arXiv subjects

Quentin Rouxel

Publications and source records attributed to Quentin Rouxel.

At least 19 recordsLinked to original sources

arXiv2026

A Unified Fractal Impedance Control Framework

Physical human--robot interaction requires controllers capable of maintaining intrinsically bounded interactions with the environment. The Fractal Impedance Controller (FIC) has been proven to guarantee these safety characteristics by design. This paper introduces the robotic implementation and experimental validation of its most advanced architecture: the Harmonic-Passive Motion Paradigm (H-PMP), an evolution of the Passive Motion Paradigm designed to mimic human-like interaction. Furthermore, we present a unified framework for FIC deployment, supported by an open-source software suite featuring ROS2, MuJoCo, and physical hardware integrations. Through three experiments conducted on a Kinova Gen3 arm (trajectory tracking, a wall press with sudden loss of contact, and learning-by-demonstration) we compare the H-PMP against earlier FIC architectures. The results demonstrate that the H-PMP achieves a seamless trade-off between admittance and impedance behaviours. This makes it ideal for deployment in unstructured environments, enabling the robot to transition between interaction modes with no computational overhead.

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arXiv2026

In-Context Learning World Models for Robot Dual-Arm Manipulation of Soft Garment with Partial Observations

Robust manipulation of soft objects, such as garments, is a critical skill for service and household robots to adopt, but remains challenging due to its high-dimensional state space, and material-dependent deformations. Humans address this challenge by using mental world models that "imagine" the shape and material properties from partial observations, which enables them to predict how actions affect future states. As such, embedding such world models in robots allows for better generalization in manipulating a wide variety of garments in the real world. However, learned world models typically require large amounts of real world data that is strenuous to collect, and a mechanism to infer latent properties that govern dynamics. We present a Soft-Object World Model that performs generative full-state estimation and action-conditioned dynamics prediction using a transformer backbone trained on large-scale simulated soft object interactions, with measures to mitigate the sim-to-real gap. By learning to infer latent shape and material cues through in-context learning over a variable-length partial point-cloud history, the model generalizes across robot embodiments, material parameters, garment types and shapes, and manipulation coordination modes without weight updates. The world model is then integrated with a Goal-Conditioned action planner for motion synthesis. We validate the approach in simulation and real world for bimanual garment folding, reporting improvements in reconstruction fidelity, prediction accuracy, and task success. Project Website: https://1am5hy.github.io/ICL soft object world model/

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arXiv2026

Beyond Point-Attached Semantics: Stable Object-Centric Semantic Fields for Robust Manipulation

Robotic manipulation often requires identifying functional parts, such as a mug handle or a hammer head. However, features attached to observed 3D points can vary with viewpoint and sensor noise, giving a policy inconsistent representations of the same part. We propose an object-centric semantic field to provide more consistent part-aware features for manipulation. We use the observed object cloud to build a continuous field, then read features from this field at 3D locations independently resampled from the cloud. Each feature uses the sampled object support as context, rather than directly reusing an individual point descriptor. Part classification distinguishes functional regions, cross-instance alignment brings corresponding part features together, and perturbation consistency encourages similar features under observation changes. The queried coordinates and features form semantic point clouds that are supplied to a DP3-based policy. We evaluate the approach on four RoboTwin simulation tasks and four real-world bimanual tasks, achieving average success rates of 69.3\% and 67.5\%, respectively. These improve on Utonia Point-wise by 7.0 and 32.5 percentage points, respectively, with real-world tests on held-out objects. A point-wise control with matched part supervision scores 63.5\% in simulation, compared with our 69.3\%. These results highlight the value of stable, object-conditioned semantic fields for manipulation across object instances and varying observations. Project Page: \href{https://zainzh.github.io/beyond-point-attached-semantics}{https://zainzh.github.io/beyond-point-attached-semantics}.

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arXiv2026

Reasoning Without Inference Cost: Latent Semantic Scaffolding for Robot VLA Policies

Vision-language-action (VLA) models are trained by imitation and capture what action to take but not why; adding causal reasoning improves manipulation, but current methods pay for it at inference time - generating reasoning tokens or rolling out predicted future states at every step, a cost that compounds over long horizons. We ask whether this benefit can instead be captured during training and discarded before deployment. We introduce Latent Semantic Scaffolding (LSS), an auxiliary loss applied during human-demonstration pretraining that aligns a VLA's action-token representations to text embeddings of physical-reasoning rationales through a small projection head. The head is dropped at inference, leaving the unmodified base policy with zero added cost. Our central finding concerns alignment granularity: aligning each action token to the rationale of its own manipulation phase (Dense LSS) rather than to a single pooled episode-level embedding (Pooled LSS) yields representations that transfer markedly better to held-out tasks. Dense LSS attains both the best in-distribution success and the best transfer to tasks unseen during alignment, whereas pooled alignment over-specializes to the training task. A representational probe shows Dense LSS induces roughly twice the per-phase separability in the backbone, supporting that phase-local alignment is the operative mechanism.

