AdaMorph: Unified Motion Retargeting via Embodiment-Aware Adaptive Transformers

• URL: http://arxiv.org/abs/2601.07284v1
• Date: Mon, 12 Jan 2026 07:39:38 GMT
• Title: AdaMorph: Unified Motion Retargeting via Embodiment-Aware Adaptive Transformers
• Abstract summary: We present a unified neural framework that enables a single model to adapt human motion to diverse robot morphologies.<n> Experimental results on 12 distinct humanoid robots demonstrate that AdaMorph effectively control across heterogeneous topologies.
• Score: 49.796014934503184
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Retargeting human motion to heterogeneous robots is a fundamental challenge in robotics, primarily due to the severe kinematic and dynamic discrepancies between varying embodiments. Existing solutions typically resort to training embodiment-specific models, which scales poorly and fails to exploit shared motion semantics. To address this, we present AdaMorph, a unified neural retargeting framework that enables a single model to adapt human motion to diverse robot morphologies. Our approach treats retargeting as a conditional generation task. We map human motion into a morphology-agnostic latent intent space and utilize a dual-purpose prompting mechanism to condition the generation. Instead of simple input concatenation, we leverage Adaptive Layer Normalization (AdaLN) to dynamically modulate the decoder's feature space based on embodiment constraints. Furthermore, we enforce physical plausibility through a curriculum-based training objective that ensures orientation and trajectory consistency via integration. Experimental results on 12 distinct humanoid robots demonstrate that AdaMorph effectively unifies control across heterogeneous topologies, exhibiting strong zero-shot generalization to unseen complex motions while preserving the dynamic essence of the source behaviors.

