Towards Human-Like Manipulation through RL-Augmented Teleoperation and Mixture-of-Dexterous-Experts VLA

• URL: http://arxiv.org/abs/2603.08122v1
• Date: Mon, 09 Mar 2026 09:02:30 GMT
• Title: Towards Human-Like Manipulation through RL-Augmented Teleoperation and Mixture-of-Dexterous-Experts VLA
• Abstract summary: We introduce IMCopilot, a shared-autonomy assistant to simplify teleoperation data collection.<n>We present MoDE-VLA, an architecture that seamlessly integrates heterogeneous force and tactile modalities into a pretrained VLA backbone.<n>We validate our approach on four tasks of escalating complexity, demonstrating doubled success rate improvement over the baseline in dexterous contact-rich tasks.
• Score: 62.16042475700567
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While Vision-Language-Action (VLA) models have demonstrated remarkable success in robotic manipulation, their application has largely been confined to low-degree-of-freedom end-effectors performing simple, vision-guided pick-and-place tasks. Extending these models to human-like, bimanual dexterous manipulation-specifically contact-rich in-hand operations-introduces critical challenges in high-fidelity data acquisition, multi-skill learning, and multimodal sensory fusion. In this paper, we propose an integrated framework to address these bottlenecks, built upon two components. First, we introduce IMCopilot (In-hand Manipulation Copilot), a suite of reinforcement learning-trained atomic skills that plays a dual role: it acts as a shared-autonomy assistant to simplify teleoperation data collection, and it serves as a callable low-level execution primitive for the VLA. Second, we present MoDE-VLA (Mixture-of-Dexterous-Experts VLA), an architecture that seamlessly integrates heterogeneous force and tactile modalities into a pretrained VLA backbone. By utilizing a residual injection mechanism, MoDE-VLA enables contact-aware refinement without degrading the model's pretrained knowledge. We validate our approach on four tasks of escalating complexity, demonstrating doubled success rate improvement over the baseline in dexterous contact-rich tasks.

Related papers

• DexHiL: A Human-in-the-Loop Framework for Vision-Language-Action Model Post-Training in Dexterous Manipulation [14.050551792714083]
  Human-in-the-Loop (HiL) learning has proven to be a powerful mechanism for refining robot policies.<n>We present DexHiL, the first integrated arm-hand human-in-the-loop framework for dexterous VLA models.
  arXiv  Detail & Related papers  (2026-03-10T02:55:27Z)
• End-to-End Dexterous Arm-Hand VLA Policies via Shared Autonomy: VR Teleoperation Augmented by Autonomous Hand VLA Policy for Efficient Data Collection [10.217810309422232]
  We propose a framework that divides control between macro and micro motions.<n>A human operator guides the robot's arm pose through intuitive VR teleoperation.<n>An autonomous DexGrasp-VLA policy handles fine-grained hand control using real-time tactile and visual feedback.
  arXiv  Detail & Related papers  (2025-10-31T16:12:02Z)
• Expertise need not monopolize: Action-Specialized Mixture of Experts for Vision-Language-Action Learning [56.129822832095726]
  AdaMoE is a Mixture-of-Experts (MoE) architecture that inherits pretrained weights from dense VLA models.<n>A substantial 21.5% improvement in real-world experiments validates its practical effectiveness for robotic manipulation tasks.
  arXiv  Detail & Related papers  (2025-10-16T04:52:57Z)
• IntentionVLA: Generalizable and Efficient Embodied Intention Reasoning for Human-Robot Interaction [51.130510883952546]
  Vision-Language-Action (VLA) models leverage pretrained vision-language models (VLMs) to couple perception with robotic control.<n>We propose textbfIntentionVLA, a VLA framework with a curriculum training paradigm and an efficient inference mechanism.<n>Our proposed method first leverages carefully designed reasoning data that combine intention inference, spatial grounding, and compact embodied reasoning.
  arXiv  Detail & Related papers  (2025-10-09T04:49:46Z)
• ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation [62.58034332427291]
  ForceVLA is a novel end-to-end manipulation framework.<n>It treats external force sensing as a first-class modality within VLA systems.
  arXiv  Detail & Related papers  (2025-05-28T09:24:25Z)
• ChatVLA: Unified Multimodal Understanding and Robot Control with Vision-Language-Action Model [21.844214660424175]
  ChatVLA is a novel framework featuring Phased Alignment Training, which incrementally integrates multimodal data after initial control mastery, and a Mixture-of-Experts architecture to minimize task interference.<n>ChatVLA demonstrates competitive performance on visual question-answering datasets and significantly surpasses state-of-the-art vision-language-action (VLA) methods on multimodal understanding benchmarks.<n>Our findings highlight the potential of our unified framework for achieving both robust multimodal understanding and effective robot control.
  arXiv  Detail & Related papers  (2025-02-20T10:16:18Z)
• VTAO-BiManip: Masked Visual-Tactile-Action Pre-training with Object Understanding for Bimanual Dexterous Manipulation [8.882764358932276]
  Bimanual dexterous manipulation remains significant challenges in robotics due to the high DoFs of each hand and their coordination.<n>Existing single-hand manipulation techniques often leverage human demonstrations to guide RL methods but fail to generalize to complex bimanual tasks involving multiple sub-skills.<n>We introduce VTAO-BiManip, a novel framework that combines visual-tactile-action pretraining with object understanding to facilitate curriculum RL to enable human-like bimanual manipulation.
  arXiv  Detail & Related papers  (2025-01-07T08:14:53Z)
• Multi-Stage Knowledge Integration of Vision-Language Models for Continual Learning [79.46570165281084]
  We propose a Multi-Stage Knowledge Integration network (MulKI) to emulate the human learning process in distillation methods. MulKI achieves this through four stages, including Eliciting Ideas, Adding New Ideas, Distinguishing Ideas, and Making Connections. Our method demonstrates significant improvements in maintaining zero-shot capabilities while supporting continual learning across diverse downstream tasks.
  arXiv  Detail & Related papers  (2024-11-11T07:36:19Z)
• OpenVLA: An Open-Source Vision-Language-Action Model [131.74098076670103]
  We introduce OpenVLA, an open-source VLA trained on a diverse collection of 970k real-world robot demonstrations. OpenVLA shows strong results for generalist manipulation, outperforming closed models such as RT-2-X (55B) by 16.5% in absolute task success rate. We release model checkpoints, fine-tuning notebooks, and our PyTorch with built-in support for training VLAs at scale on Open X-Embodiment datasets.
  arXiv  Detail & Related papers  (2024-06-13T15:46:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.