BiPreManip: Learning Affordance-Based Bimanual Preparatory Manipulation through Anticipatory Collaboration

• URL: http://arxiv.org/abs/2603.21679v1
• Date: Mon, 23 Mar 2026 08:12:24 GMT
• Title: BiPreManip: Learning Affordance-Based Bimanual Preparatory Manipulation through Anticipatory Collaboration
• Abstract summary: Collaborative Preparatory Manipulation is a class of bimanual manipulation tasks that demand understanding object semantics and geometry.<n>We propose a visual affordance-based framework that first envisions the final goal-directed action and then guides one arm to perform a sequence of preparatory manipulations.<n>This affordance-centric representation enables anticipatory inter-arm reasoning and coordination, generalizing effectively across various objects.
• Score: 35.18224449308742
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many everyday objects are difficult to directly grasp (e.g., a flat iPad) or manipulate functionally (e.g., opening the cap of a pen lying on a desk). Such tasks require sequential, asymmetric coordination between two arms, where one arm performs preparatory manipulation that enables the other's goal-directed action - for instance, pushing the iPad to the table's edge before picking it up, or lifting the pen body to allow the other hand to remove its cap. In this work, we introduce Collaborative Preparatory Manipulation, a class of bimanual manipulation tasks that demand understanding object semantics and geometry, anticipating spatial relationships, and planning long-horizon coordinated actions between the two arms. To tackle this challenge, we propose a visual affordance-based framework that first envisions the final goal-directed action and then guides one arm to perform a sequence of preparatory manipulations that facilitate the other arm's subsequent operation. This affordance-centric representation enables anticipatory inter-arm reasoning and coordination, generalizing effectively across various objects spanning diverse categories. Extensive experiments in both simulation and the real world demonstrate that our approach substantially improves task success rates and generalization compared to competitive baselines.

Related papers

• CosmoH2G: A Hand-to-Gripper Transfer Dataset and Baseline Method for Object Manipulation with Complex Spatial Movements [63.40365657068892]
  Transferring human hand demonstrations to robotic grippers has recently emerged as a cost-effective solution for robot learning.<n>Motivated by this gap, we adopt an implicit, data-driven approach guided by fine-grained hand-pose motions.<n>In both simulation and real-robot experiments, our framework enables stable and precise hand-to-gripper transfer of complex spatial manipulations.
  arXiv  Detail & Related papers  (2026-09-07T13:47:22Z)
• EMPIRE: Explicit Manipulation Planning as a Learnable Intermediate Representation for Egocentric Hand-Motion Forecasting [75.76075128622837]
  We propose a two-stage framework that introduces Explicit Manipulation Planning as an Intermediate Representation for Egocentric hand-motion forecasting.<n>A motion generator synthesizes future bimanual hand motions conditioned on frozen planner representations.<n>Our method achieves state-of-the-art forecasting accuracy, with an MPJPE of 84.53 mm and a finger-relative error of 38.97mm.
  arXiv  Detail & Related papers  (2026-08-23T14:58:31Z)
• Task-Aware Bimanual Affordance Prediction via VLM-Guided Semantic-Geometric Reasoning [22.1291953459429]
  Bimanual manipulation requires reasoning about where to interact with an object and which arm should perform each action.<n>Existing approaches either treat affordance prediction as coarse part segmentation or rely on geometrics for arm assignment.<n>We propose a hierarchical framework for task-aware bimanual affordance prediction.
  arXiv  Detail & Related papers  (2026-04-09T19:39:45Z)
• Language-Grounded Decoupled Action Representation for Robotic Manipulation [78.42228162226839]
  We propose the Language-Grounded Decoupled Action Representation (LaDA) framework to connect perception and control.<n>LaDA introduces a fine-grained intermediate layer of three interpretable action primitives--translation, rotation, and gripper control--providing explicit semantic structure for low-level actions.<n>It further employs a semantic-guided soft-label contrastive learning objective to align similar action primitives across tasks, enhancing generalization and motion consistency.
  arXiv  Detail & Related papers  (2026-03-13T13:08:26Z)
• Learning Bimanual Manipulation via Action Chunking and Inter-Arm Coordination with Transformers [4.119006369973485]
  We focus on coordination and efficiency between both arms, particularly synchronized actions.<n>We propose a novel imitation learning architecture that predicts cooperative actions.<n>Our model demonstrated a high success rate for comparison and suggested a suitable architecture for the policy learning of bimanual manipulation.
  arXiv  Detail & Related papers  (2025-03-18T05:20:34Z)
• Rethinking Bimanual Robotic Manipulation: Learning with Decoupled Interaction Framework [51.39847596489193]
  Bimanual robotic manipulation is an emerging and critical topic in the robotics community.<n>We propose a novel decoupled interaction framework that considers the characteristics of different tasks in bimanual manipulation.<n>Our framework achieves outstanding performance, with a 23.5% boost over the SOTA method.
  arXiv  Detail & Related papers  (2025-03-12T09:28:41Z)
• DexHandDiff: Interaction-aware Diffusion Planning for Adaptive Dexterous Manipulation [78.60543357822957]
  Dexterous manipulation with contact-rich interactions is crucial for advanced robotics.<n>We introduce DexHandDiff, an interaction-aware diffusion planning framework for adaptive dexterous manipulation.<n>Our framework achieves an average of 70.7% success rate on goal adaptive dexterous tasks, highlighting its robustness and flexibility in contact-rich manipulation.
  arXiv  Detail & Related papers  (2024-11-27T18:03:26Z)
• PEAR: Phrase-Based Hand-Object Interaction Anticipation [20.53329698350243]
  First-person hand-object interaction anticipation aims to predict the interaction process based on current scenes and prompts. Existing research typically anticipates only interaction intention while neglecting manipulation. We propose a novel model, PEAR, which jointly anticipates interaction intention and manipulation.
  arXiv  Detail & Related papers  (2024-07-31T10:28:49Z)
• DiffH2O: Diffusion-Based Synthesis of Hand-Object Interactions from Textual Descriptions [15.417836855005087]
  We propose a novel method, dubbed DiffH2O, which can synthesize realistic, one or two-handed object interactions.<n>The method introduces three techniques that enable effective learning from limited data.
  arXiv  Detail & Related papers  (2024-03-26T16:06:42Z)
• Physically Plausible Full-Body Hand-Object Interaction Synthesis [32.83908152822006]
  We propose a physics-based method for synthesizing dexterous hand-object interactions in a full-body setting. Existing methods often focus on isolated segments of the interaction process and rely on data-driven techniques that may result in artifacts.
  arXiv  Detail & Related papers  (2023-09-14T17:55:18Z)
• Adversarial Motion Modelling helps Semi-supervised Hand Pose Estimation [116.07661813869196]
  We propose to combine ideas from adversarial training and motion modelling to tap into unlabeled videos. We show that an adversarial leads to better properties of the hand pose estimator via semi-supervised training on unlabeled video sequences. The main advantage of our approach is that we can make use of unpaired videos and joint sequence data both of which are much easier to attain than paired training data.
  arXiv  Detail & Related papers  (2021-06-10T17:50:19Z)

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.