Related papers

• Unified Motion Retargeting for Humanoids with Learned Point Cloud Correspondence [24.060385614379783]
  We present Unified Motion Retargeting (UMR), a framework that learns dense point cloud correspondence without requiring manually designed human-robot mappings.<n>UMR achieves higher motion fidelity and plausibility than state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-09-02T05:43:19Z)
• PhysDrift: Bridging the Embodiment Gap in Humanoid Co-Speech Motion Generation [31.168201029670865]
  We introduce PhysDrift, an embodiment-aware co-speech motion framework that predicts humanoid joint trajectories from speech without relying on human-body representations.<n>Experiments and real-world humanoid deployment demonstrate that embodiment-aware robot-native generation substantially improves speech-motion alignment, physical plausibility, motion smoothness, inference efficiency, and real-time interaction capability.
  arXiv  Detail & Related papers  (2026-06-18T08:31:58Z)
• Human2Humanoid: Physics-Aware Cross-Morphology Motion Retargeting for Humanoid Robots [9.096721498763717]
  Retargeting human motion to humanoid robots is critical for teleoperation, imitation learning and human-robot interaction.<n>This paper presents Human2Human, an unsupervised motion framework that transfers human motions to humanoid robot behaviors with high fidelity.<n> Experimental results show that the proposed method successfully retargets human motion to the Unitree G1 humanoid robot without paired data.
  arXiv  Detail & Related papers  (2026-06-02T10:53:06Z)
• OmniHumanoid: Streaming Cross-Embodiment Video Generation with Paired-Free Adaptation [63.65885191759872]
  Cross-embodiment video generation aims to transfer motions across different humanoid embodiments.<n>We present OmniHumanoid, a framework that factorizes transferable motion learning and embodiment-specific adaptation.
  arXiv  Detail & Related papers  (2026-05-12T12:21:11Z)
• ReActor: Reinforcement Learning for Physics-Aware Motion Retargeting [8.302518310051232]
  We propose a bilevel optimization framework that adapts reference motions to a robot's morphology.<n>By integrating directly with physics simulation, we produce physically plausible motions that facilitate robust imitation learning.
  arXiv  Detail & Related papers  (2026-05-07T17:20:15Z)
• Toward Hardware-Agnostic Quadrupedal World Models via Morphology Conditioning [29.296527466383242]
  We take a step towards a framework for training a generalizable Quadrupedal World Model.<n>We explicitly condition the generative dynamics on the robot's engineering specifications.<n>We introduce, for the first time, a world model that enables zero-shot generalization to new morphologies for locomotion.
  arXiv  Detail & Related papers  (2026-04-09T21:31:24Z)
• RoboForge: Physically Optimized Text-guided Whole-Body Locomotion for Humanoids [20.796118584632904]
  We present a unified latent-driven framework that bridges natural language and whole-body locomotion.<n>Our framework provides a practical path toward deployable text-guided humanoid intelligence.
  arXiv  Detail & Related papers  (2026-03-18T17:02:56Z)
• SPARK: Skeleton-Parameter Aligned Retargeting on Humanoid Robots with Kinodynamic Trajectory Optimization [9.553570391005257]
  Human motion provides rich priors for training general-purpose humanoid control policies, but raw demonstrations are often incompatible with a robot's kinematics and dynamics.<n>We present a two-stage pipeline for generating natural and dynamically feasible motion references from task-space human data.
  arXiv  Detail & Related papers  (2026-03-12T03:03:14Z)
• EchoMotion: Unified Human Video and Motion Generation via Dual-Modality Diffusion Transformer [64.69014756863331]
  We introduce EchoMotion, a framework designed to model the joint distribution of appearance and human motion.<n>We also propose MVS-RoPE, which offers unified 3D positional encoding for both video and motion tokens.<n>Our findings reveal that explicitly representing human motion is to appearance, significantly boosting the coherence and plausibility of human-centric video generation.
  arXiv  Detail & Related papers  (2025-12-21T17:08:14Z)
• OmniRetarget: Interaction-Preserving Data Generation for Humanoid Whole-Body Loco-Manipulation and Scene Interaction [76.44108003274955]
  A dominant paradigm for teaching humanoid robots complex skills is to retarget human motions as kinematic references to train reinforcement learning policies.<n>We introduce OmniRetarget, an interaction-preserving data generation engine based on an interaction mesh.<n>By minimizing the Laplacian deformation between the human and robot meshes, OmniRetarget generates kinematically feasible trajectories.
  arXiv  Detail & Related papers  (2025-09-30T17:59:02Z)
• GENMO: A GENeralist Model for Human MOtion [64.16188966024542]
  We present GENMO, a unified Generalist Model for Human Motion that bridges motion estimation and generation in a single framework.<n>Our key insight is to reformulate motion estimation as constrained motion generation, where the output motion must precisely satisfy observed conditioning signals.<n>Our novel architecture handles variable-length motions and mixed multimodal conditions (text, audio, video) at different time intervals, offering flexible control.
  arXiv  Detail & Related papers  (2025-05-02T17:59:55Z)
• HeteroMorpheus: Universal Control Based on Morphological Heterogeneity Modeling [12.771577344846282]
  HeteroMorpheus is a novel method based on heterogeneous graph Transformer. We demonstrate the superiority of HeteroMorpheus against state-of-the-art methods in the capability of policy generalization.
  arXiv  Detail & Related papers  (2024-08-02T12:40:01Z)
• I-CTRL: Imitation to Control Humanoid Robots Through Constrained Reinforcement Learning [8.97654258232601]
  We develop a framework to control humanoid robots through bounded residual reinforcement learning (I-CTRL)<n>I-CTRL excels in motion imitation with simple and unique rewards that generalize across five robots.<n>Our framework introduces an automatic priority scheduler to manage large-scale motion datasets.
  arXiv  Detail & Related papers  (2024-05-14T16:12:27Z)
• Universal Morphology Control via Contextual Modulation [52.742056836818136]
  Learning a universal policy across different robot morphologies can significantly improve learning efficiency and generalization in continuous control. Existing methods utilize graph neural networks or transformers to handle heterogeneous state and action spaces across different morphologies. We propose a hierarchical architecture to better model this dependency via contextual modulation.
  arXiv  Detail & Related papers  (2023-02-22T00:04:12Z)

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.