Author name cluster

Jianwei Tan

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

IROS Conference 2025 Conference Paper

Constrained Behavior Cloning for Robotic Learning

Jun Xie
Jianwei Tan
Wensheng Liang
Zhicheng Wang
Xiaoguang Ma

Behavior cloning (BC) is a widely used method for learning from expert demonstrations due to its simplicity and efficiency. However, the reliability and stability of BC decline when facing data distribution shifts, especially in single-arm robots with limited fields of view. This study introduces a Geometrically and Historically Constrained Behavior Cloning (GHCBC) method, where an HCBC module utilizes visual and action histories to capture temporal dependencies, maximizing the use of available information, and a GCBC module incorporates high-level perceptual data, such as the relative poses of joints and end-effectors, to enhance BC performance. Experiments demonstrate that the GHCBC outperforms current SOTA BC methods, achieving a 31. 5% improvement in simulation success rates and 48. 4% in real-robot scenarios respectively. To the best of our knowledge, this is the first time that the GHCBC has been introduced in robotic BC where great potential is demonstrated for long-term tasks in real world environments.

Details

ECAI Conference 2025 Conference Paper

Subconscious Robotic Imitation Learning

Jun Xie
Zhicheng Wang
Jianwei Tan
Huanxu Lin
Yang Jiang
Xiaoguang Ma

While imitation learning (IL) emerges as a promising paradigm for embodied intelligent robots, its practical application is constrained by slow execution speeds, caused by the computational intensity of precise multi-model trajectory prediction, especially in complex dynamic environments. In contrast, humans can efficiently perform long-duration tasks through subconscious-driven habitual actions, such as riding bikes, without focusing on execution details. Motivated by this insight, we proposed Subconscious Robotic Imitation Learning (SRIL) framework, which mimicked the subconscious information extraction and decision-making abilities through intent-aware sampling and cognitive hierarchical reasoning, thereby significantly improving IL task execution efficiency. Experimental results demonstrated that execution speeds of the SRIL were 100% to 200% faster over SOTA policies for comprehensive bimanual tasks, with consistently higher success rates.

Details