Contact-Based Motion Generation

Revolutionizing Robotic Motion Planning with Environmental Constraints

Imagine a future where robots navigate and manipulate their environment with unprecedented efficiency and accuracy. My thesis explains and applies a key concept rooted in groundbreaking research to motion planning in robotics, a domain traditionally fraught with challenges from high-dimensional search spaces and inherent inaccuracies in robot sensing and motion capabilities.

My thesis pivots on a transformative idea: leveraging Environmental Constraints (EC) to significantly reduce uncertainties in robot motion caused by perception and motion inaccuracies. Drawing inspiration from human behavior and previous robotic advancements, I've developed innovative strategies that embrace contact with the environment rather than avoiding it to achieve more reliable and effective motion planning.

Here's what sets the contact-based approach apart:

My thesis advances the field of EC-based manipulation and motion planning and proposes a new paradigm in robotics. By blurring the lines between control, perception, and planning, we can shift the burden of manipulation from the robot to the environment. This revolutionary approach promises to redefine how robots interact with their surroundings, making them more adaptable, efficient, and capable in various applications, from industrial automation to service robotics.