Bi-LAT: Bilateral Control-Based Imitation Learning via Natural Language and Action Chunking with Transformers
This work addresses the challenge of intuitive and adaptive human-robot interaction for robotics applications, though it appears incremental as it builds on existing imitation learning and multimodal methods.
The paper tackles the problem of precise force modulation in robotic manipulation by integrating bilateral control with natural language processing, resulting in a framework that effectively reproduces instructed force levels in tasks like cup-stacking and sponge-twisting.
We present Bi-LAT, a novel imitation learning framework that unifies bilateral control with natural language processing to achieve precise force modulation in robotic manipulation. Bi-LAT leverages joint position, velocity, and torque data from leader-follower teleoperation while also integrating visual and linguistic cues to dynamically adjust applied force. By encoding human instructions such as "softly grasp the cup" or "strongly twist the sponge" through a multimodal Transformer-based model, Bi-LAT learns to distinguish nuanced force requirements in real-world tasks. We demonstrate Bi-LAT's performance in (1) unimanual cup-stacking scenario where the robot accurately modulates grasp force based on language commands, and (2) bimanual sponge-twisting task that requires coordinated force control. Experimental results show that Bi-LAT effectively reproduces the instructed force levels, particularly when incorporating SigLIP among tested language encoders. Our findings demonstrate the potential of integrating natural language cues into imitation learning, paving the way for more intuitive and adaptive human-robot interaction. For additional material, please visit: https://mertcookimg.github.io/bi-lat/