlinear static or dynamic models from measured data, optionally supplemented by a system description, the intended use of the model, and physical requirements expressed formally or in natural language. Each model includes its parameters, an estimation record, an interpretation of its terms, and an error evaluated on test data inaccessible to the agent. Formal physical requirements are verified independently. ASPIRE operates autonomously or interactively, allowing the agent to ask questions and the user to request further analyses and identification rounds. We evaluate ASPIRE on three measured systems: a mobile robot, a rotational system with dry friction, and a laboratory fermenter. Each system is tested with and without a description, in five runs per task. All 30 runs delivered valid models, at a cost of 0.96 to 3.38 US dollars per run. An assessment by the authors found that, for the 30 models recommended by the agent, one per run, the interpretation of the model and the documentation of its structure selection and parameter estimation fully satisfied 132 of 150 assessed items and partly satisfied 18. The most frequent discrepancy, found in nine models, concerned inaccurate or unsupported claims about model structure selection.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13114
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Robo-MLT: Dual-System Robotic Manipulation for Long-Horizon Medical Laboratory Tasks
2026-11-09 11:27:55Zequan Zeng, Kai Wang, Delvin Ce Zhang, Huaping Liu, Zhipeng Yuan, Junqin Yang, YunLong ZhangAbstract
Reliable medical laboratory automation requires robots to complete entire workflows in protocol order while handling changes during execution. A robot may execute each motion correctly yet fail the workflow by confusing visually similar stages or stalling after human intervention. We present Robo-MLT, a dual-system framework that couples explicit workflow monitoring with learned action generation. A vision--language planner composes an ordered atomic-skill sequence from the protocol and the initial workspace observation. A multiframe execution monitor detects when the active skill has completed or become infeasible, then advances the workflow or triggers replanning. A skill-conditioned vision--language--action policy generates the manipulation trajectories, and an asynchronous runtime coordinates it with the monitor. We evaluate Robo-MLT against four policy baselines and a VLM-based monitor baseline on blood gas analysis and blood sample tube sorting, with 50 nominal configurations per method and 20 sample-removal configurations. Robo-MLT achieves task success rates of 82\% and 70\%, exceeding $\pi_{0.5}$ by 30 and 22 percentage points, respectively. After an operator removes the sample being handled, the system revises the remaining plan and continues processing the other samples. These results indicate that, under the evaluated conditions, explicit workflow monitoring makes long-horizon laboratory manipulation more reliable and recoverable.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13039
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RoboACE: Adversarial Co-Evolution for Robot Self-Improvement
2026-11-09 10:29:56Delin Zhao, Zhengrong Yue, Junlin He, Yali WangAbstract
Robot agents can improve reusable skills, planning procedures, and recovery strategies from execution feedback, yet a fixed practice source leaves the search for new weaknesses unchanged. Useful practice depends on the agent's current capabilities, so the procedure that chooses practice must also be revisable. We present RoboACE (Robot Adversarial Co-Evolution), a framework that jointly evolves curriculum and execution harnesses around frozen foundation models. The curriculum harness uses accumulated capability evidence to propose informative challenges, while the execution harness responds with reusable revisions that expand execution capabilities. Alternating updates hold one side fixed as the other changes, and admitted challenges form fixed snapshots for comparing execution revisions. Starting from a compact set of LIBERO practice conditions, the resulting frozen deployment outperforms the evaluated controllers and agent systems across four LIBERO-family panels. On LIBERO-PRO, it reaches 74.0% success, exceeding the initial deployment by 15.0 percentage points and direct execution by its VLA controller by 13.5 points. Case studies document changes to evidence-guided practice selection, drawer-skill grounding, and controller intervention.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13034
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APS-VLA: Action-Guided and Future-Aware Token Pruning for Efficient Vision-Language-Action Models
2026-11-09 09:28:08Xiaoyang Xing, Chun-Mei Feng, Anthony Cohn, Xiaoliang Gong, Asoke NandiAbstract
