Language-Conditioned Robot Control

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Overview

The field studies how robots can turn spoken or written instructions into actions that fit what they perceive and what their bodies can do, from object manipulation and navigation to humanoid movement. Approaches include end-to-end vision-language-action policies, language-model planners paired with low-level controllers, and reusable skills or robot programs; training draws on demonstrations, simulation, reinforcement learning, and motion data. Work is expanding from simulated benchmarks such as LIBERO to real robots, including mobile platforms, drones, arms, and humanoids, with added tools for user feedback, safety checks, and adaptation. A central challenge is making language understanding reliable under ambiguity and distribution shift while ensuring that generated actions remain physically feasible, safe, and responsive in closed-loop execution.

Latest papers

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74 papers12 papers in the last four weeks, up 71% on the four weeks before. 0.1% of all new papers.

Apr 1, 2026

Low-Burden LLM-Based Preference Learning: Personalizing Assistive Robots from Natural Language Feedback for Users with Paralysis

Physically Assistive Robots require personalized behaviors to ensure user safety and comfort. However, traditional preference learning methods, like exhaustive pairwise comparisons, cause substantial physical and cognitive fatigue for users with severe motor impairments. To solve this, we propose a low-burden, offline framework that translates unstructured natural language feedback directly into deterministic robotic control policies. To safely bridge the gap between ambiguous human speech and robotic code, our pipeline uses Large Language Models (LLMs) grounded in the Occupational Therapy Practice Framework. This clinical reasoning decodes subjective user reactions into explicit physical and psychological needs, which are then mapped into transparent decision trees. Before deployment, an automated "LLM-as-a-Judge" verifies the code's structural safety. We validated this system in a simulated meal preparation study with 10 adults with paralysis. Results show our natural language approach significantly reduces user workload compared to traditional baselines. Additionally, occupational therapists confirmed the generated policies are safe and accurately reflect user preferences.

Keshav Shankar, Dan Ding, Wei Gao

Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA. · Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA.

Assistive RoboticsRobot LearningHuman-Robot InteractionLanguage Model-Based ControlLanguage-Conditioned Robot Control
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Mar 26, 2026

ThermoAct:Thermal-Aware Vision-Language-Action Models for Robotic Perception and Decision-Making

In recent human-robot collaboration environments, there is a growing focus on integrating diverse sensor data beyond visual information to enable safer and more intelligent task execution. Although thermal data can be crucial for enhancing robot safety and operational efficiency, its integration has been relatively overlooked in prior research. This paper proposes a novel Vision-Language-Action (VLA) framework that incorporates thermal information for robot task execution. The proposed system leverages a Vision-Language Model (VLM) as a high-level planner to interpret complex natural language commands and decompose them into simpler sub-tasks. This approach facilitates efficient data collection and robust reasoning for complex operations. Unlike conventional methods that rely solely on visual data, our approach integrates thermal information, enabling the robot to perceive physical properties and proactively ensure environmental safety. Experimental results from real-world task scenarios validate the feasibility of our proposed framework, suggesting its potential to enhance task success rates and safety compared to existing vision-based systems.Project page: https://youngchae912.github.io/ThermoAct/

Young-Chae Son, Dae-Kwan Ko, Yoon-Ji Choi +1

Dongguk University

Robotic PerceptionLanguage-Conditioned Robot ControlVLMs for Robotics
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Mar 25, 2026

QuadFM: Foundational Text-Driven Quadruped Motion Dataset for Generation and Control

Despite significant advances in quadrupedal robotics, a critical gap persists in foundational motion resources that holistically integrate diverse locomotion, emotionally expressive behaviors, and rich language semantics-essential for agile, intuitive human-robot interaction. Current quadruped motion datasets are limited to a few mocap primitives (e.g., walk, trot, sit) and lack diverse behaviors with rich language grounding. To bridge this gap, we introduce Quadruped Foundational Motion (QuadFM) , the first large-scale, ultra-high-fidelity dataset designed for text-to-motion generation and general motion control. QuadFM contains 11,784 curated motion clips spanning locomotion, interactive, and emotion-expressive behaviors (e.g., dancing, stretching, peeing), each with three-layer annotation-fine-grained action labels, interaction scenarios, and natural language commands-totaling 35,352 descriptions to support language-conditioned understanding and command execution. We further propose Gen2Control RL, a unified framework that jointly trains a general motion controller and a text-to-motion generator, enabling efficient end-to-end inference on edge hardware. On a real quadruped robot with an NVIDIA Orin, our system achieves real-time motion synthesis (<500 ms latency). Simulation and real-world results show realistic, diverse motions while maintaining robust physical interaction. The dataset will be released at https://github.com/GaoLii/QuadFM.

