# SPIDER: Scalable Physics-Informed Dexterous Retargeting

arXiv: https://arxiv.org/abs/2511.09484 · HTML: https://mlsanity.com/paper/2511.09484/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 62 of its 72 references that are in the map.
- First posted on arXiv 12 Nov 2025.

## Builds on (most cited first, 30 of 62)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset](https://mlsanity.com/paper/2403.12945/) (2024)
- [Structured 3D Latents for Scalable and Versatile 3D Generation](https://mlsanity.com/paper/2412.01506/) (2024)
- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017)
- [FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects](https://mlsanity.com/paper/2312.08344/) (2023)
- [Humans in 4D: Reconstructing and Tracking Humans with Transformers](https://mlsanity.com/paper/2305.20091/) (2023)
- [MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations](https://mlsanity.com/paper/2310.17596/) (2023)
- [DexYCB: A Benchmark for Capturing Hand Grasping of Objects](https://mlsanity.com/paper/2104.04631/) (2021)
- [ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation](https://mlsanity.com/paper/2204.13662/) (2022)
- [Reconstructing Hands in 3D with Transformers](https://mlsanity.com/paper/2312.05251/) (2023)
- [Robust Motion In-betweening](https://mlsanity.com/paper/2102.04942/) (2021)
- [MimicPlay: Long-Horizon Imitation Learning by Watching Human Play](https://mlsanity.com/paper/2302.12422/) (2023)
- [Object Motion Guided Human Motion Synthesis](https://mlsanity.com/paper/2309.16237/) (2023)
- [EgoDex: Learning Dexterous Manipulation from Large-Scale Egocentric Video](https://mlsanity.com/paper/2505.11709/) (2025)
- [OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning](https://mlsanity.com/paper/2406.08858/) (2024)
- [BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion](https://mlsanity.com/paper/2508.08241/) (2025)
- [AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System](https://mlsanity.com/paper/2307.04577/) (2023)
- [OakInk: A Large-scale Knowledge Repository for Understanding Hand-Object Interaction](https://mlsanity.com/paper/2203.15709/) (2022)
- [ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills](https://mlsanity.com/paper/2502.01143/) (2025)
- [HOT3D: Hand and Object Tracking in 3D from Egocentric Multi-View Videos](https://mlsanity.com/paper/2411.19167/) (2024)
- [Track2Act: Predicting Point Tracks from Internet Videos enables Generalizable Robot Manipulation](https://mlsanity.com/paper/2405.01527/) (2024)
- [DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning](https://mlsanity.com/paper/2410.24185/) (2024)
- [VideoDex: Learning Dexterity from Internet Videos](https://mlsanity.com/paper/2212.04498/) (2022)
- [TWIST: Teleoperated Whole-Body Imitation System](https://mlsanity.com/paper/2505.02833/) (2025)
- [General In-Hand Object Rotation with Vision and Touch](https://mlsanity.com/paper/2309.09979/) (2023)