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arXiv2026

Towards Deploying VLA without Fine-Tuning: Plug-and-Play Inference-Time VLA Policy Steering via Embodied Evolutionary Diffusion

Vision-Language-Action (VLA) models have demonstrated significant potential in real-world robotic manipulation. However, pre-trained VLA policies still suffer from substantial performance degradation during downstream deployment. Although fine-tuning can mitigate this issue, its reliance on costly demonstration collection and intensive computation makes it impractical in real-world settings. In this work, we introduce VLA-Pilot, a plug-and-play inference-time policy steering method for zero-shot deployment of pre-trained VLA without any additional fine-tuning or data collection. We evaluate VLA-Pilot on six real-world downstream manipulation tasks across two distinct robotic embodiments, encompassing both in-distribution and out-of-distribution scenarios. Experimental results demonstrate that VLA-Pilot substantially boosts the success rates of off-the-shelf pre-trained VLA policies, enabling robust zero-shot generalization to diverse tasks and embodiments. Experimental videos and code are available at: https://rip4kobe.github.io/vla-pilot/.

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arXiv2026

Transferability Through Cooperative Competitions

This paper presents a novel framework for cooperative robotics competitions (coopetitions) that promote the transferability and composability of robotics modules, including software, hardware, and data, across heterogeneous robotic systems. The framework is designed to incentivize collaboration between teams through structured task design, shared infrastructure, and a royalty-based scoring system. As a case study, the paper details the implementation and outcomes of the first euROBIN Coopetition, held under the European Robotics and AI Network (euROBIN), which featured fifteen robotic platforms competing across Industrial, Service, and Outdoor domains. The study highlights the practical challenges of achieving module reuse in real-world scenarios, particularly in terms of integration complexity and system compatibility. It also examines participant performance, integration behavior, and team feedback to assess the effectiveness of the framework. The paper concludes with lessons learned and recommendations for future coopetitions, including improveme

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arXiv2026

Adapt as You Say: Online Interactive Bimanual Skill Adaptation via Human Language Feedback

Developing general-purpose robots capable of autonomously operating in human living environments requires the ability to adapt to continuously evolving task conditions. However, adapting high-dimensional coordinated bimanual skills to novel task variations at deployment remains a fundamental challenge. In this work, we present BiSAIL (Bimanual Skill Adaptation via Interactive Language), a novel framework that enables zero-shot online adaptation of offline-learned bimanual skills through interactive language feedback. The key idea of BiSAIL is to adopt a hierarchical reason-then-modulate paradigm, which first infers generalized adaptation objectives from multimodal task variations, and then adapts bimanual motions via diffusion modulation to achieve the inferred objectives. Extensive real-robot experiments across six bimanual tasks and two dual-arm platforms demonstrate that BiSAIL significantly outperforms existing methods in human-in-the-loop adaptability, task generalization and cross-embodiment scalability. This work enables the development of adaptive bimanual assistants that can be flexibly customized by non-expert users via intuitive verbal corrections. Experimental videos and code are available at https://rip4kobe.github.io/BiSAIL/.

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arXiv2026

From Vocal Instructions to Household Tasks: The Inria TIAGo++ in the euROBIN Service Robots Coopetition

This paper describes the Inria team's integrated robotics system used in the 1st euROBIN coopetition, during which service robots performed voice-activated household tasks in a kitchen setting. The team developed a modified TIAGo++ platform that leverages a whole-body control stack for autonomous and teleoperated modes, and an LLM-based pipeline for instruction understanding and task planning. The key contributions (opens-sourced) are the integration of these components and the design of custom teleoperation devices, addressing practical challenges in the deployment of service robots.