Li Gao, Fuzhi Yang, Jianhui Chen +4

AMAP, Alibaba Group

Robot Motion GenerationLegged RoboticsQuadruped LocomotionLanguage-Conditioned Robot ControlRL for Robotics
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Mar 25, 2026

SafeFlow: Real-Time Text-Driven Humanoid Whole-Body Control via Physics-Guided Rectified Flow and Selective Safety Gating

Recent advances in real-time interactive text-driven motion generation have enabled humanoids to perform diverse behaviors. However, kinematics-only generators often exhibit physical hallucinations, producing motion trajectories that are physically infeasible to track with a downstream motion tracking controller or unsafe for real-world deployment. These failures often arise from the lack of explicit physics-aware objectives for real-robot execution and become more severe under out-of-distribution (OOD) user inputs. Hence, we propose SafeFlow, a text-driven humanoid whole-body control framework that combines physics-guided motion generation with a 3-Stage Safety Gate driven by explicit risk indicators. SafeFlow adopts a two-level architecture. At the high level, we generate motion trajectories using Physics-Guided Rectified Flow Matching in a VAE latent space to improve real-robot executability, and further accelerate sampling via Reflow to reduce the number of function evaluations (NFE) for real-time control. The 3-Stage Safety Gate enables selective execution by detecting semantic OOD prompts using a Mahalanobis score in text-embedding space, filtering unstable generations via a directional sensitivity discrepancy metric, and enforcing final hard kinematic constraints such as joint and velocity limits before passing the generated trajectory to a low-level motion tracking controller. Extensive experiments on the Unitree G1 demonstrate that SafeFlow outperforms diffusion- and retargeting-based baselines in success rate, physical compliance, and inference speed while preserving motion diversity, with consistent gains across three downstream tracking controllers.

Hanbyel Cho, Sang-Hun Kim, Jeonguk Kang +1

Physical AI Lab, RX, Samsung Electronics

Robotic ControlHumanoid Robot ControlRobot Motion GenerationPhysics-Informed MLLanguage-Conditioned Robot Control
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Mar 18, 2026

RoboForge: Physically Optimized Text-guided Whole-Body Locomotion for Humanoids

While generative models have become effective at producing human-like motions from text, transferring these motions to humanoid robots for physical execution remains challenging. Existing pipelines are often limited by retargeting, where kinematic quality is undermined by physical infeasibility, contact-transition errors, and the high cost of real-world dynamical data. We present a unified latent-driven framework that bridges natural language and whole-body humanoid locomotion through a retarget-free, physics-optimized pipeline. Rather than treating generation and control as separate stages, our key insight is to couple them bidirectionally under physical constraints.We introduce a Physical Plausibility Optimization (PP-Opt) module as the coupling interface. In the forward direction, PP-Opt refines a teacher-student distillation policy with a plausibility-centric reward to suppress artifacts such as floating, skating, and penetration. In the backward direction, it converts reward-optimized simulation rollouts into high-quality explicit motion data, which is used to fine-tune the motion generator toward a more physically plausible latent distribution. This bidirectional design forms a self-improving cycle: the generator learns a physically grounded latent space, while the controller learns to execute latent-conditioned behaviors with dynamical integrity.Extensive experiments on the Unitree G1 humanoid show that our bidirectional optimization improves tracking accuracy and success rates. Across IsaacLab and MuJoCo, the implicit latent-driven pipeline consistently outperforms conventional explicit retargeting baselines in both precision and stability. By coupling diffusion-based motion generation with physical plausibility optimization, our framework provides a practical path toward deployable text-guided humanoid intelligence.

Xichen Yuan, Zhe Li, Bofan Lyu +4

MARS Lab, Nanyang Technological University

Humanoid Robot LocomotionHuman Motion GenerationLanguage-Conditioned Robot ControlDiffusion Models for Motion GenerationSynthetic Data Generation for Robotics
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Mar 18, 2026

Interpreting Context-Aware Human Preferences for Multi-Objective Robot Navigation

