# DemoBot: Efficient Learning of Bimanual Manipulation with Dexterous Hands From Third-Person Human Videos

arXiv: https://arxiv.org/abs/2601.01651 · HTML: https://mlsanity.com/paper/2601.01651/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2026.

## Built on by (most cited first, 3 of 3)

- [EgoKit: Towards Unified Low-Cost Egocentric Data Collection with Heterogeneous Devices](https://mlsanity.com/paper/2605.16797/) (2026)
- [C2Dex: Contact-Consistent Reconstruction and Retargeting for Dexterous Manipulation from Monocular Video](https://mlsanity.com/paper/2608.07045/) (2026)
- [DexMimic: A Generalist Dexterous Hand-Object Tracking Policy via Value-Guided Reset Allocation](https://mlsanity.com/paper/2610.19089/) (2026)

## Abstract (arXiv)

This work presents DemoBot, a learning framework that enables a dual-arm, multi-finger robotic system to acquire complex manipulation skills from a single unannotated RGB-D video demonstration. The method extracts structured motion trajectories of both hands and objects from raw video data. These trajectories serve as motion priors for a novel reinforcement learning (RL) pipeline that learns to refine them through contact-rich interactions, thereby eliminating the need to learn from scratch. To address the challenge of learning long-horizon manipulation skills, we introduce: (1) Temporal-segment based RL to enforce temporal alignment of the current state with demonstrations; (2) Success-Gated Reset strategy to balance the refinement of readily acquired skills and the exploration of subsequent task stages; and (3) Event-Driven Reward curriculum with adaptive thresholding to guide the RL learning of high-precision manipulation. The novel video processing and RL framework successfully achieved long-horizon synchronous and asynchronous bimanual assembly tasks, offering a scalable approach for direct skill acquisition from human videos.