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arXiv2025

CaFe-TeleVision: A Coarse-to-Fine Teleoperation System with Immersive Situated Visualization for Enhanced Ergonomics

Teleoperation presents a promising paradigm for remote control and robot proprioceptive data collection. Despite recent progress, current teleoperation systems still suffer from limitations in efficiency and ergonomics, particularly in challenging scenarios. In this paper, we propose CaFe-TeleVision, a coarse-to-fine teleoperation system with immersive situated visualization for enhanced ergonomics. At its core, a coarse-to-fine control mechanism is proposed in the retargeting module to bridge workspace disparities, jointly optimizing efficiency and physical ergonomics. To stream immersive feedback with adequate visual cues for human vision systems, an on-demand situated visualization technique is integrated in the perception module, which reduces the cognitive load for multi-view processing. The system is built on a humanoid collaborative robot and validated with six challenging bimanual manipulation tasks. User study among 24 participants confirms that CaFe-TeleVision enhances ergonomics with statistical significance, indicating a lower task load and a higher user acceptance during teleoperation. Quantitative results also validate the superior performance of our system across six tasks, surpassing comparative methods by up to 28.89% in success rate and accelerating by 26.81% in completion time. Project webpage: https://clover-cuhk.github.io/cafe_television/

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arXiv2025

Extremum Flow Matching for Offline Goal Conditioned Reinforcement Learning

Imitation learning is a promising approach for enabling generalist capabilities in humanoid robots, but its scaling is fundamentally constrained by the scarcity of high-quality expert demonstrations. This limitation can be mitigated by leveraging suboptimal, open-ended play data, often easier to collect and offering greater diversity. This work builds upon recent advances in generative modeling, specifically Flow Matching, an alternative to Diffusion models. We introduce a method for estimating the minimum or maximum of the learned distribution by leveraging the unique properties of Flow Matching, namely, deterministic transport and support for arbitrary source distributions. We apply this method to develop several goal-conditioned imitation and reinforcement learning algorithms based on Flow Matching, where policies are conditioned on both current and goal observations. We explore and compare different architectural configurations by combining core components, such as critic, planner, actor, or world model, in various ways. We evaluated our agents on the OGBench benchmark and analyzed how different demonstration behaviors during data collection affect performance in a 2D non-prehensile pushing task. Furthermore, we validated our approach on real hardware by deploying it on the Talos humanoid robot to perform complex manipulation tasks based on high-dimensional image observations, featuring a sequence of pick-and-place and articulated object manipulation in a realistic kitchen environment. Experimental videos and code are available at: https://hucebot.github.io/extremum_flow_matching_website/

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arXiv2024

Words2Contact: Identifying Support Contacts from Verbal Instructions Using Foundation Models

This paper presents Words2Contact, a language-guided multi-contact placement pipeline leveraging large language models and vision language models. Our method is a key component for language-assisted teleoperation and human-robot cooperation, where human operators can instruct the robots where to place their support contacts before whole-body reaching or manipulation using natural language. Words2Contact transforms the verbal instructions of a human operator into contact placement predictions; it also deals with iterative corrections, until the human is satisfied with the contact location identified in the robot's field of view. We benchmark state-of-the-art LLMs and VLMs for size and performance in contact prediction. We demonstrate the effectiveness of the iterative correction process, showing that users, even naive, quickly learn how to instruct the system to obtain accurate locations. Finally, we validate Words2Contact in real-world experiments with the Talos humanoid robot, instructed by human operators to place support contacts on different locations and surfaces to avoid falling when reaching for distant objects.

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arXiv2024

Achieving Dexterous Bidirectional Interaction in Uncertain Conditions for Medical Robotics

Medical robotics can help improve and extend the reach of healthcare services. A major challenge for medical robots is the complex physical interaction between the robot and the patients which is required to be safe. This work presents the preliminary evaluation of a recently introduced control architecture based on the Fractal Impedance Control (FIC) in medical applications. The deployed FIC architecture is robust to delay between the master and the replica robots. It can switch online between an admittance and impedance behaviour, and it is robust to interaction with unstructured environments. Our experiments analyse three scenarios: teleoperated surgery, rehabilitation, and remote ultrasound scan. The experiments did not require any adjustment of the robot tuning, which is essential in medical applications where the operators do not have an engineering background required to tune the controller. Our results show that is possible to teleoperate the robot to cut using a scalpel, do an ultrasound scan, and perform remote occupational therapy. However, our experiments also highlighted the need for a better robots embodiment to precisely control the system in 3D dynamic tasks.

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arXiv2024

Flow Matching Imitation Learning for Multi-Support Manipulation

Humanoid robots could benefit from using their upper bodies for support contacts, enhancing their workspace, stability, and ability to perform contact-rich and pushing tasks. In this paper, we propose a unified approach that combines an optimization-based multi-contact whole-body controller with Flow Matching, a recently introduced method capable of generating multi-modal trajectory distributions for imitation learning. In simulation, we show that Flow Matching is more appropriate for robotics than Diffusion and traditional behavior cloning. On a real full-size humanoid robot (Talos), we demonstrate that our approach can learn a whole-body non-prehensile box-pushing task and that the robot can close dishwasher drawers by adding contacts with its free hand when needed for balance. We also introduce a shared autonomy mode for assisted teleoperation, providing automatic contact placement for tasks not covered in the demonstrations. Full experimental videos are available at: https://hucebot.github.io/flow_multisupport_website/