Vision-language-action (VLA) policies have demonstrated strong generalization in robotic manipulation, yet processing dense visual tokens often incurs high inference latency. Visual token pruning offers a practical way to reduce this cost, but identifying a compact set of tokens that preserves reliable action generation remains challenging. Existing methods largely rely on static or immediate heuristic cues to estimate token importance. We introduce APS-VLA, a framework that learns visual token selection from robot demonstrations while keeping the pretrained VLA backbone frozen. A lightweight Decision Influence Estimator scores tokens conditioned on current visual observations, language instructions, and robot proprioception. Its training combines two complementary signals: action supervision matches both the full-input policy predictions and demonstrated actions, while an auxiliary visual transition objective uses demonstrated actions to predict subsequent visual features from the selected representation. At deployment, the estimator performs a single hard selection and packing step before the policy's native action-generation procedure; the auxiliary predictor is removed. Experiments with OpenVLA-OFT on LIBERO and with $\pi_{0.5}$ and XR-1 on RoboCasa365 demonstrate inference acceleration with modest changes in task success. Across five real-world tasks on two dual-arm platforms, APS-VLA reduces mean policy inference latency from 137.25 to 93.25 ms, achieving a $1.47\times$ speedup with an average success rate of 61%, compared with 60% for the unpruned baseline. Together, these results support APS-VLA as a modular, plug-in framework for efficient robotic control across VLA architectures, preserving the underlying policy parameters and native action-generation procedure.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13030
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PhysMoE: Fast Spatial Grounding with Single-Token Coordinate Representation in Modality-Decoupled VLMs
2026-11-08 16:00:02Ruimeng Liu, Denan Liang, Ruofei Bai, Fuming Zhang, Kuan Xu, Shenghai Yuan, Lihua XieAbstract
Embodied Vision-Language Models (VLMs) are backbones for robotic planning and manipulation. However, for coordinate prediction in grounding tasks, existing architectures suffer from two distinct bottlenecks: discrete tokenization disrupts spatial continuity with high decoding latency, while standard point regression fails on multi-contact affordances and irregularly shaped targets. To resolve these challenges, we propose PhysMoE, introducing three core contributions: (1) sharing lower layers for multimodal context while routing upper layers to specialized spatial (pixel) and linguistic (text) experts by physical modality, eliminating gradient interference; (2) encoding each coordinate as a single token by reusing the visual encoder's learned positional embeddings, injecting 2D spatial priors to natively align coordinates with visual features and ease training; and (3) differentiable Signed Distance Field (SDF) supervision on pointing, accommodating affordances and irregular geometries without costly mask decoders. Compared to the base model, PhysMoE achieves a 15.7% average gain and an 8.31x throughput speedup across 9 coordinate-based grounding benchmarks (trajectory, affordance, and localization), while preserving multimodal understanding and reasoning across 11 text-based embodied benchmarks.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13000
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CAFE: Closed-Loop Autonomous Task Formulation and Execution without Instance-Specific Instructions
2026-11-08 08:08:15Mingjia Chen, Xuewen Zhao, Philip Dames, Qi Mao, Fei Xie, Jun ChenAbstract
Scene-driven robot autonomy requires selecting tasks from observations as well as executing them. We study scene-driven task formulation and execution under predefined cleaning, safety, and storage/organization categories, without instance-specific goals or action sequences. CAFE maintains online memory of objects, functional zones, and execution states. A rule-guided Trigger-LLM admits formulation from accumulated evidence; Draft, Complete, and Audit propose goals, resolve execution details, and check dependencies. Outcomes update memory for correction and subsequent discovery. Evaluation in 12 AI2-THOR scenes compares three backbones and task-complexity tiers using coverage, constraint, and execution metrics. Tri-LLM records higher GCR scores than the evaluated baselines across three backbones and task-complexity tiers. Controls examine completion, auditing, zones, feedback, triggering, and prompt examples. LimoCobot traces illustrate storage, cleanup, and recovery from a failed grasp. The study examines how shared scene state connects task discovery to execution and outcome-driven revision.