## Built on by (most cited first, 30 of 32)

- [SimToolReal: An Object-Centric Policy for Zero-Shot Dexterous Tool Manipulation](https://mlsanity.com/paper/2602.16863/) (2026)
- [Dexterous Manipulation Policies from RGB Human Videos via 3D Hand-Object Trajectory Reconstruction](https://mlsanity.com/paper/2602.09013/) (2026)
- [OSMO: Open-Source Tactile Glove for Human-to-Robot Skill Transfer](https://mlsanity.com/paper/2512.08920/) (2025)
- [ULTRA: Unified Multimodal Control for Autonomous Humanoid Whole-Body Loco-Manipulation](https://mlsanity.com/paper/2603.03279/) (2026)
- [Open-AoE: An Open Egocentric Manipulation Dataset and Toolchain for Embodied Learning](https://mlsanity.com/paper/2607.14183/) (2026)
- [MeshMimic: Geometry-Aware Humanoid Motion Learning through 3D Scene Reconstruction](https://mlsanity.com/paper/2602.15733/) (2026)
- [DexHiL: A Human-in-the-Loop Framework for Vision-Language-Action Model Post-Training in Dexterous Manipulation](https://mlsanity.com/paper/2603.09121/) (2026)
- [TopoRetarget: Interaction-Preserving Retargeting for Dexterous Manipulation](https://mlsanity.com/paper/2606.16272/) (2026)
- [Learning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration](https://mlsanity.com/paper/2607.00033/) (2026)
- [LUCID: Learning Embodiment-Agnostic Intent Models from Unstructured Human Videos for Scalable Dexterous Robot Skill Acquisition](https://mlsanity.com/paper/2606.11628/) (2026)
- [FEEL (Force-Enhanced Egocentric Learning): A Dataset for Physical Action Understanding](https://mlsanity.com/paper/2603.15847/) (2026)
- [A Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation](https://mlsanity.com/paper/2607.11874/) (2026)
- [AnyHand: A Large-Scale Synthetic Dataset for RGB(-D) Hand Pose Estimation](https://mlsanity.com/paper/2603.25726/) (2026)
- [Direct Dynamic Retargeting for Humanoid Imitation Learning from Videos](https://mlsanity.com/paper/2605.23762/) (2026)
- [HandFlow: Fully Generative 4D Hand Recovery with Flow Matching](https://mlsanity.com/paper/2607.11221/) (2026)
- [PDF-HR: Pose Distance Fields for Humanoid Robots](https://mlsanity.com/paper/2602.04851/) (2026)
- [Ruka-v2: Tendon Driven Open-Source Dexterous Hand with Wrist and Abduction for Robot Learning](https://mlsanity.com/paper/2603.26660/) (2026)
- [KITE: Decoupling Kinematics and Interaction for Zero-Shot Cross-Embodiment Manipulation](https://mlsanity.com/paper/2606.22113/) (2026)
- [MachEmbodied-U0: Unified Understanding and Generation Model for Embodied Intelligence](https://mlsanity.com/paper/2609.25627/) (2026)
- [DexAgent: An Agentic Human2Sim2Robot Framework for Dexterous Manipulation with Self-Evolving Tool Library](https://mlsanity.com/paper/2609.35318/) (2026)
- [SynAgent: Generalizable Cooperative Humanoid Manipulation via Solo-to-Cooperative Agent Synergy](https://mlsanity.com/paper/2604.18557/) (2026)
- [WristMimic: Full-Body Humanoid Control with Wrist-Guided Manipulation](https://mlsanity.com/paper/2607.06438/) (2026)
- [Track, Articulate, Act: Generating Articulation from Casual Human Videos](https://mlsanity.com/paper/2609.19119/) (2026)
- [SocialHumanoid: Towards Expressive Humanoid Behavior via One-Step Co-Speech Motion Generation](https://mlsanity.com/paper/2609.33311/) (2026)
- [DexTaG: Tactile-as-Guidance in Reinforcement Learning for Dexterous Manipulation](https://mlsanity.com/paper/2609.33882/) (2026)
- [EgoHumanoid-V2: Human-to-Humanoid Transfer of Coordinated Whole-Body Skills for Loco-Manipulation](https://mlsanity.com/paper/2609.37181/) (2026)
- [GOTT: Object-centric Dexterous Manipulation with a Reusable Cross-Embodiment Primitive](https://mlsanity.com/paper/2610.03861/) (2026)
- [InterMimicGen: Scaling Humanoid Loco-Manipulation through Self-Evolving Motion Imitation](https://mlsanity.com/paper/2610.06850/) (2026)
- [EigenDEXplore: Structured Exploration for Dexterous Manipulation with Human Priors](https://mlsanity.com/paper/2610.07681/) (2026)
- [Dex-One2Many: Learning Dexterous Manipulation from a Single Human Demonstration](https://mlsanity.com/paper/2610.12470/) (2026)

## Abstract (arXiv)

Learning agile robotic policies requires large-scale demonstrations, but translating abundant human motion data to robots is bottlenecked by the embodiment gap and missing dynamic information. To bridge this gap, we propose Scalable Physics-Informed DExterous Retargeting (SPIDER), a physics-based retargeting framework to transform and augment kinematic-only human demonstrations into dynamically feasible robot trajectories at scale. Our key insight is that human demonstrations should provide global task structure and objective, while a sampling-based solver can be used to find the feasible solution given the physics constraints. As a general framework, SPIDER is an efficient physics-based retargeting method that can be applied to both humanoid whole-body loco-manipulation and dexterous manipulation across 9 humanoid/dexterous hand embodiments, 6 datasets and 3 simulators. By bypassing the need for policy optimization, it achieves state-of-the-art performance while being 10x faster than reinforcement learning (RL) baselines. Furthermore, SPIDER enables physics-based data augmentation, such as imposing external payloads, perturbations, and new contact patterns, to generate diverse data. We demonstrate that the retargeted motion can be executed on the robot directly open-loop or serve as feasible reference motion for efficient RL policy learning. Our pipeline enables large-scale generation of robot trajectories from human motion, and we release a dataset of 6,520 simulated demonstrations across four hands and 191 dataset-specific object identities to support future research.