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arXiv2024

Multi-Contact Whole-Body Force Control for Position-Controlled Robots

Many humanoid and multi-legged robots are controlled in positions rather than in torques, which prevents direct control of contact forces, and hampers their ability to create multiple contacts to enhance their balance, such as placing a hand on a wall or a handrail. This letter introduces the SEIKO (Sequential Equilibrium Inverse Kinematic Optimization) pipeline, and proposes a unified formulation that exploits an explicit model of flexibility to indirectly control contact forces on traditional position-controlled robots. SEIKO formulates whole-body retargeting from Cartesian commands and admittance control using two quadratic programs solved in real-time. Our pipeline is validated with experiments on the real, full-scale humanoid robot Talos in various multi-contact scenarios, including pushing tasks, far-reaching tasks, stair climbing, and stepping on sloped surfaces. Code and videos are available at: https://hucebot.github.io/seiko_controller_website/

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arXiv2023

Feasibility Retargeting for Multi-contact Teleoperation and Physical Interaction

This short paper outlines two recent works on multi-contact teleoperation and the development of the SEIKO (Sequential Equilibrium Inverse Kinematic Optimization) framework. SEIKO adapts commands from the operator in real-time and ensures that the reference configuration sent to the underlying controller is feasible. Additionally, an admittance scheme is used to implement physical interaction, which is then combined with the operator's command and retargeted. SEIKO has been applied in simulations on various robots, including humanoid and quadruped robots designed for loco-manipulation. Furthermore, SEIKO has been tested on real hardware for bimanual heavy object carrying tasks.

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arXiv2023

Collaborative Bimanual Manipulation Using Optimal Motion Adaptation and Interaction Control

This work developed collaborative bimanual manipulation for reliable and safe human-robot collaboration, which allows remote and local human operators to work interactively for bimanual tasks. We proposed an optimal motion adaptation to retarget arbitrary commands from multiple human operators into feasible control references. The collaborative manipulation framework has three main modules: (1) contact force modulation for compliant physical interactions with objects via admittance control; (2) task-space sequential equilibrium and inverse kinematics optimization, which adapts interactive commands from multiple operators to feasible motions by satisfying the task constraints and physical limits of the robots; and (3) an interaction controller adopted from the fractal impedance control, which is robust to time delay and stable to superimpose multiple control efforts for generating desired joint torques and controlling the dual-arm robots. Extensive experiments demonstrated the capability of the collaborative bimanual framework, including (1) dual-arm teleoperation that adapts arbitrary infeasible commands that violate joint torque limits into continuous operations within safe boundaries, compared to failures without the proposed optimization; (2) robust maneuver of a stack of objects via physical interactions in presence of model inaccuracy; (3) collaborative multi-operator part assembly, and teleoperated industrial connector insertion, which validate the guaranteed stability of reliable human-robot co-manipulation.

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arXiv2022

Robust High-Transparency Haptic Exploration for Dexterous Telemanipulation

Robotic teleoperation will allow us to perform complex manipulation tasks in dangerous or remote environments, such as needed for planetary exploration or nuclear decommissioning. This work proposes a novel telemanipulation architecture using a passive Fractal Impedance Controller (FIC), which does not depend upon an active viscous component for stability guarantees. Compared to a traditional impedance controller in ideal conditions (no delays and maximum communication bandwidth), our proposed method yields higher transparency in interaction and demonstrates superior dexterity and capability in our telemanipulation test scenarios. We also validate its performance with extreme delays up to 1 s and communication bandwidths as low as 10 Hz. All results validate a consistent stability when using the proposed controller in challenging conditions, regardless of operator expertise.

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arXiv2022

Robust Impedance Control for Dexterous Interaction Using Fractal Impedance Controller with IK-Optimisation

Robust dynamic interactions are required to move robots in daily environments alongside humans. Optimisation and learning methods have been used to mimic and reproduce human movements. However, they are often not robust and their generalisation is limited. This work proposed a hierarchical control architecture for robot manipulators and provided capabilities of reproducing human-like motions during unknown interaction dynamics. Our results show that the reproduced end-effector trajectories can preserve the main characteristics of the initial human motion recorded via a motion capture system, and are robust against external perturbations. The data indicate that some detailed movements are hard to reproduce due to the physical limits of the hardware that cannot reach the same velocity recorded in human movements. Nevertheless, these technical problems can be addressed by using better hardware and our proposed algorithms can still be applied to produce imitated motions.

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