Abstract (translated)
URL
https://arxiv.org/abs/2611.12983
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Embodied Science Agents: Making Embodied Experimental Design Learnable
2026-11-07 07:52:16Yihang Che, Zhongwei Yu, Yang Li, Xingyu Lu, Hongxia Yu, Jun WangAbstract
Recent advances in large models have accelerated AI for Science toward agentic science, where AI agents autonomously conduct scientific discovery. Realising this vision requires closing the loop between computational reasoning and physical experimentation, positioning self-driving laboratories (SDLs) as critical enablers. However, existing SDLs exhibit a fundamental paradox: foundation model integration has increased linguistic sophistication while severing physical grounding. By treating models as disembodied planners that output symbolic workflows for separate robotic systems, current architectures inherently block the development of embodied experimental intuition. We argue that overcoming this paradox requires embedding \emph{learnability} along three interdependent axes: cognitive learnability (learning what to investigate), experimental learnability (learning where to search), and embodied learnability (learning how to act). Building on this view, we introduce the Embodied Science Agent (ESA), which unifies digital intelligence with physical embodiment through three synergistic components: a Digital Agent that provides cognitive and experimental learnability via memory-based reasoning and Bayesian optimisation, an Embodied Agent that provides embodied learnability via generalisable instrument control, and an Embodied Unified Interface for seamless coordination. We validated ESA in autonomous wastewater treatment experiments, navigating over 200 million possible conditions. Through 15 continuous rounds, ESA raised the final solution transmittance from 82\% to 99.7\%, corresponding to a 98.5\% reduction in residual dye concentration and near-complete decolourisation, while its learned visuomotor policies generalised across varying instrument placements, a promising step toward next-generation SDLs for autonomous scientific discovery.
Abstract (translated)
URL
https://arxiv.org/abs/2611.12958
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Maze Runner: Autonomous Maze Navigation with Line-Based Localization and Control Barrier Functions
2026-11-06 22:32:59Alvaro J. Gaona, Lucas G\'omez-Velayos, Peter Pasuy-QuevedoAbstract
Maze navigation requires localization from sparse, repetitive geometry, planning through narrow corridors, and control that stays safe when the estimate or the map is wrong. We present Maze Runner, an open autonomy stack in C++ and ROS 2 that addresses these needs end to end on a simulated Husarion ROSbot XL with a 2D LiDAR in Gazebo. Perception extracts wall lines from each scan with RANSAC and total least squares, with per-line uncertainty. The same line front end feeds two estimators: an Extended Kalman Filter (EKF) that localizes against a line map built from the maze mesh, and an EKF SLAM variant that builds the line map online. Planning runs online on an inflated occupancy grid with A* plus line-of-sight shortcutting, or with a forward-only Hybrid A*. A nonlinear Model Predictive Controller (NMPC) tracks the plan while discrete-time Control Barrier Functions (CBFs) on world-frame LiDAR points keep the robot clear of walls and unmapped obstacles. The algorithms live in a ROS-free, unit-tested core library, and the whole stack builds and runs from a single reproducible environment. In simulation, the robot drives a 135 m route through the maze with 1.0 cm localization RMSE against the known map and 24 cm in SLAM mode, the planner returns a path in 6 ms, and the NMPC solves in 11.7 ms on average, never exceeding its 80 ms budget.
Abstract (translated)
URL
https://arxiv.org/abs/2611.12949
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On Agentic Manipulation Policies
2026-11-06 18:59:34Chen Chen, Omid Bagherieh, Naveen Nikamanth Appiah Balaji, Jesse T. Gonzalez, Negin Heravi, Fahad Islam, Xueting Li, Jean-Baptiste Regli, Kejia Ren, Petr \v{S}vec, David J. Yoon, Matthias M\"uller, Vladlen KoltunAbstract
Recent work on robot manipulation has focused on specialist policies trained on demonstrations. We show that generalist AI models can control manipulators more reliably than specialist policies such as vision-language-action models (VLAs) and world-action models (WAMs). We systematically analyze agentic manipulation policies (AMPs), in which a generalist AI model commands the manipulator directly, adapting its actions as the task unfolds. We present AMP1, a reference harness that runs unchanged across single-arm, bimanual, and mobile manipulation. AMP1 achieves state-of-the-art results on the RoboLab-120 and CaP-Gym benchmarks, with no demonstrations or task-specific training. On RoboLab-120, AMP1 reaches an 85.6\% success rate zero-shot, compared to 49.2\% and 42.9\% for leading VLA and WAM policies. The agent succeeds zero-shot in 104 of 108 real-world trials spanning twelve tasks and three robot instances, reducing failure rates by 20x and 15x relative to strong VLA and WAM baselines. The basic harness supplies no grasping, pushing, or placing skills; the agent develops its own strategies, such as searching for hidden objects, using objects as tools, and calibrating its cameras. With the addition of a persistent memory store, the agent can also learn from experience: an augmented harness with a memory representation that is carried across episodes lowers the failure rate on RoboDojo bimanual manipulation tasks by 4.6x relative to zero-shot deployment. Our analysis suggests that AMPs are substantially more general and robust than specialist manipulation policies, providing a promising foundation for robot manipulation.