Robots operating in human-shared environments must not only achieve task-level navigation objectives such as safety and efficiency, but also adapt their behavior to human preferences. However, as human preferences are typically expressed in natural language and depend on environmental context, it is difficult to directly integrate them into low-level robot control policies. In this work, we present a pipeline that enables robots to understand and apply context-dependent navigation preferences by combining foundational models with a Multi-Objective Reinforcement Learning (MORL) navigation policy. Thus, our approach integrates high-level semantic reasoning with low-level motion control. A Vision-Language Model (VLM) extracts structured environmental context from onboard visual observations, while Large Language Models (LLM) convert natural language user feedback into interpretable, context-dependent behavioral rules stored in a persistent but updatable rule memory. A preference translation module then maps contextual information and stored rules into numerical preference vectors that parameterize a pretrained MORL policy for real-time navigation adaptation. We evaluate the proposed framework through quantitative component-level evaluations, a user study, and real-world robot deployments in various indoor environments. Our results demonstrate that the system reliably captures user intent, generates consistent preference vectors, and enables controllable behavior adaptation across diverse contexts. Overall, the proposed pipeline improves the adaptability, transparency, and usability of robots operating in shared human environments, while maintaining safe and responsive real-time control.

Tharun Sethuraman, Subham Agrawal, Nils Dengler +3

Hochschule Bonn-Rhein-Sieg, Germany · University of Bonn, Germany · Lamarr Institute for Machine Learning and Artificial Intelligence, Germany

Robot NavigationRobot Policy AdaptationSocial Robot NavigationLanguage-Conditioned Robot Control
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Mar 17, 2026

ECHO: Edge-Cloud Humanoid Orchestration for Language-to-Motion Control

We present ECHO, an edge--cloud framework for language-driven whole-body control of humanoid robots. A cloud-hosted diffusion-based text-to-motion generator synthesizes motion references from natural language instructions, while an edge-deployed reinforcement-learning tracker executes them in closed loop on the robot. The two modules are bridged by a compact, robot-native 38-dimensional motion representation that encodes joint angles, root planar velocity, root height, and a continuous 6D root orientation per frame, eliminating inference-time retargeting from human body models and remaining directly compatible with low-level PD control. The generator adopts a 1D convolutional UNet with cross-attention conditioned on CLIP-encoded text features; at inference, DDIM sampling with 10 denoising steps and classifier-free guidance produces motion sequences in approximately one second on a cloud GPU. The tracker follows a Teacher--Student paradigm: a privileged teacher policy is distilled into a lightweight student equipped with an evidential adaptation module for sim-to-real transfer, further strengthened by morphological symmetry constraints and domain randomization. An autonomous fall recovery mechanism detects falls via onboard IMU readings and retrieves recovery trajectories from a pre-built motion library. We evaluate ECHO on a retargeted HumanML3D benchmark, where it achieves strong generation quality (FID 0.029, R-Precision Top-1 0.686) under a unified robot-domain evaluator, while maintaining high motion safety and trajectory consistency. Real-world experiments on a Unitree G1 humanoid demonstrate stable execution of diverse text commands with zero hardware fine-tuning.

Haozhe Jia, Jianfei Song, Yuan Zhang +5

The Hong Kong University of Science and Technology (Guangzhou). · LimX Dynamics Technology Co., Ltd. · Shandong University. +1

Language-Conditioned Robot ControlRobot Motion GenerationHumanoid Motion TrackingDiffusion Models for Motion Generation
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Mar 4, 2026

IROSA: Interactive Robot Skill Adaptation using Natural Language

Foundation models have demonstrated impressive capabilities across diverse domains, while imitation learning provides principled methods for robot skill adaptation from limited data. Combining these approaches holds significant promise for direct application to robotics, yet this combination has received limited attention, particularly for industrial deployment. We present a novel framework that enables open-vocabulary skill adaptation through a tool-based architecture, maintaining a protective abstraction layer between the language model and robot hardware. Our approach leverages pre-trained LLMs to select and parameterize specific tools for adapting robot skills without requiring fine-tuning or direct model-to-robot interaction. We demonstrate the framework on a 7-DoF torque-controlled robot performing an industrial bearing ring insertion task, showing successful skill adaptation through natural language commands for speed adjustment, trajectory correction, and obstacle avoidance while maintaining safety, transparency, and interpretability.

Markus Knauer, Samuel Bustamante, Thomas Eiband +3

All authors are with the German Aerospace Center (DLR), Institute of Robotics and Mechatronics (RMC), Münchener Str. 20, 82234 Weßling, Germany. · Markus Knauer, Alin Albu-Schäffer and Samuel Bustamante are also with the School of Computation, Information and Technology (CIT), Technical University of Munich (TUM), Arcisstr. 21, 80333 Munich, Germany.

