Tian Gao

Zhixing Hou, Yuzhang Shang, Tian Gao et al.

Place recognition, an algorithm to recognize the re-visited places, plays the role of back-end optimization trigger in a full SLAM system. Many works equipped with deep learning tools, such as MLP, CNN, and transformer, have achieved great improvements in this research field. Point cloud transformer is one of the excellent frameworks for place recognition applied in robotics, but with large memory consumption and expensive computation, it is adverse to widely deploy the various point cloud transformer networks in mobile or embedded devices. To solve this issue, we propose a binary point cloud transformer for place recognition. As a result, a 32-bit full-precision model can be reduced to a 1-bit model with less memory occupation and faster binarized bitwise operations. To our best knowledge, this is the first binary point cloud transformer that can be deployed on mobile devices for online applications such as place recognition. Experiments on several standard benchmarks demonstrate that the proposed method can get comparable results with the corresponding full-precision transformer model and even outperform some full-precision deep learning methods. For example, the proposed method achieves 93.28% at the top @1% and 85.74% at the top @1% on the Oxford RobotCar dataset in terms of the metric of the average recall rate. Meanwhile, the size and floating point operations of the model with the same transformer structure reduce 56.1% and 34.1% respectively from original precision to binary precision.

Tian Gao, Soroush Nasiriany, Huihan Liu et al.

Imitation learning has shown great potential for enabling robots to acquire complex manipulation behaviors. However, these algorithms suffer from high sample complexity in long-horizon tasks, where compounding errors accumulate over the task horizons. We present PRIME (PRimitive-based IMitation with data Efficiency), a behavior primitive-based framework designed for improving the data efficiency of imitation learning. PRIME scaffolds robot tasks by decomposing task demonstrations into primitive sequences, followed by learning a high-level control policy to sequence primitives through imitation learning. Our experiments demonstrate that PRIME achieves a significant performance improvement in multi-stage manipulation tasks, with 10-34% higher success rates in simulation over state-of-the-art baselines and 20-48% on physical hardware.