FORCA: A Smart Coding Agent for Robotic Manipulation

Paper: FORCA: A Force-Aware Coding Agent for Contact-Rich Robot Manipulation

Authors: Xiao Tan, Yixuan Wang, Zhexi Wang, Wenzhen Yuan

Executive Summary

FORCA is a novel coding agent designed to enhance the capabilities of robots in tasks that require precise manipulation in contact-rich environments. Traditional robotic programming often relies heavily on pre-defined instructions or extensive training, which can limit adaptability and effectiveness. FORCA addresses these limitations by enabling robots to translate visual observations and force/torque (F/T) measurements into functional Python programs, allowing them to perform tasks that involve direct physical interaction.

This research demonstrates that FORCA can effectively generate and refine control logic for robots, enabling them to respond dynamically to physical interactions. The agent processes inputs from visual data and F/T sensors to create motion plans that accommodate various manipulation scenarios. Notably, it does not require prior demonstrations, task-specific training, or simulation, which simplifies the deployment process and expands its usability across different tasks.

The results are based on evaluations conducted in five real-robot tasks, showcasing FORCA's capabilities in three key areas of contact-rich manipulation:

1. Improving Manipulation Accuracy: Robots utilizing FORCA showed enhanced precision in tasks requiring careful handling of objects. This could lead to better outcomes in applications where accuracy is critical, such as in assembly lines or delicate operations.

2. Adapting to Changes in Object or Surface Properties: As objects or surfaces change — for instance, when dealing with varying textures or weights — FORCA's ability to adapt could be beneficial. This flexibility may support industries where products frequently vary, allowing for more robust robotic systems.

3. Regulating Contact Under Surface Variation and Object Motion: The agent also manages variations in surface conditions and object movements effectively. This capability could be particularly relevant in logistics or warehousing, where robots must navigate unpredictable environments and handle diverse items.

Comparative analysis with a vision-only version of the same agent illustrates FORCA's advantages. The coding agent outperformed its counterpart in terms of grasp accuracy and overall task success. It also reduced instances of contact loss in specific activities like wiping and peeling, which are common in cleaning or assembly tasks.

The implications of this research are significant for industries relying on robotics for manipulation tasks. By increasing the adaptability and effectiveness of robots in dynamic environments, FORCA could enhance operational efficiency and reduce the need for manual intervention. As businesses continue to explore automation, advancements like FORCA could play a key role in optimizing processes and improving productivity.

Academic Abstract

Coding agents can translate language instructions into robot programs. For contact-rich manipulation, a key challenge is to generate and refine control logic that responds to physical interaction. We present FORCA, a coding agent that takes visual observations and wrist force/torque (F/T) measurements as input to write Python programs for contact probing and force-responsive motion. Between executions, the agent inspects force records alongside visual outcomes to diagnose failures and revise both motion and force-feedback control logic. FORCA requires no demonstrations, task-specific training, or simulation. Across five real-robot tasks, we demonstrate its effectiveness in three forms of contact-rich manipulation: improving manipulation accuracy, adapting to changes in object or surface properties, and regulating contact under surface variation and object motion. Compared with a vision-only variant of the same agent, FORCA improves grasp accuracy and task success and reduces contact loss in wiping and peeling.