Robot Policy AdaptationRobot Skill LearningLanguage-Conditioned Robot Control
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Mar 2, 2026

LAD-Drive: Bridging Language and Trajectory with Action-Aware Diffusion Transformers

While multimodal large language models (MLLMs) provide advanced reasoning for autonomous driving, translating their discrete semantic knowledge into continuous trajectories remains a fundamental challenge. Existing methods often rely on unimodal planning heads that inherently limit their ability to represent multimodal driving behavior. Furthermore, most generative approaches frequently condition on one-hot encoded actions, discarding the nuanced navigational uncertainty critical for complex scenarios. To resolve these limitations, we introduce LAD-Drive, a generative framework that structurally disentangles high-level intention from low-level spatial planning. LAD-Drive employs an action decoder to infer a probabilistic meta-action distribution, establishing an explicit belief state that preserves the nuanced intent typically lost by one-hot encodings. This distribution, fused with the vehicle's kinematic state, conditions an action-aware diffusion decoder that utilizes a truncated denoising process to refine learned motion anchors into safe, kinematically feasible trajectories. Extensive evaluations on the LangAuto benchmark demonstrate that LAD-Drive achieves state-of-the-art results, outperforming competitive baselines by up to 59% in Driving Score while significantly reducing route deviations and collisions. We will publicly release the code and models on https://github.com/iis-esslingen/lad-drive.

Fabian Schmidt, Karol Fedurko, Markus Enzweiler +1

Institute for Intelligent Systems, Esslingen University of Applied Sciences, Germany. · Department of Computer Science, University of Freiburg, Germany.

Language-Conditioned Robot PlanningAutonomous Driving PlanningVLMs for Autonomous DrivingDiffusion-Based PlanningLanguage-Conditioned Robot Control
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Feb 28, 2026

TMR-VLA:Vision-Language-Action Model for Magnetic Motion Control of Tri-leg Silicone-based Soft Robot

In-vivo environments, magnetically actuated soft robots offer advantages such as wireless operation and precise control, showing promising potential for painless detection and therapeutic procedures. We developed a trileg magnetically driven soft robot (TMR) whose multi-legged design enables more flexible gaits and diverse motion patterns. For the silicone made of reconfigurable soft robots, its navigation ability can be separated into sequential motions, namely squatting, rotation, lifting a leg, walking and so on. Its motion and behavior depend on its bending shapes. To bridge motion type description and specific low-level voltage control, we introduced TMR-VLA, an end-to-end multi-modal system for a trileg magnetic soft robot capable of performing hybrid motion types, which is promising for developing a navigation ability by adapting its shape to language-constrained motion types. The TMR-VLA deploys embodied endoluminal localization ability from EndoVLA, and fuses sequential frames and natural language commands as input. Low-level voltage output is generated based on the current observation state and specific motion type description. The result shows the TMR-VLA can predict how the voltage applied to TMR will change the dynamics of a silicon-made soft robot. The TMR-VLA reached a 74% average success rate.

Ruijie Tang, Chi Kit Ng, Kaixuan Wu +5

Language-Conditioned Robot ControlSoft RoboticsVLMs for Robotics
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Feb 26, 2026

SignVLA: A Gloss-Free Vision-Language-Action Framework for Real-Time Sign Language-Guided Robotic Manipulation

We present, to our knowledge, the first sign language-driven Vision-Language-Action (VLA) framework for intuitive and inclusive human-robot interaction. Unlike conventional approaches that rely on gloss annotations as intermediate supervision, the proposed system adopts a gloss-free paradigm and directly maps visual sign gestures to semantic instructions. This design reduces annotation cost and avoids the information loss introduced by gloss representations, enabling more natural and scalable multimodal interaction. In this work, we focus on a real-time alphabet-level finger-spelling interface that provides a robust and low-latency communication channel for robotic control. Compared with large-scale continuous sign language recognition, alphabet-level interaction offers improved reliability, interpretability, and deployment feasibility in safety-critical embodied environments. The proposed pipeline transforms continuous gesture streams into coherent language commands through geometric normalization, temporal smoothing, and lexical refinement, ensuring stable and consistent interaction. Furthermore, the framework is designed to support future integration of transformer-based gloss-free sign language models, enabling scalable word-level and sentence-level semantic understanding. Experimental results demonstrate the effectiveness of the proposed system in grounding sign-derived instructions into precise robotic actions under diverse interaction scenarios. These results highlight the potential of the framework to advance accessible, scalable, and multimodal embodied intelligence.

Xinyu Tan, Ningwei Bai, Harry Gardener +6

Department of Computer Science, University College London (UCL), London, WC1E 6BT, U.K.

