Real-Time Compliance and Position Control of a Hyper-redundant Soft Robotic Arm
2026-06-29 • Robotics
Robotics
AI summaryⓘ
The authors built a robot arm with seven segments and 12 joints controlled by pairs of air-powered muscles, allowing it to adjust both its position and stiffness at the same time. This design helps the arm be soft enough to handle bumps and misalignments but still precise enough to control accurately. They created a special control system that uses math models to tell the arm where to go and how stiff to be, and tested it both in real life and simulation. Their robot successfully performed tricky tasks like writing on a moving board and inserting keys, showing the benefit of combining smart controls with a specially designed structure. This work helps make soft robots better at handling complex, real-world tasks.
soft roboticscompliance controlinverse kinematicspneumatic musclesarticulated robot armdisturbance rejectiontask-space controlrobot stiffnessphysical compliancerobot actuation
Authors
Runze Zuo, Tianhua Zou, Naike Wu, Mingyuan Li, Daniel Bruder
Abstract
Robots working in unstructured or partially unobservable environments must combine accurate motion with physical compliance that can passively correct contact misalignment. Soft robots provide this compliance but have struggled to precisely control their tip compliance and position. This paper presents a robot architecture designed around that control problem: a 7-link arm whose six articulated joints provide twelve independently driven revolute axes, each actuated by an antagonistic pair of pneumatic muscles, so that every axis can simultaneously change its angle and linearly adjust its stiffness. The rigid articulated backbone makes the tip compliance and position of the arm predictable enough to be commanded quantitatively in real time. The robot employs a unified iterative inverse-kinematics and inverse-compliance controller to achieve simultaneous, quantitative control of both compliance and position. The task-space compliance and kinematics models and the control law are derived and verified on both the physical arm and a matched simulation. Simulation is then used to study how the same framework extends to other arm morphologies. Finally, the arm demonstrates tasks that have been difficult for both rigid and soft arms: rejecting disturbances while writing on a moving whiteboard, and passively correcting hidden misalignment during a key-insertion and drawer-opening task. That these tasks succeed under so straightforward a controller is evidence for the advantage of this algorithm-informed structural design.