Safe Autonomous Environmental Contact for Soft Robots Using Control Barrier Functions
Akua Dickson, Juan Pacheco Garcia, Meredith Anderson, Ran Jing, Sarah Alizadeh-Shabdiz, Audrey Wang, Charles DeLorey, Zachary Patterson, Andrew Sabelhaus
AI summary
Problem
Soft robots are often assumed to be inherently safe due to compliance, but their closed-loop control systems do not formally guarantee that contact forces stay within safe limits. This leaves a gap in generalizable methods for enforcing formal safety constraints during soft robot operation.
Approach
The method maps a known maximum safe force to a corresponding safe set of end-effector poses by modeling environment deformation, then uses a quadratic program with Control Barrier Functions to supervise a nominal controller and keep the robot tip within that safe set.
Key results
- A force-to-state mapping method with a one-to-one set inclusion criterion
- A CBF-based supervisory controller with a proof-by-construction of force invariance
- Simulation and hardware validation demonstrating a positive safety margin on contact forces
- Demonstration that the framework prevents force violations where open-loop control fails
Why it matters
Enables formally verifiable safety for soft robots, critical for reliable deployment in sensitive medical and human-robot interaction settings.
Abstract
Robots built from soft materials will inherently ap- ply lower environmental forces than their rigid counterparts, and therefore may be more suitable in sensitive settings with unintended contact. However, these robots’ applied forces result from both their design and their control system in closed-loop, and therefore, ensuring bounds on these forces requires controller synthesis for safety as well. This letter introduces the first feedback controller for a soft manipulator that formally meets a safety specification with respect to environmental contact. In our proof-of-concept setting, the robot’s environment has known geometry and is deformable with a known elastic modulus. Our approach maps a bound on appliedforcestoasafesetofpositionsoftherobot’stipviapredicted deformations of the environment. Then, a quadratic program with Control Barrier Functions in its constraints is used to supervise a nominal feedback signal, verifiably maintaining the robot’s tip within this safe set. Hardware experiments on a multi-segment soft pneumatic robot demonstrate that the proposed framework successfully maintains a positive safety margin. This framework represents a fundamental shift in perspective on control and safety for soft robots, implementing a formally verifiable logic specifica- tion on their pose and contact forces.