Vision-Language-Action ModelsHuman-Robot InteractionLanguage-Conditioned Robot ControlSign Language RecognitionSign Language Translation
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Feb 23, 2026

An Approach to Combining Video and Speech with Large Language Models in Human-Robot Interaction

Interpreting human intent accurately is a central challenge in human-robot interaction (HRI) and a key requirement for achieving more natural and intuitive collaboration between humans and machines. This work presents a novel multimodal HRI framework that combines advanced vision-language models, speech processing, and fuzzy logic to enable precise and adaptive control of a Dobot Magician robotic arm. The proposed system integrates Florence-2 for object detection, Llama 3.1 for natural language understanding, and Whisper for speech recognition, providing users with a seamless and intuitive interface for object manipulation through spoken commands. By jointly addressing scene perception and action planning, the approach enhances the reliability of command interpretation and execution. Experimental evaluations conducted on consumer-grade hardware demonstrate a command execution accuracy of 75%, highlighting both the robustness and adaptability of the system. Beyond its current performance, the proposed architecture serves as a flexible and extensible foundation for future HRI research, offering a practical pathway toward more sophisticated and natural human-robot collaboration through tightly coupled speech and vision-language processing.

Guanting Shen, Zi Tian

The authors are with the Dalian University of Technology. Dalian, China.

Human-Robot InteractionLanguage-Conditioned Robot ManipulationLanguage-Conditioned Robot ControlVLMs for Robotics
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Feb 19, 2026

When Vision Overrides Language: Evaluating and Mitigating Counterfactual Failures in VLAs

Vision-Language-Action models (VLAs) promise to ground language instructions in robot control, yet in practice often fail to faithfully follow language. When presented with instructions that lack strong scene-specific supervision, VLAs suffer from counterfactual failures: they act based on vision shortcuts induced by dataset biases, repeatedly executing well-learned behaviors and selecting objects frequently seen during training regardless of language intent. To systematically study it, we introduce LIBERO-CF, the first counterfactual benchmark for VLAs that evaluates language following capability by assigning alternative instructions under visually plausible LIBERO layouts. Our evaluation reveals that counterfactual failures are prevalent yet underexplored across state-of-the-art VLAs. We propose Counterfactual Action Guidance (CAG), a simple yet effective dual-branch inference scheme that explicitly regularizes language conditioning in VLAs. CAG combines a standard VLA policy with a language-unconditioned Vision-Action (VA) module, enabling counterfactual comparison during action selection. This design reduces reliance on visual shortcuts, improves robustness on under-observed tasks, and requires neither additional demonstrations nor modifications to existing architectures or pretrained models. Extensive experiments demonstrate its plug-and-play integration across diverse VLAs and consistent improvements. For example, on LIBERO-CF, CAG improves π0.5π_{0.5} by 9.7% in language following accuracy and 3.6% in task success on under-observed tasks using a training-free strategy, with further gains of 15.5% and 8.5%, respectively, when paired with a VA model. In real-world evaluations, CAG reduces counterfactual failures of 9.4% and improves task success by 17.2% on average.

Yu Fang, Yuchun Feng, Dong Jing +5

University of North Carolina at Chapel Hill

VLM RobustnessCounterfactual Evaluation of VLMsRobotic ManipulationVision-Language-Action ModelsLanguage-Conditioned Robot ControlRobustness of VLA Models
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Sep 29, 2025

Prompting Robot Teams with Natural Language

This paper presents a framework to prompt multi-robot teams with high-level tasks using natural language expressions. Our objective is to use the reasoning capabilities of language models in understanding and decomposing multi-robot collaboration and decision-making tasks, but in settings where such models cannot be called at deployment time. However, it is hard to specify the behavior of an individual robot from a team instruction, and have it continuously adapt to actions from other robots. This necessitates a framework with the representational capacity required by the logic and semantics of a task, and yet supports decentralized, real-time operation. We solve this dilemma by recognizing that a task can be represented as a deterministic finite automaton, and that recurrent neural networks (RNNs) can encode numerous automata. This allows us to distill the logic and sequential decompositions of sub-tasks obtained from a language model into an RNN, and align its internal states with the semantics of a given task. This leads to a tiny model that encapsulates the reasoning of the language model and can be implemented onboard. To interpret the internal state of the RNN for a decentralized execution, we train a graph neural network control policy conditioned on the hidden states of the RNN and the language embeddings. We present evaluations on simulated and real-world multi-robot tasks that require sequential and collaborative behavior by the team, demonstrating scalable, robust, real-time performance -- sites.google.com/view/prompting-teams.

Eduardo Sebastián, Nicolas Pfitzer, Ajay Shankar +1

Department of Computer Science and Technology, University of Cambridge, UK · Department of Mechanical and Process Engineering, ETH Zurich, Switzerland

Large Language Model-Based Robot PlanningMulti-Robot CoordinationLanguage-Conditioned Robot Control
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