Abstract (translated)
URL
https://arxiv.org/abs/2611.09892
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Locating Contact on Tendon-Driven Continuum Robots From Tendon Measurements Alone via Constrained Moving Horizon Estimation
Abstract
Tendon-driven continuum robots can reach through constrained spaces, but contact along their bodies is invisible to the sensors at their motors. We show that this is a structural limitation rather than a tuning problem: the map from contact forces to tendon measurements has a null space of dimension at least seven, so no estimator that leaves the contact support free can locate contact. When the obstacle geometry is known, however, non-penetration and complementarity make the contact force locally identifiable, and we prove that the resulting system with unknown contact inputs is locally exponentially strongly detectable. We present a constrained moving horizon estimator (MHE) that encodes these conditions and runs in real time as one quadratic program per sample, and we give a local, mode-wise robust stability result for it. In a finite- element benchmark of a two-section robot pressing and sliding on a wall, the estimator located contact to 0.9 mm and estimated contact force to 0.22 N, compared with 65 mm and 0.72 N for a tuned extended Kalman filter (EKF) with state projection. The estimator tolerated millimeter- level errors in the obstacle position and unmodeled wall friction, under which the projected EKF failed. Under randomized model mismatch over 40 trials, including unmodeled tendon friction, it remained better in force and location in every trial. Without contact, the MHE offered no advantage over the EKF, which confirms that its benefit comes from the contact constraints. This report extends a letter submitted to IEEE Control Systems Letters with complete proofs and additional experiments.
Abstract (translated)
URL
https://arxiv.org/abs/2611.09877
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Grasp-TAGS: Task-Aware Grasp Selection via Critic Distillation for Prehensile Loco-Manipulation
2026-11-06 18:44:33Xun Tu, Minghao Zou, Karthik DesinghAbstract
Loco-manipulation, which requires mobile manipulators to coordinate locomotion and manipulation over long horizons, is a key capability for large-object rearrangement in applications such as service robotics and warehouse automation. Despite recent progress, reliably executing these tasks remains challenging because performance depends on complex robot-object and object-environment interaction dynamics. Existing approaches address this challenge either through model-based methods, which often require accurate dynamics models, or through learning-based methods, which reduce this requirement but typically assume a fixed or predefined initial grasp. However, different geometrically stable grasps can lead to substantially different downstream manipulation performance. Therefore, we propose Grasp-TAGS, a task-aware grasp selection framework for goal-conditioned large-object rearrangement. We train a unified PPO-based reinforcement learning policy across multiple initial grasp configurations and distill the learned critic into a lightweight grasp utility network that ranks candidate grasps using only the candidate grasp configuration and goal information. At inference time, we select the grasp with the highest predicted downstream utility for the downstream policy to complete the task, before even making a contact. Experiments demonstrate that Grasp-TAGS improves rearrangement success over both fixed single-grasp policies and random grasp selection, achieving an average success rate of 89.84% compared with 79.87% for the best-performing single-grasp policies, an improvement of 9.97 percentage points. Grasp-TAGS also reduces final orientation errors across all evaluated objects. For more qualitative results, refer to the supplementary webpage: this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2611.09864
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